graph learning | BestHub

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.

AIGraph Neural Networksgraph learning

0 likes · 19 min read

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

DataFunSummit

Jun 9, 2024 · Artificial Intelligence

Graph Technology and Graph Learning in Telecom Networks: Development, Applications, and Performance Optimization

This article reviews the evolution of graph technology, its applications in telecom, finance, and recommendation systems, discusses challenges of storage and querying large-scale graphs, and presents performance‑optimizing techniques for graph learning engines such as Wind.

Artificial IntelligencePerformance Optimizationgraph databases

0 likes · 30 min read

Graph Technology and Graph Learning in Telecom Networks: Development, Applications, and Performance Optimization

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.

Graph Neural Networksdomain adaptationgraph learning

0 likes · 22 min read

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

DataFunSummit

Apr 21, 2024 · Big Data

Application of Graph Technology in Financial Anti‑Fraud

This article explains how large‑scale graph technology is applied to financial anti‑fraud, covering background, graph‑driven perception, analysis, decision‑making and enforcement, evolution of graph methods, a comprehensive risk‑control platform, and a Q&A on practical implementation.

AIBig Datafinancial fraud detection

0 likes · 13 min read

Application of Graph Technology in Financial Anti‑Fraud

DataFunTalk

Mar 17, 2024 · Artificial Intelligence

Leveraging Large Language Models to Enhance Comprehensive Graph Learning Capabilities

In this talk, researcher Jiang Zhuoren from Zhejiang University reviews the current state of large language models applied to graph learning, discusses their roles across various graph scenarios, and outlines promising research directions for unified cross‑domain graph learning.

Artificial Intelligencecross-domain learninggraph learning

0 likes · 3 min read

Leveraging Large Language Models to Enhance Comprehensive Graph Learning Capabilities

AntTech

Dec 13, 2023 · Artificial Intelligence

IEEE ICDM 2023 Graph Learning Challenge: Community Detection and Fraud Group Mining

The IEEE ICDM 2023 Graph Learning Challenge, co‑hosted by Ant Group and Zhejiang University, showcased deep graph learning approaches for community detection and fraud‑group mining, highlighting the winning team's Risk‑DCRN method and emphasizing the importance of pretrained models in large‑scale network analysis.

ICDMcommunity-detectiondeep learning

0 likes · 5 min read

IEEE ICDM 2023 Graph Learning Challenge: Community Detection and Fraud Group Mining

AntTech

Oct 9, 2023 · Databases

TuGraph-DB v4.0: New Features Including ISO GQL Support, Enterprise High Availability, and Graph Learning Engine

TuGraph-DB v4.0, the open‑source graph database from Ant Group, introduces ISO GQL compliance, enterprise‑grade high availability with RAFT‑based leader election, and an integrated graph learning engine compatible with DGL and PyG, enhancing query capabilities, scalability, and AI‑driven analytics.

AIDistributed SystemsHigh Availability

0 likes · 5 min read

TuGraph-DB v4.0: New Features Including ISO GQL Support, Enterprise High Availability, and Graph Learning Engine

AntTech

Sep 7, 2023 · Artificial Intelligence

Ant Group Open-sources Ant Graph Learning (AGL), the First General Industrial Graph Learning System

Ant Group announced the open-source release of Ant Graph Learning (AGL), a pioneering industrial‑grade graph learning platform that supports trillion‑scale graph data, offers ready‑to‑use algorithms, and aims to lower the barrier for large‑scale graph AI applications across industries.

Ant Groupgraph learningindustrial AI

0 likes · 4 min read

Ant Group Open-sources Ant Graph Learning (AGL), the First General Industrial Graph Learning System

DataFunSummit

May 9, 2023 · Artificial Intelligence

Graph Machine Learning for Credit Risk Management: Algorithms, Systems, and Applications at Ant Group

This article presents Ant Group's use of graph machine learning for credit risk management, covering the background of small‑business lending, the proprietary AGL graph learning algorithms and system architecture, and detailed applications such as supply‑chain risk analysis, GMV prediction, and temporal graph‑based credit scoring.

Temporal GNNcredit riskgraph learning

0 likes · 15 min read

Graph Machine Learning for Credit Risk Management: Algorithms, Systems, and Applications at Ant Group

Baidu Geek Talk

Feb 15, 2023 · Artificial Intelligence

PaddlePaddle 2.4 Release: New Sparse, Graph, and Audio APIs

PaddlePaddle 2.4 introduces 167 new APIs—including sparse computing (paddle.sparse), graph learning (paddle.geometric), and audio processing (paddle.audio) modules—enabling efficient sparse model training and inference, graph message‑passing, advanced audio feature extraction, plus fresh loss functions, tensor utilities, and expanded vision transforms.

API ReleaseAudio ProcessingPaddlePaddle

0 likes · 16 min read

PaddlePaddle 2.4 Release: New Sparse, Graph, and Audio APIs

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.

GNNGraph Neural Networksdeep learning

0 likes · 49 min read

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

DataFunSummit

Sep 19, 2022 · Artificial Intelligence

Privacy-Preserving Graph Learning and Recommendation: Techniques, Challenges, and Platform Overview

This article reviews the rapid development of privacy-preserving computation, explains its classification, discusses differential privacy, secure multi‑party computation, federated and split learning, and demonstrates how these techniques can be combined for graph learning and recommendation systems, culminating in a description of the JinZhiTa privacy‑computing platform.

Federated LearningRecommendation systemsdifferential privacy

0 likes · 20 min read

Privacy-Preserving Graph Learning and Recommendation: Techniques, Challenges, and Platform Overview

DataFunTalk

Sep 18, 2022 · Artificial Intelligence

Applying Graph Machine Learning in Ant Group's Recommendation System

This article presents how Ant Group leverages graph machine learning, including knowledge graph, social network, and cross-domain techniques, to enhance recommendation for low-activity users across scenarios such as fund, coupon, and waistband recommendations, detailing model architecture, challenges, solutions, and experimental results.

GNNgraph learningknowledge graph

0 likes · 13 min read

Applying Graph Machine Learning in Ant Group's Recommendation System

Alimama Tech

Dec 15, 2021 · Artificial Intelligence

Scalable Multi-View Ad Retrieval (SMAD): A Graph-Based Framework for E-commerce Advertising

SMAD is a scalable graph‑based ad retrieval framework for e‑commerce search that builds a heterogeneous Query‑Item‑Ad graph, learns multi‑view embeddings with a parallel deep neural network and attention, employs category‑aware sampling for efficient distributed training, and delivers significant gains in offline relevance and online CTR, RPM, and PVR.

Ad RetrievalAttentiondistributed training

0 likes · 17 min read

Scalable Multi-View Ad Retrieval (SMAD): A Graph-Based Framework for E-commerce Advertising

DataFunTalk

Apr 12, 2021 · Artificial Intelligence

Comprehensive Survey of Graph Neural Networks: 15 Key Review Papers and Resources

This article compiles and summarizes fifteen influential survey papers on Graph Neural Networks, covering their models, applications, datasets, benchmarks, challenges, and future directions, while providing links to the original PDFs and highlighting distinctions between small and large-scale graph learning.

Graph Neural NetworksSurveydeep learning

0 likes · 20 min read

Comprehensive Survey of Graph Neural Networks: 15 Key Review Papers and Resources