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Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 27, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)

This article presents concise English summaries of four recent AI‑driven quantitative finance papers, covering an agentic AI screening platform for portfolio investment, a wavelet‑based physics‑informed neural network for option pricing, the FinRL‑X modular trading infrastructure, and the S³G stock state‑space graph for enhanced trend prediction, each with authors, links, and key experimental results.

AILLMModular Trading Infrastructure
0 likes · 12 min read
Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)
HyperAI Super Neural
HyperAI Super Neural
Mar 9, 2026 · Artificial Intelligence

Physics‑Informed GNN Breakthrough for Accurate, Real‑Time Multi‑Body Dynamics

Researchers from EPFL introduce DYNAMI‑CAL GraphNet, a graph neural network that embeds linear and angular momentum conservation, delivering highly accurate, interpretable and real‑time predictions for complex multi‑body systems across robotics, aerospace and materials science, and outperforming existing baselines on four diverse benchmark datasets.

DYNAMI‑CAL GraphNetEmbodied AIgraph neural networks
0 likes · 16 min read
Physics‑Informed GNN Breakthrough for Accurate, Real‑Time Multi‑Body Dynamics
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 6, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Summary (Jan 31–Feb 6 2026)

This article summarizes recent quantitative‑finance research, presenting abstracts and key findings of three papers—BPASGM for machine‑learning‑driven portfolio construction, PIKAN‑enhanced deep reinforcement learning with physics‑informed regularization, and GAPNet’s dynamic graph‑based stock relation learning—along with links to numerous related studies.

deep reinforcement learninggraph neural networksmachine learning
0 likes · 11 min read
Weekly Quantitative Finance Paper Summary (Jan 31–Feb 6 2026)
AI Frontier Lectures
AI Frontier Lectures
Jan 12, 2026 · Artificial Intelligence

How GraphKeeper Tackles Catastrophic Forgetting in Domain‑Incremental Graph Learning

This article analyzes the GraphKeeper framework, which combines multi‑domain graph decoupling, unbiased ridge‑regression knowledge preservation, and a domain‑aware distribution discriminator to overcome catastrophic forgetting in domain‑incremental graph neural network training, and validates its superiority through extensive experiments and ablations.

Catastrophic ForgettingDomain Incremental LearningGraphKeeper
0 likes · 15 min read
How GraphKeeper Tackles Catastrophic Forgetting in Domain‑Incremental Graph Learning
Data Party THU
Data Party THU
Dec 23, 2025 · Artificial Intelligence

How STFAN Revolutionizes Traffic Flow Forecasting with Spatio‑Temporal‑Frequency Attention

This article introduces the Spatio‑Temporal‑Frequency Attention Network (STFAN), a deep‑learning model that fuses graph neural networks, attention mechanisms, and frequency‑domain analysis to capture hidden spatial, temporal, and spectral dependencies in traffic data, achieving superior short‑ and long‑term forecasting performance on real‑world datasets.

STFANTraffic Predictionfrequency analysis
0 likes · 20 min read
How STFAN Revolutionizes Traffic Flow Forecasting with Spatio‑Temporal‑Frequency Attention
Kuaishou Tech
Kuaishou Tech
Dec 18, 2025 · Artificial Intelligence

How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem

The paper introduces SSR, a novel multimodal recommendation framework that leverages graph Fourier transforms, energy‑balanced frequency bands, structured regularization, and low‑rank tensor decomposition to replace black‑box fusion with explainable, adaptive reasoning, achieving state‑of‑the‑art results on Amazon datasets and strong cold‑start performance.

cold startcontrastive learningfrequency domain
0 likes · 15 min read
How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem
HyperAI Super Neural
HyperAI Super Neural
Dec 12, 2025 · Artificial Intelligence

Weekly AI Paper Digest: Attention, Nvidia VLA, TTS, and Graph Neural Networks

This roundup presents five recent AI papers covering hierarchical sparse attention for ultra‑long context, Nvidia's Alpamayo‑R1 VLA model for autonomous driving, the non‑autoregressive F5‑TTS system, LatentMAS for latent‑space multi‑agent collaboration, and Deeper‑GXX that deepens arbitrary graph neural networks, highlighting each method's key innovations and reported performance gains.

Attention Mechanismautonomous drivinggraph neural networks
0 likes · 6 min read
Weekly AI Paper Digest: Attention, Nvidia VLA, TTS, and Graph Neural Networks
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 11, 2025 · Artificial Intelligence

A New Method: Dynamic Higher-Order Relations and Event-Driven Modeling for Stock Price Prediction

The article reviews a novel stock price prediction model that integrates a Hawkes‑process layer to capture sudden co‑movements and a dynamic hypergraph to represent high‑order relationships, detailing its formulation, training objective, extensive experiments on S&P 500 data, and superior performance over transformer, graph, and hypergraph baselines.

Financial AIHawkes processTime Series
0 likes · 12 min read
A New Method: Dynamic Higher-Order Relations and Event-Driven Modeling for Stock Price Prediction
Alimama Tech
Alimama Tech
Nov 11, 2025 · Artificial Intelligence

Industrial-Scale Graph Learning: Boosting Ad ROI and Winning Beijing’s Science Award

The award‑winning industrial graph learning system developed by Peking University and Alibaba Mama combines novel dynamic graph embedding and GNN techniques, scales to millions of merchants, and has driven over 12% ad ROI improvement while publishing dozens of top‑conference papers.

AI researchIndustrial AIadvertising optimization
0 likes · 6 min read
Industrial-Scale Graph Learning: Boosting Ad ROI and Winning Beijing’s Science Award
Architect's Guide
Architect's Guide
Nov 7, 2025 · Artificial Intelligence

Why Multi-Agent Communication Protocols Are Crucial for Next-Gen AI

The article examines the need for Multi‑Agent Communication Protocols (MCP), outlines the limitations of single‑agent and centralized systems, compares MCP with other interaction methods, reviews current research and industrial applications, and highlights future directions such as hardware integration, bio‑inspired mechanisms, and blockchain convergence.

BlockchainReinforcement Learningcommunication protocols
0 likes · 9 min read
Why Multi-Agent Communication Protocols Are Crucial for Next-Gen AI
Data Party THU
Data Party THU
Oct 18, 2025 · Artificial Intelligence

Can Classic Graph Autoencoders Rival SOTA? Surprising Optimizations Reveal Their Power

Researchers from Peking University demonstrate that, by applying modern optimization techniques to the decades‑old Graph Autoencoder (GAE), the model can achieve state‑of‑the‑art link‑prediction performance on benchmarks like ogbl‑ppa, while delivering orders‑of‑magnitude speed improvements, challenging the trend toward ever‑more complex GNNs.

Model Optimizationefficiencygraph autoencoder
0 likes · 10 min read
Can Classic Graph Autoencoders Rival SOTA? Surprising Optimizations Reveal Their Power
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 13, 2025 · Artificial Intelligence

Paper Summary: Recent AI Advances in Financial Time-Series (Sep 6‑12, 2025)

This article summarizes four recent AI research papers that explore zero‑shot PDE extrapolation with text‑trained LLMs, causal hidden‑state interventions for rare financial events, tabular reformulation of graph node classification, and a multimodal model for financial time‑series forecasting, detailing their methods, experiments, and key findings.

LLMTime Seriescausal intervention
0 likes · 10 min read
Paper Summary: Recent AI Advances in Financial Time-Series (Sep 6‑12, 2025)
Data Party THU
Data Party THU
Aug 20, 2025 · Artificial Intelligence

How Dual‑Granularity Prompting Boosts Graph‑Enhanced LLMs for Fraud Detection

The article analyzes the Dual Granularity Prompting (DGP) framework, which mitigates information overload in graph‑enhanced large language models for fraud detection by applying fine‑grained processing to target nodes and coarse‑grained summarization to neighbors, achieving superior accuracy and token efficiency across multiple public and industrial datasets.

Large Language Modelsdual granularity promptingfraud detection
0 likes · 6 min read
How Dual‑Granularity Prompting Boosts Graph‑Enhanced LLMs for Fraud Detection
Data Party THU
Data Party THU
Jul 30, 2025 · Artificial Intelligence

Can Graph Neural Networks Accurately Predict Antibody‑Antigen Binding Affinity?

A recent Oxford study introduces Graphinity, an equivariant graph neural network that directly uses antibody‑antigen structures to predict ΔΔG, achieving up to r = 0.89 on large synthetic datasets, but reveals that data volume and diversity, rather than model architecture, remain the primary bottleneck for reliable affinity prediction.

Protein Engineeringantibody affinitygraph neural networks
0 likes · 7 min read
Can Graph Neural Networks Accurately Predict Antibody‑Antigen Binding Affinity?
JD Cloud Developers
JD Cloud Developers
Jul 18, 2025 · Artificial Intelligence

New Precise Matching Techniques from JD’s SIGIR 2025 Papers

JD's retail technology team presents five SIGIR 2025 papers that introduce advanced graph neural, causal optimal transport, domain‑oriented relevance, multi‑objective bid‑word generation, and hierarchical user behavior models to dramatically improve precise matching in e‑commerce search, recommendation, and advertising.

AdvertisingCTR predictioncausal optimal transport
0 likes · 11 min read
New Precise Matching Techniques from JD’s SIGIR 2025 Papers
JD Retail Technology
JD Retail Technology
Jul 18, 2025 · Artificial Intelligence

How Cutting-Edge AI Models Are Revolutionizing E‑Commerce CTR Prediction

This article showcases five JD Retail Technology research papers accepted at SIGIR 2025, covering graph‑based cohort modeling, causal optimal transport post‑event modeling, an autonomous domain‑oriented relevance engine, a multi‑objective bidword generation model, and hierarchical long‑term user behavior modeling, all advancing e‑commerce CTR prediction and advertising.

CTR predictioncausal optimal transporte-commerce relevance
0 likes · 10 min read
How Cutting-Edge AI Models Are Revolutionizing E‑Commerce CTR Prediction
Architect
Architect
Jul 6, 2025 · Artificial Intelligence

How Graphs Empower AI Agents: Taxonomy, Advances, and Future Opportunities

An extensive review introduces a taxonomy for integrating graph techniques with AI agents, detailing how graphs enhance core functions such as planning, execution, memory, and multi‑agent coordination, and discusses representative applications, challenges, and future research directions.

AI agentsKnowledge GraphsPlanning
0 likes · 9 min read
How Graphs Empower AI Agents: Taxonomy, Advances, and Future Opportunities
AntTech
AntTech
May 15, 2025 · Artificial Intelligence

Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference

This announcement introduces a live session that will dissect two best‑paper award research works from WSDM 2025—one revealing how recommendation models amplify popularity bias through spectral analysis and proposing a lightweight regularizer, and the other presenting a graph disentangle causal model that integrates GNNs with structural causal models to improve causal inference on networked observational data.

Recommendation SystemsWSDM 2025causal inference
0 likes · 4 min read
Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference
Alimama Tech
Alimama Tech
Apr 23, 2025 · Artificial Intelligence

Distribution-aware Graph Prompt Tuning (DAGPrompT) for Heterophilic Graphs

Distribution‑aware Graph Prompt Tuning (DAGPrompT) tackles the pre‑training/downstream mismatch on heterophilic graphs by jointly applying low‑rank GLoRA adaptation and hop‑specific prompts that recast tasks as link‑prediction, yielding up to 4.79% accuracy gains and an average 2.43% improvement in few‑shot node classification.

Few‑Shot LearningPrompt Tuningdistribution-aware
0 likes · 9 min read
Distribution-aware Graph Prompt Tuning (DAGPrompT) for Heterophilic Graphs
AntTech
AntTech
Mar 4, 2025 · Artificial Intelligence

GraphCLIP and 2D‑TPE: Enhancing Transferability of Graph Models and Table Understanding for Large Language Models

This article introduces GraphCLIP, a self‑supervised graph‑summary pre‑training framework that boosts zero‑ and few‑shot transferability of graph foundation models for text‑attributed graphs, and 2D‑TPE, a two‑dimensional positional encoding method that preserves table structure to markedly improve large language model performance on table‑understanding tasks, while also announcing a live paper session at WWW 2025 featuring the authors.

Large Language ModelsPositional EncodingSelf‑Supervised Learning
0 likes · 6 min read
GraphCLIP and 2D‑TPE: Enhancing Transferability of Graph Models and Table Understanding for Large Language Models
DataFunTalk
DataFunTalk
Feb 6, 2025 · Artificial Intelligence

Why Graph Neural Networks Are Suitable for Recommendation Systems

Graph Neural Networks excel in recommendation systems because they can model complex user‑item relationships, capture high‑order interactions, adapt dynamically to real‑time behavior, propagate multi‑step information, enrich contextual embeddings, alleviate data sparsity, and improve long‑tail item coverage, with practical e‑commerce case studies available for download.

Artificial IntelligenceGNNRecommendation Systems
0 likes · 5 min read
Why Graph Neural Networks Are Suitable for Recommendation Systems
DataFunSummit
DataFunSummit
Jan 13, 2025 · Artificial Intelligence

Deep Learning Approaches for Solving Graph Optimization Problems

This article reviews the use of deep learning, including supervised, reinforcement, and self‑supervised paradigms, to address graph optimization problems such as facility location and balanced graph partitioning, discusses existing research challenges, presents a three‑stage self‑supervised model with graph contrastive pre‑training, and evaluates its performance on synthetic and real‑world datasets.

Deep Learningcombinatorial optimizationexperimental evaluation
0 likes · 14 min read
Deep Learning Approaches for Solving Graph Optimization Problems
ZhongAn Tech Team
ZhongAn Tech Team
Dec 28, 2024 · Artificial Intelligence

Weekly AI Digest Issue 8: OpenAI Robotics, ModernBERT Upgrade, Spatial Cognition, LLM Agent Evolution, and GNN‑LLM Fusion

This issue surveys recent AI developments, covering OpenAI's renewed robot program, the ModernBERT encoder upgrade, spatial reasoning advances in multimodal models, automated environment generation for LLM agents, and a novel GNN‑LLM approach for label‑free node classification.

Artificial IntelligenceBERTLLM
0 likes · 10 min read
Weekly AI Digest Issue 8: OpenAI Robotics, ModernBERT Upgrade, Spatial Cognition, LLM Agent Evolution, and GNN‑LLM Fusion
NewBeeNLP
NewBeeNLP
Nov 14, 2024 · Artificial Intelligence

What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview

The 30th SIGKDD conference in Barcelona featured 2,046 research papers with a 20% acceptance rate, and this article compiles the 59 recommendation‑system papers—covering large‑model recommenders, graph‑based methods, sequential models, fairness, privacy, advertising, debiasing, reinforcement learning and more—for researchers to explore the latest academic advances.

FairnessKDD2024Large Language Models
0 likes · 15 min read
What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview
DataFunSummit
DataFunSummit
Oct 31, 2024 · Artificial Intelligence

Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)

This article presents Tencent's research on community recommendation for online games, introducing an adaptive K‑Free community detection algorithm (DAG) to address cold‑start and unknown community count, a constrained large‑scale recommendation method (ComRec), their evaluation metrics, experimental results, and deployment insights.

Tencent GamesUnsupervised Learningcommunity-detection
0 likes · 20 min read
Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)
JD Tech Talk
JD Tech Talk
Sep 23, 2024 · Artificial Intelligence

JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering

The JD Advertising R&D team applies cutting‑edge AI techniques—including query intent models, multimodal representation pipelines, reinforcement‑learning‑based auction mechanisms, generative recommendation with quantized product tokens, and large‑model infrastructure—to boost traffic valuation, ad relevance, revenue, and creative generation across the platform.

AIAdvertisingMultimodal
0 likes · 19 min read
JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering
AntTech
AntTech
Aug 28, 2024 · Artificial Intelligence

Ant Group’s Selected Papers at KDD2024: Abstracts and Highlights

The article presents a curated collection of Ant Group's research papers accepted at KDD2024, summarizing each paper's title, type, link, source, relevant fields, and abstract, covering topics such as graph mining, large language models, fraud detection, recommendation systems, and multimodal medical AI.

AI researchAnt GroupKDD2024
0 likes · 31 min read
Ant Group’s Selected Papers at KDD2024: Abstracts and Highlights
DataFunSummit
DataFunSummit
Jul 28, 2024 · Artificial Intelligence

Leveraging Large Language Models for Graph Learning: Opportunities, Current Progress, and Future Directions

This article reviews why large language models can be applied to graph learning, outlines their capabilities and graph data characteristics, surveys current research across different graph types and LLM roles, and proposes future research directions for unified cross‑domain graph learning.

AILarge Language ModelsMultimodal
0 likes · 19 min read
Leveraging Large Language Models for Graph Learning: Opportunities, Current Progress, and Future Directions
Meituan Technology Team
Meituan Technology Team
Jul 25, 2024 · Artificial Intelligence

Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers

Meituan’s five long papers accepted at KDD 2024 introduce a dual‑intent model for search‑recommendation, a joint auction mechanism for ads, a robust ATE estimator for heavy‑tailed metrics, a decision‑focused causal learning framework for marketing, and an efficient on‑demand order‑pooling system for real‑time courier assignments.

Controlled ExperimentsKDD 2024Recommendation Systems
0 likes · 12 min read
Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers
DataFunSummit
DataFunSummit
Jul 25, 2024 · Artificial Intelligence

LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control

This article presents the latest advances from the Chinese Academy of Sciences in graph machine learning for user behavior risk control, introducing the LOGIN framework that leverages large language models as consultants to iteratively enhance GNN training, and demonstrates its effectiveness through extensive experiments on homogeneous and heterogeneous graph benchmarks.

Large Language Modelsgraph neural networksmachine learning
0 likes · 14 min read
LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control
DataFunSummit
DataFunSummit
Jun 1, 2024 · Artificial Intelligence

Graph Foundation Models: Concepts, Progress, and Future Directions

This article provides a comprehensive overview of Graph Foundation Models (GFMs), covering their definition, key characteristics, historical development of graph machine learning, recent research trends such as PT‑HGNN, Specformer, and GraphTranslator, and discusses future challenges and research directions.

Large Language Modelsfoundation-modelsgraph neural networks
0 likes · 23 min read
Graph Foundation Models: Concepts, Progress, and Future Directions
DataFunTalk
DataFunTalk
May 9, 2024 · Artificial Intelligence

Graph Model Practices and Applications in Baidu Recommendation System

This article introduces the background of graph data, explains common graph modeling algorithms such as graph embedding and graph neural networks, compares their strengths, and details the evolution and large‑scale deployment of Feed graph models in Baidu's recommendation platform.

BaiduEmbeddingRecommendation Systems
0 likes · 11 min read
Graph Model Practices and Applications in Baidu Recommendation System
DataFunSummit
DataFunSummit
Apr 28, 2024 · Artificial Intelligence

Graph Knowledge Transfer: Methods, Practices, and the Knowledge Bridge Learning Framework

This article presents a comprehensive overview of graph knowledge transfer, covering its definition, the data‑hungry problem, distribution shift challenges, the Knowledge Bridge Learning (KBL) framework, the Bridged‑GNN model, extensive experiments on real‑world scenarios, and a concluding Q&A session.

Knowledge Transferdomain adaptationgraph learning
0 likes · 22 min read
Graph Knowledge Transfer: Methods, Practices, and the Knowledge Bridge Learning Framework
NewBeeNLP
NewBeeNLP
Apr 26, 2024 · Artificial Intelligence

Self-Attention vs Virtual Nodes in Graph Neural Networks: What Really Works?

This article reviews the paper “Distinguished in Uniform: Self-Attention vs. Virtual Nodes,” comparing graph Transformers and MPGNNs with virtual nodes on theoretical consistency and experimental performance, revealing that neither approach universally dominates the other.

MPGNNSelf-Attentiongraph neural networks
0 likes · 9 min read
Self-Attention vs Virtual Nodes in Graph Neural Networks: What Really Works?
NewBeeNLP
NewBeeNLP
Mar 26, 2024 · Artificial Intelligence

How OpenGraph Enables Zero‑Shot Graph Learning Across Datasets

OpenGraph introduces a zero‑shot graph learning framework that unifies graph tokenization, a scalable transformer with efficient sampling, and LLM‑driven data augmentation, achieving superior cross‑dataset generalization on node classification and link prediction tasks, as demonstrated by extensive experiments.

LLM data augmentationgraph neural networksgraph tokenization
0 likes · 20 min read
How OpenGraph Enables Zero‑Shot Graph Learning Across Datasets
DataFunSummit
DataFunSummit
Mar 23, 2024 · Artificial Intelligence

Graph Neural Networks for Real-World Complex Scenarios

This article presents a comprehensive overview of recent graph neural network research, covering adversarial representation learning for network embedding, block‑model guided GCN, enhanced class‑discriminative GNNs, self‑supervised contrastive GNNs, experimental results, and conclusions, highlighting their significance in real‑world applications.

GCNadversarial learninggraph neural networks
0 likes · 13 min read
Graph Neural Networks for Real-World Complex Scenarios
DataFunTalk
DataFunTalk
Mar 10, 2024 · Artificial Intelligence

Aligning Graph Models with Large Language Models for Open-Task Scenarios

This talk presents GraphTranslator, a framework that bridges pretrained graph models and large language models to enable unified handling of both predefined and open-ended graph analysis tasks by translating node representations into language tokens and training an alignment producer for node‑text pairs.

AI researchLarge Language ModelsModel Alignment
0 likes · 3 min read
Aligning Graph Models with Large Language Models for Open-Task Scenarios
DataFunSummit
DataFunSummit
Feb 17, 2024 · Artificial Intelligence

When to Pre‑Train Graph Neural Networks: Data‑Active Pre‑Training and a Graph Generator Framework

This article examines the conditions under which graph neural network pre‑training is beneficial, proposes a data‑centric generator framework to assess transferability, introduces a data‑active pre‑training strategy that selects informative graphs, and presents experimental results showing that using less, well‑chosen data can outperform full‑scale pre‑training.

Pre‑trainingdata selectiongraph generator
0 likes · 16 min read
When to Pre‑Train Graph Neural Networks: Data‑Active Pre‑Training and a Graph Generator Framework
DataFunTalk
DataFunTalk
Jan 11, 2024 · Artificial Intelligence

Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution

This article introduces the use of graph models in Baidu's recommendation system, covering graph fundamentals, common graph algorithms such as graph embedding and graph neural networks, the evolution of the Feed graph model, and its subsequent promotion across multiple product lines.

Baidugraph embeddinggraph models
0 likes · 10 min read
Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution
AntTech
AntTech
Dec 13, 2023 · Information Security

Graph-Based Intelligent Risk Control: Technologies, Infrastructure, and Real‑World Cases

The article reviews the rise of graph‑based intelligent risk control in the digital economy, outlining its technological evolution, key algorithmic capabilities, underlying infrastructure requirements, and practical case studies that demonstrate its impact on financial security and high‑concurrency scenarios.

big data analyticsfinancial securitygraph neural networks
0 likes · 9 min read
Graph-Based Intelligent Risk Control: Technologies, Infrastructure, and Real‑World Cases
JD Retail Technology
JD Retail Technology
Nov 23, 2023 · Artificial Intelligence

Recent Advances in Advertising Recommendation Algorithms and Their Applications

This article reviews recent progress in advertising recommendation technologies, covering deep learning‑based ranking, sequence modeling, self‑supervised learning, online and reinforcement learning, multimodal recommendation, and fairness, and details four key breakthroughs—data‑driven incremental learning, dynamic group parameter modeling, bilateral interactive graph convolution, and a relation‑aware diffusion model for poster layout generation, along with experimental results and future challenges.

Deep LearningDiffusion ModelsIncremental Learning
0 likes · 25 min read
Recent Advances in Advertising Recommendation Algorithms and Their Applications
Alimama Tech
Alimama Tech
Nov 22, 2023 · Artificial Intelligence

Robust Link Prediction under Bilateral Edge Noise via Robust Graph Information Bottleneck (RGIB)

The paper introduces Robust Graph Information Bottleneck (RGIB), a framework that jointly mitigates bilateral edge noise in link prediction by decoupling topology, label, and representation information, with two variants (RGIB‑SSL and RGIB‑REP) that achieve up to 12.9% AUC gains on benchmarks and have already boosted click‑through‑rate robustness and revenue in Alibaba’s advertising system.

RGIBRobustnessbilateral noise
0 likes · 13 min read
Robust Link Prediction under Bilateral Edge Noise via Robust Graph Information Bottleneck (RGIB)
Kuaishou Tech
Kuaishou Tech
Nov 21, 2023 · Artificial Intelligence

Kuaishou Academic Forum on Cutting-Edge Short Video Recommendation Algorithms (Nov 23, 2023)

The Kuaishou Academic Forum held on November 23 in Beijing presented cutting‑edge research on short‑video recommendation algorithms, featuring talks on reinforcement learning, user interest modeling, graph neural networks, and a comprehensive recommender‑system simulator, while also offering registration details and a brief company overview.

KuaishouReinforcement Learningacademic forum
0 likes · 5 min read
Kuaishou Academic Forum on Cutting-Edge Short Video Recommendation Algorithms (Nov 23, 2023)
DataFunSummit
DataFunSummit
Nov 17, 2023 · Artificial Intelligence

Semantic‑Aware Active Learning on Graph Data for Risk Control: Tackling Sample Imbalance

This presentation discusses the challenges of label scarcity and class imbalance in graph‑based risk‑control scenarios and proposes a semantic‑aware active‑learning framework that combines uncertainty, graph structure, prototype diversity, and double‑channel information alignment to improve node classification performance.

active learninggraph datagraph neural networks
0 likes · 18 min read
Semantic‑Aware Active Learning on Graph Data for Risk Control: Tackling Sample Imbalance
DataFunTalk
DataFunTalk
Sep 21, 2023 · Artificial Intelligence

Active Learning and Sample Imbalance in Graph Data for Risk Control

This presentation explores the challenges of label scarcity and class imbalance in graph‑based risk‑control scenarios, proposing semantic‑aware active learning and prototype‑driven sampling strategies to improve node classification performance on imbalanced graph datasets.

active learninggraph datagraph neural networks
0 likes · 16 min read
Active Learning and Sample Imbalance in Graph Data for Risk Control
Alimama Tech
Alimama Tech
Sep 12, 2023 · Artificial Intelligence

Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search

CC‑GNN addresses three drawbacks of existing graph‑neural retrieval for e‑commerce by adding content phrase nodes, scalable meta‑path message passing, and difficulty‑aware noisy contrastive learning with counterfactual augmentation, achieving up to 16 % recall improvement and notably larger gains on long‑tail queries and cold‑start items.

E-commerce SearchLong Tailcold start
0 likes · 19 min read
Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search
Baidu Tech Salon
Baidu Tech Salon
Aug 30, 2023 · Artificial Intelligence

How Spatial Heterophily Improves Urban Graph Neural Networks

Urban graphs often exhibit spatial heterophily, where neighboring nodes differ significantly, limiting traditional GNNs; this article introduces a Spatial Heterophily Aware GNN (SHGNN) that partitions neighborhoods by geographic proximity, employs rotation‑scaling aggregation and heterophily‑sensitive interaction, and demonstrates superior performance on three real city datasets.

Heterophily-AwareNode RepresentationPGL
0 likes · 13 min read
How Spatial Heterophily Improves Urban Graph Neural Networks
JD Tech
JD Tech
Jul 24, 2023 · Artificial Intelligence

An Introduction to Graph Computing: Concepts, History, and Real‑World Applications

This article provides a comprehensive overview of graph computing, covering its fundamental concepts, historical development from Euler's bridges to modern graph neural networks, various algorithmic techniques, and practical applications in search, recommendation, finance, fraud detection, and many other AI‑driven domains.

graph computinggraph neural networksgraph theory
0 likes · 12 min read
An Introduction to Graph Computing: Concepts, History, and Real‑World Applications
DataFunTalk
DataFunTalk
Jul 18, 2023 · Artificial Intelligence

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

This article presents Fliggy's work on user travel demand prediction, outlining the unique challenges of travel scenarios, the evolution of recall and ranking algorithms—including multi‑task learning, graph‑based models, and intention‑capture mechanisms—and discusses future research directions such as long‑sequence modeling and cross‑domain learning.

Recommendation Systemsgraph neural networksmachine learning
0 likes · 19 min read
Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions
DataFunTalk
DataFunTalk
Jul 16, 2023 · Artificial Intelligence

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

This article introduces graph neural networks, explains graph representation learning, discusses their evolution from random walks to spectral and spatial convolutions, and details how OPPO applies GNNs to improve recommendation system recall and ranking, highlighting practical architecture, experimental gains, and future research directions.

OPPORecommendation Systemsgraph neural networks
0 likes · 19 min read
Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice
DataFunTalk
DataFunTalk
Jul 4, 2023 · Artificial Intelligence

Subgraph Graph Neural Networks: Balancing Scalability and Expressivity

This article reviews the challenges of traditional graph neural networks on large graphs, introduces subgraph‑based GNN methods such as GraphSAINT, Shadow‑GNN, OSAN and ESAN, and discusses how these approaches improve scalability and expressive power while outlining future research directions.

AIexpressivitygraph neural networks
0 likes · 14 min read
Subgraph Graph Neural Networks: Balancing Scalability and Expressivity
DataFunSummit
DataFunSummit
Jun 26, 2023 · Artificial Intelligence

Advances in Graph Neural Networks and Graph Representation Learning for Protein Modeling

This article reviews the fundamentals of graph neural networks and graph representation learning, explains why proteins can be modeled as graphs, and surveys recent GNN‑based applications such as structure prediction, function annotation, protein design, and self‑supervised representation learning, concluding with future research directions.

AlphaFold2bioinformaticsgraph neural networks
0 likes · 12 min read
Advances in Graph Neural Networks and Graph Representation Learning for Protein Modeling
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jun 5, 2023 · Artificial Intelligence

How Alibaba’s DGS Enables Real‑Time GNN Inference on Massive Dynamic Graphs

The Dynamic Graph Sampling (DGS) service, built on GraphLearn, delivers sub‑20 ms latency for real‑time GNN inference on large, constantly evolving graphs by separating storage from computation, using event‑driven pre‑sampling, lazy multi‑hop concatenation, and a publish‑subscribe architecture that scales linearly across distributed workers.

Alibaba CloudDistributed SystemsGraphLearn
0 likes · 12 min read
How Alibaba’s DGS Enables Real‑Time GNN Inference on Massive Dynamic Graphs
DataFunTalk
DataFunTalk
May 29, 2023 · Artificial Intelligence

Applying Graph Computing for Risk Control in Wing Pay: Architecture, Algorithms, and Future Directions

The presentation details how Wing Pay leverages graph computing and graph neural networks to detect and mitigate financial fraud across payment, e‑commerce, and credit scenarios, describing business background, system architecture, algorithmic approaches, real‑time subgraph mining, and future research directions.

distributed graph databasefinancial fraud detectiongraph computing
0 likes · 15 min read
Applying Graph Computing for Risk Control in Wing Pay: Architecture, Algorithms, and Future Directions
DataFunTalk
DataFunTalk
May 27, 2023 · Artificial Intelligence

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

The article presents a comprehensive overview of how graph algorithms are applied in Alibaba's e‑commerce risk control system, detailing six sections that include risk scenario introductions, interaction and product content risk methods, dynamic heterogeneous graph practices, a large‑scale competition, and future research directions.

Dynamic GraphRisk Detectione-commerce risk
0 likes · 18 min read
Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights
Top Architect
Top Architect
May 25, 2023 · Artificial Intelligence

A Brief Overview of Graph Neural Networks: GCN, GraphSAGE, GAT, GAE and DiffPool

This article provides an introductory overview of graph neural networks, explaining their motivation, basic concepts, and detailing classic models such as GCN, GraphSAGE, GAT, Graph Auto‑Encoder, and DiffPool, along with their advantages, limitations, and experimental results on various benchmark datasets.

DiffPoolGATGCN
0 likes · 20 min read
A Brief Overview of Graph Neural Networks: GCN, GraphSAGE, GAT, GAE and DiffPool
Architect
Architect
May 24, 2023 · Artificial Intelligence

A Comprehensive Overview of Graph Neural Networks: Models, Techniques, and Applications

Graph Neural Networks (GNNs) have become a research hotspot, and this article provides an intuitive overview of classic GNN models such as GCN, GraphSAGE, GAT, graph auto‑encoders, and DiffPool, discussing their architectures, advantages, limitations, and experimental results across various benchmark datasets.

DiffPoolGATGCN
0 likes · 18 min read
A Comprehensive Overview of Graph Neural Networks: Models, Techniques, and Applications
DataFunTalk
DataFunTalk
May 24, 2023 · Artificial Intelligence

Graph Transfer Learning and VS-Graph: Knowledge Transferable Graph Neural Networks

This article reviews recent advances in graph transfer learning, introduces the novel VS-Graph scenario for knowledge transfer between dominant and silent nodes, and details the Knowledge Transferable Graph Neural Network (KTGNN) framework with domain‑adaptive feature completion, message passing, and transferable classifier modules, highlighting experimental results and future research directions.

AIKnowledge TransferVS-Graph
0 likes · 27 min read
Graph Transfer Learning and VS-Graph: Knowledge Transferable Graph Neural Networks
JD Cloud Developers
JD Cloud Developers
May 17, 2023 · Artificial Intelligence

Why Graph Computing Is the Hidden Powerhouse Behind AI and Fraud Detection

This article introduces graph computing, explaining its fundamentals, historical origins, key concepts such as nodes, edges, degrees, and graph representations, and explores its algorithms, graph neural networks, and real‑world applications ranging from search engines and social graphs to financial fraud detection and emerging AI technologies.

Artificial Intelligencefraud detectiongraph computing
0 likes · 12 min read
Why Graph Computing Is the Hidden Powerhouse Behind AI and Fraud Detection
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 27, 2023 · Artificial Intelligence

How uGrapher Boosts GNN Performance 3.5× with a Unified Graph Operator Abstraction

Alibaba Cloud's PAI platform and Shanghai Jiao Tong University’s team announced their ASPLOS 2023‑accepted paper uGrapher, which unifies graph operator computation for GNNs, achieving up to 3.5× speedup over existing frameworks and paving the way for industrial‑scale acceleration.

ASPLOS 2023Alibaba Cloud PAIHigh‑performance computing
0 likes · 4 min read
How uGrapher Boosts GNN Performance 3.5× with a Unified Graph Operator Abstraction
Baidu Geek Talk
Baidu Geek Talk
Mar 20, 2023 · Artificial Intelligence

How Graph Neural Networks Boost Anti‑Cheat in User Referral Activities

This article analyzes the use of graph neural network models, including GCN and multi‑graph SCGCN, to tackle cheating in referral‑based user acquisition by capturing user relationships, improving sample purity, and achieving up to a 50% increase in cheat‑sample recall.

GCNSCGCNanti-cheat
0 likes · 12 min read
How Graph Neural Networks Boost Anti‑Cheat in User Referral Activities
AntTech
AntTech
Feb 24, 2023 · Artificial Intelligence

Large-Scale Complex Heterogeneous Graph Data Intelligent Analysis Technology Wins 2022 CIEE Science and Technology Award

The 2022 China Institute of Electronics (CIEE) Science and Technology Award recognized a collaborative project between Beijing University of Posts and Telecommunications and Ant Group for pioneering large-scale heterogeneous graph neural network models, a trillion‑scale dynamic graph learning system, and extensive industry applications, earning top honors, patents, papers, and standards.

Technology Awardgraph neural networksheterogeneous graphs
0 likes · 4 min read
Large-Scale Complex Heterogeneous Graph Data Intelligent Analysis Technology Wins 2022 CIEE Science and Technology Award
Baidu Geek Talk
Baidu Geek Talk
Feb 17, 2023 · Artificial Intelligence

How PGLBox Achieves 27× Faster GPU‑Powered Large‑Scale Graph Learning

PGLBox, Baidu’s GPU‑based large‑scale graph training framework, delivers up to 27× speedup over CPU clusters by fully GPU‑accelerating storage, sampling, and training, supporting billions of nodes, advanced GNN algorithms, multi‑level storage, and seamless integration of massive pretrained models.

GPULarge-Scale TrainingPGLBox
0 likes · 7 min read
How PGLBox Achieves 27× Faster GPU‑Powered Large‑Scale Graph Learning
DataFunTalk
DataFunTalk
Jan 27, 2023 · Artificial Intelligence

GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks

This article reviews the role of graph neural networks in AI for science, covering background, the evolution of GNN models, applications in physics and biomedicine, recent advances in Euclidean equivariant GNNs, and the authors' own contributions such as GMN and GROVER, concluding with key distinctions between traditional GNNs and science‑focused approaches.

AI for ScienceMolecular Representationequivariant GNN
0 likes · 16 min read
GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks
DataFunSummit
DataFunSummit
Jan 18, 2023 · Artificial Intelligence

Interview on the Current State, Challenges, and Future Trends of Graph Algorithms

This interview summarizes experts' insights on graph algorithm technology, covering its early industrial adoption, data scale and sparsity challenges, various graph types and models, application scenarios such as recommendation and risk control, R&D workflow hurdles, and emerging research directions like pre‑training, explainability, and combinatorial optimization.

ApplicationsFuture Trendsgraph algorithms
0 likes · 14 min read
Interview on the Current State, Challenges, and Future Trends of Graph Algorithms
DataFunTalk
DataFunTalk
Dec 30, 2022 · Artificial Intelligence

Graph Representation Learning for Drug Package Recommendation: Discriminative and Generative Approaches

This article reviews the challenges of drug package recommendation in smart healthcare and presents two graph‑based solutions—a discriminative model (DPR) that scores existing drug packages and a generative model (DPG) that creates personalized packages—demonstrating superior performance through extensive experiments and analysis.

AI in healthcareGenerative ModelsReinforcement Learning
0 likes · 19 min read
Graph Representation Learning for Drug Package Recommendation: Discriminative and Generative Approaches
DataFunTalk
DataFunTalk
Dec 28, 2022 · Artificial Intelligence

A Comprehensive Survey of Graph Neural Networks: Development, Complex Graph Models, Applications, Scalability, and Future Directions

This article provides an extensive overview of graph neural networks, tracing their evolution from early RNN‑based models to modern message‑passing frameworks, discussing complex graph types, diverse real‑world applications, scalability challenges, design spaces, training platforms, and promising research directions.

Deep LearningGNNScalability
0 likes · 49 min read
A Comprehensive Survey of Graph Neural Networks: Development, Complex Graph Models, Applications, Scalability, and Future Directions
DataFunTalk
DataFunTalk
Dec 25, 2022 · Artificial Intelligence

GraphSynergy: A Network‑Inspired Deep Learning Model for Predicting Anti‑Cancer Drug Combinations

GraphSynergy integrates network science and graph convolutional networks to predict synergistic anti‑cancer drug combinations by modeling protein‑protein interaction networks, computing therapy and toxicity scores, and outperforming baseline methods on DrugCombDB and Oncology‑Screen datasets, while offering interpretable mechanisms for drug repurposing.

AI drug discoverycancer therapydrug repurposing
0 likes · 19 min read
GraphSynergy: A Network‑Inspired Deep Learning Model for Predicting Anti‑Cancer Drug Combinations
AntTech
AntTech
Dec 5, 2022 · Artificial Intelligence

Four AAAI‑23 Papers from Ant Security Lab on Adversarial 3D Point Clouds, GNN‑Based Anti‑Money Laundering, Spiking Neural Network Dynamic Graph Learning, and Differential‑Private Adaptive Clipping

Ant Security Lab reports four AAAI‑23 accepted papers that introduce PF‑Attack for transferable 3D adversarial point clouds, AMAP a GNN‑driven anti‑money‑laundering framework, SpikeNet a spiking‑neural‑network approach for efficient dynamic graph representation, and DP‑PSAC a per‑sample adaptive clipping method for differential privacy, each with experimental validation and expert commentary.

AAAI-23adversarial attacksdifferential privacy
0 likes · 18 min read
Four AAAI‑23 Papers from Ant Security Lab on Adversarial 3D Point Clouds, GNN‑Based Anti‑Money Laundering, Spiking Neural Network Dynamic Graph Learning, and Differential‑Private Adaptive Clipping
DataFunSummit
DataFunSummit
Nov 30, 2022 · Artificial Intelligence

Combining Knowledge Graphs with Personalized News Recommendation Systems

This article presents a comprehensive overview of a personalized news recommendation system that leverages knowledge graphs to improve accuracy, explainability, and user satisfaction, detailing background motivations, graph construction methods, model architecture, experimental results, and practical insights from a Meituan research perspective.

Deep Learningexplainabilitygraph neural networks
0 likes · 23 min read
Combining Knowledge Graphs with Personalized News Recommendation Systems
Meituan Technology Team
Meituan Technology Team
Nov 24, 2022 · Artificial Intelligence

Large-Scale Graph Retrieval for Meituan In-Store Advertising: Design, Optimization, and Deployment

The article details Meituan's deployment of large-scale heterogeneous graph recall for in‑store recommendation ads, covering full‑scene graph construction, graph pruning, dynamic negative sampling, spatiotemporal sub‑graph fusion, and performance optimizations that together raise offline hit‑rate by over 5% and online revenue per search by 10‑15%.

Large-Scale TrainingMeituangraph neural networks
0 likes · 25 min read
Large-Scale Graph Retrieval for Meituan In-Store Advertising: Design, Optimization, and Deployment
Tencent Advertising Technology
Tencent Advertising Technology
Nov 15, 2022 · Artificial Intelligence

PaSca: A Scalable Graph Neural Architecture Search System for Large‑Scale Graph Learning

The paper presents PaSca, a scalable graph neural architecture search system that introduces a new SGAP modeling paradigm, a 150,000‑structure design space, and an automated multi‑objective search engine, achieving high scalability and strong predictive performance on real‑world large‑scale graph tasks.

Architecture SearchPaScaTencent
0 likes · 10 min read
PaSca: A Scalable Graph Neural Architecture Search System for Large‑Scale Graph Learning
Alimama Tech
Alimama Tech
Nov 9, 2022 · Artificial Intelligence

Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

The paper introduces a graph‑based weakly supervised contrastive learning framework that uses heterogeneous user‑behavior graphs, e‑commerce‑specific augmentations, and a hybrid fine‑tuning/transfer learning strategy to improve semantic relevance matching between queries and product titles, achieving significant gains on a large‑scale Taobao dataset.

Weak Supervisioncontrastive learninge‑commerce
0 likes · 12 min read
Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce
AntTech
AntTech
Nov 6, 2022 · Artificial Intelligence

Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking

This article surveys recent Ant Group research on explainable fraud detection, including constraint‑adaptive rule‑set learning (CRSL), meta‑path guided rule generation (MetaRule), biased sampling for imbalanced ranking, and a semi‑supervised data‑augmentation framework (SDAT) for tabular data, highlighting their motivations, methodologies, deployments, and experimental results.

Semi-supervised Learningconstraint adaptivedata augmentation
0 likes · 18 min read
Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking
DataFunTalk
DataFunTalk
Oct 28, 2022 · Artificial Intelligence

Geometric Graph Neural Networks for Drug Discovery: 3D Structure‑Based Binding Affinity Prediction and Molecular Property Learning

This article presents a comprehensive overview of using geometric graph neural networks on the Baidu PaddleHelix platform to address challenges in drug discovery, including 3D‑structure‑aware protein‑ligand binding affinity prediction, molecular property prediction, and self‑supervised pre‑training, with experimental results showing significant improvements over existing baselines.

drug discoverygeometric-deep-learninggraph neural networks
0 likes · 16 min read
Geometric Graph Neural Networks for Drug Discovery: 3D Structure‑Based Binding Affinity Prediction and Molecular Property Learning
Alimama Tech
Alimama Tech
Oct 12, 2022 · Artificial Intelligence

Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval

Decoupled Graph Neural Networks (DC‑GNN) improve large‑scale e-commerce ad recall by separating graph processing from CTR prediction, using multi‑task pretraining (edge prediction + contrastive learning), efficient deep linear aggregation, and a dual‑tower CTR model, achieving higher efficiency and performance on billions‑scale data.

CTR predictionDecoupled ArchitectureLarge-Scale Graph
0 likes · 15 min read
Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval
DataFunTalk
DataFunTalk
Oct 6, 2022 · Information Security

Graph Machine Learning for Security Risk Control: Architecture, Models, and Future Directions

This article presents a comprehensive overview of applying graph machine learning to security risk control, covering background cases, system architecture, dynamic heterogeneous graph modeling with HGT and DDGCL, experimental results, and future research directions for fraud, money‑laundering, and gambling detection.

contrastive learningdynamic heterogeneous graphfinancial fraud detection
0 likes · 10 min read
Graph Machine Learning for Security Risk Control: Architecture, Models, and Future Directions
DataFunSummit
DataFunSummit
Oct 4, 2022 · Artificial Intelligence

Graph Federated Learning: Necessity, Classification, Algorithms, Platform Architecture, and Financial Applications

This article provides a comprehensive overview of graph federated learning, covering its motivation, taxonomy, representative algorithms, platform design, practical financial use cases, and future research challenges, with a focus on privacy-preserving distributed graph neural network training.

AIFederated LearningFinancial Applications
0 likes · 15 min read
Graph Federated Learning: Necessity, Classification, Algorithms, Platform Architecture, and Financial Applications
AntTech
AntTech
Sep 29, 2022 · Artificial Intelligence

Privacy-Preserving Vertical Federated Graph Neural Network for Node Classification

This article presents VFGNN, a privacy‑preserving vertical federated graph neural network designed for node classification, detailing its architecture, differential‑privacy enhancements, and experimental results that demonstrate superior accuracy over single‑party baselines across multiple graph datasets.

Federated LearningVertical Partitiondifferential privacy
0 likes · 14 min read
Privacy-Preserving Vertical Federated Graph Neural Network for Node Classification
DataFunTalk
DataFunTalk
Sep 26, 2022 · Artificial Intelligence

Intelligent Entity Recommendation in Search Scenarios: Architecture, Relevance, Sparse Data Recall, and Multi‑Domain Strategies

This article presents a comprehensive overview of intelligent entity recommendation for search, covering scenario introduction, relevance modeling, handling sparse query and entity data with graph‑based methods, and multi‑domain, multi‑scenario ranking techniques to improve user experience.

Multi-domainSearchSparse Data
0 likes · 15 min read
Intelligent Entity Recommendation in Search Scenarios: Architecture, Relevance, Sparse Data Recall, and Multi‑Domain Strategies
DataFunTalk
DataFunTalk
Sep 19, 2022 · Artificial Intelligence

Pretraining Models and Graph Neural Networks for Recommendation Systems

This talk explores the evolution, objectives, and core challenges of pretraining models, their application in recommendation scenarios, service modes, and detailed case studies of graph neural network pretraining, illustrating how self‑supervised learning and multi‑domain data integration enhance user and item embeddings for improved recommendation performance.

Multi-domainRecommendation Systemsgraph neural networks
0 likes · 16 min read
Pretraining Models and Graph Neural Networks for Recommendation Systems
DataFunTalk
DataFunTalk
Sep 10, 2022 · Artificial Intelligence

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

This article reviews how graph neural networks are applied across the three stages of recommendation systems—recall, ranking, and re‑ranking—detailing novel models such as NIA‑GCN, GraphSAIL, and DGENN, their experimental improvements, and future research directions.

GNN recallIncremental LearningRecommendation Systems
0 likes · 17 min read
Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking
DataFunSummit
DataFunSummit
Sep 8, 2022 · Artificial Intelligence

GAST: Graph Adaptive Semantic Transfer Model for Cross‑Domain Sentiment Analysis

This article introduces GAST, a graph‑adaptive semantic transfer framework that combines POS‑based Transformers and hybrid graph attention to improve cross‑domain sentiment analysis, presents related work, details the model architecture, reports extensive experiments showing state‑of‑the‑art results, and discusses future directions.

GAST modelNLPPOS tagging
0 likes · 13 min read
GAST: Graph Adaptive Semantic Transfer Model for Cross‑Domain Sentiment Analysis
Meituan Technology Team
Meituan Technology Team
Sep 8, 2022 · Artificial Intelligence

Graph Neural Network Based Scene Modeling for Food Delivery CTR Prediction

The article details Meituan Waimai's use of graph neural network techniques—feature‑graph crossing, subgraph expansion, and metapath‑based scene graphs—to model user‑restaurant interactions across location, time, and context, describing the engineering pipeline, online serving optimizations, and offline AUC improvements of up to 2.5 ‰ for high‑ and low‑frequency scenarios.

CTR predictionMeituan WaimaiRecommendation Systems
0 likes · 29 min read
Graph Neural Network Based Scene Modeling for Food Delivery CTR Prediction
DataFunSummit
DataFunSummit
Aug 23, 2022 · Artificial Intelligence

Graph Deep Learning for Content Risk Control and APT Detection

This article presents a comprehensive overview of Tencent AI Lab's graph‑based approaches for detecting misinformation and advanced persistent threats, detailing the challenges of modeling news content and social context, the design of the Post‑User Interaction Network (PSIN), experimental results on large multi‑topic datasets, and a novel graph‑pretraining pipeline for APT detection.

APT detectionDeep LearningSocial Network Analysis
0 likes · 12 min read
Graph Deep Learning for Content Risk Control and APT Detection
DataFunSummit
DataFunSummit
Jul 30, 2022 · Artificial Intelligence

Graph Link Prediction Techniques, Self‑Developed GNN Models, and Applications in Risk Control

This article reviews graph link prediction problems, categorizes existing methods from heuristics to GNN‑based approaches, introduces several self‑designed neighborhood attention networks and adversarial negative‑sampling strategies, discusses pairwise ranking objectives, reports OGB competition results, and explores practical risk‑control applications.

AIgraph link predictiongraph neural networks
0 likes · 15 min read
Graph Link Prediction Techniques, Self‑Developed GNN Models, and Applications in Risk Control
DataFunTalk
DataFunTalk
Jul 9, 2022 · Artificial Intelligence

Graph Neural Networks Enter the Transformer Era – Seminar by Dr. Zheng Shuxin

The LOGS seminar on July 9, 2022 featured Dr. Zheng Shuxin from Microsoft Research presenting an overview of Transformer models, their success in NLP and CV, recent breakthroughs in applying Transformers to graph data, and future directions for graph processing.

AI SeminarMicrosoft researchTransformer
0 likes · 4 min read
Graph Neural Networks Enter the Transformer Era – Seminar by Dr. Zheng Shuxin
DataFunTalk
DataFunTalk
Jul 7, 2022 · Artificial Intelligence

How Powerful are Spectral Graph Neural Networks?

On July 9, 2022, the LOGS seminar hosted Prof. Zhang Muhan from Peking University, who presented his talk “How Powerful are Spectral Graph Neural Networks?” covering the simplification to Linear GNNs, universal approximation, connections to the Weisfeiler‑Lehman test, and introducing the JacobiConv model.

JacobiConvSeminarSpectral GNN
0 likes · 4 min read
How Powerful are Spectral Graph Neural Networks?
DataFunSummit
DataFunSummit
Jul 3, 2022 · Artificial Intelligence

Graph Neural Network Approaches for Internet Financial Fraud Detection

The talk examines how the COVID‑19 pandemic accelerated online financial services and fraud, outlines the challenges of traditional and internet‑based fraud detection, and presents graph neural network solutions—including PC‑GNN and AO‑GNN—demonstrating their effectiveness on real‑world and public datasets while discussing future research directions.

AUC optimizationfinancial fraudfraud detection
0 likes · 12 min read
Graph Neural Network Approaches for Internet Financial Fraud Detection
AntTech
AntTech
Jun 14, 2022 · Big Data

Insights on Graph Computing: Technology, Applications, and Future Directions

Professor Chen Wenguang discusses how graph computing—originating from graph theory—offers a powerful way to model relationships across industries, its rapid development in China, challenges in scaling, integration with AI via graph neural networks, and the collaborative efforts needed between academia and industry to advance the field.

AIBig DataGraph Processing
0 likes · 17 min read
Insights on Graph Computing: Technology, Applications, and Future Directions
Tencent Cloud Developer
Tencent Cloud Developer
May 31, 2022 · Artificial Intelligence

Scalable Graph Neural Architecture Search System (PaSca) – WWW 2022 Best Student Paper

PaSca, a scalable graph neural architecture search system that separates message aggregation from updates, explores over 150,000 GNN designs with multi‑objective optimization, delivers models that outperform traditional GNNs in accuracy, memory and speed, has been open‑sourced and deployed at Tencent for risk control, recommendation and fraud detection, and earned the WWW 2022 Best Student Paper award.

Big DataNeural Architecture SearchScalable Systems
0 likes · 11 min read
Scalable Graph Neural Architecture Search System (PaSca) – WWW 2022 Best Student Paper
Alimama Tech
Alimama Tech
May 23, 2022 · Artificial Intelligence

Alibaba Mama Team Papers Accepted at KDD 2022 and Other Top Conferences

The Alibaba Mama technical team secured five paper acceptances at the prestigious KDD 2022 conference, presenting advances such as curriculum‑guided Bayesian reinforcement learning for ROI‑constrained bidding, adversarial‑gradient driven exploration for click‑through‑rate prediction, externality‑aware transformers for e‑commerce ads, multi‑modal multi‑query pretraining, and generative‑replay streaming graph neural networks.

Advertising BiddingE-commerce SearchKDD 2022
0 likes · 10 min read
Alibaba Mama Team Papers Accepted at KDD 2022 and Other Top Conferences
Meituan Technology Team
Meituan Technology Team
May 19, 2022 · Artificial Intelligence

Tulong: An Industrial Graph Neural Network Framework and Learning Platform at Meituan

Tulong is Meituan’s industrial graph neural network framework and learning platform that combines a compact MTGraph engine, a modular operator‑based GNN library, and visual workflow tools to enable heterogeneous, billion‑edge graph training on a single machine with up to 60 % memory savings and 2–4× speedups, streamlining search, recommendation, advertising and delivery pipelines.

FrameworkIndustrial AIgraph neural networks
0 likes · 24 min read
Tulong: An Industrial Graph Neural Network Framework and Learning Platform at Meituan
DataFunSummit
DataFunSummit
May 18, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces automated knowledge graph representation learning, covering background, key techniques such as triple‑based, path‑based and subgraph‑based models, AutoML‑driven model search (AutoSF, Interstellar, RED‑GNN), evaluation metrics, and future research directions in AI.

AutoMLEmbeddinggraph neural networks
0 likes · 21 min read
Automated Knowledge Graph Representation Learning: From Triples to Subgraphs
Laiye Technology Team
Laiye Technology Team
May 18, 2022 · Artificial Intelligence

Overview of Document Intelligence Models: StrucText, LayoutLMv3, and GraphDoc

This article reviews three representative document intelligence models—StrucText, LayoutLMv3, and GraphDoc—detailing their input features, feature fusion strategies, self‑supervised tasks, and underlying architectures, and explains how they learn embeddings for segments, words, or regions to enable classification and key‑value extraction.

Document AILayout AnalysisMultimodal
0 likes · 15 min read
Overview of Document Intelligence Models: StrucText, LayoutLMv3, and GraphDoc