graph mining | BestHub

DataFunSummit

Jul 28, 2023 · Big Data

User Path Analysis and SessionAnalytics: Business Practices, Technical Architecture, and Open‑Source Framework

This article introduces user path analysis and the SessionAnalytics open‑source framework, covering business scenarios, data processing techniques, algorithmic mining methods, technical architecture, implementation details, comparisons with event‑based analysis, and a comprehensive Q&A for practical deployment.

NLPbig datadata engineering

0 likes · 19 min read

User Path Analysis and SessionAnalytics: Business Practices, Technical Architecture, and Open‑Source Framework

DataFunTalk

Jan 13, 2022 · Artificial Intelligence

Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources

This article provides a comprehensive overview of fraud detection using graph neural networks, covering background definitions, fraud categories, GNN application steps, a timeline of key research papers, practical challenges, solutions, and a collection of open‑source resources and datasets.

Graph Neural Networksaifraud detection

0 likes · 24 min read

Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources

DataFunTalk

Jan 5, 2022 · Artificial Intelligence

Graph-Based Methods for Hot Event Discovery, Long Text Matching, and Ontology Construction in Natural Language Processing

This talk presents a series of graph‑based techniques for natural language processing, including the Story Forest system for hot event discovery, the GIANT framework for ontology creation and user interest modeling, and a divide‑and‑conquer approach to long‑text matching that leverages graph neural networks and community detection.

Graph Neural NetworksNatural Language ProcessingText Matching

0 likes · 19 min read

Graph-Based Methods for Hot Event Discovery, Long Text Matching, and Ontology Construction in Natural Language Processing

58 Tech

Dec 14, 2021 · Artificial Intelligence

Unsupervised Community Detection for Black‑Market Identification Using the Louvain Algorithm

This article presents an unsupervised community‑discovery approach based on the Louvain algorithm to identify black‑market accounts, describing the threat landscape, system architecture, algorithmic principles, optimizations, experimental results, and future directions for improving risk detection in large‑scale online services.

Louvain Algorithmbig datablack market

0 likes · 10 min read

Unsupervised Community Detection for Black‑Market Identification Using the Louvain Algorithm

Baidu Geek Talk

Sep 29, 2021 · Artificial Intelligence

Graph-Based Anti-Fraud: Gang Mining and Node Representation Using Graph Neural Networks

To curb large‑scale, organized fraud on Baidu’s platform, the Account Security team built a scalable heterogeneous graph framework that links accounts, features, and devices, trains GraphSAGE‑based node embeddings via link‑prediction, and leverages these representations to uncover fraud gangs, boosting detection accuracy above 90% across billions of nodes.

Graph Neural Networksanti-fraudgraph mining

0 likes · 13 min read

Graph-Based Anti-Fraud: Gang Mining and Node Representation Using Graph Neural Networks

DataFunTalk

Dec 31, 2020 · Artificial Intelligence

Introduction to Graph Neural Networks and Their Applications in Recommendation Systems

This article introduces graph neural networks, explains their underlying sampling and aggregation mechanisms, and demonstrates how they are applied in large‑scale recommendation scenarios such as video and content feeds at Tencent, highlighting practical results and lessons learned.

Graph Neural NetworksGraphSAGERecommendation systems

0 likes · 10 min read

Introduction to Graph Neural Networks and Their Applications in Recommendation Systems

JD Retail Technology

Dec 31, 2020 · Information Security

Graph Mining Algorithms for Advertising Traffic Fraud Detection and Platform Engineering

This article presents graph‑based fraud detection techniques for advertising traffic, detailing dense subgraph algorithms such as Fraudar and D‑Cube, their engineering optimizations, real‑world case studies, and the design of a scalable graph‑mining platform for large‑scale security applications.

D-CubeFraudaradvertising traffic

0 likes · 18 min read

Graph Mining Algorithms for Advertising Traffic Fraud Detection and Platform Engineering

58 Tech

Aug 29, 2019 · Information Security

Graph-Based Anomaly Detection Framework for Security Threats

The article presents a graph‑based anomaly detection architecture that tackles black‑market resource switching by constructing complex user‑traffic networks, mining graph similarities, and applying multi‑dimensional strategies to achieve high‑accuracy detection while meeting timeliness, performance, and interpretability requirements.

Anomaly DetectionBehavior Analysisbig data

0 likes · 8 min read

Graph-Based Anomaly Detection Framework for Security Threats

AntTech

Dec 1, 2017 · Big Data

Insights and Paper Summaries from KDD 2017 Conference

The article provides a comprehensive overview of KDD 2017, including acceptance statistics, best paper awards, Ant Group's contributions, detailed discussions on AB testing, graph mining, and selected research papers across data mining, machine learning, and anomaly detection, offering valuable insights for practitioners and researchers.

AB testingAnomaly DetectionData Mining

0 likes · 30 min read

Insights and Paper Summaries from KDD 2017 Conference

High Availability Architecture

Jul 12, 2017 · Artificial Intelligence

Machine Learning Platform and Risk‑Control Applications at DianRong Net

The article presents a comprehensive overview of DianRong Net's in‑house machine‑learning platform built on Spark, its workflow, pain points it addresses, risk‑control case studies using graph mining, and practical tips for improving model performance through data, algorithms, hyper‑parameter tuning and ensemble methods.

Sparkbig datagraph mining

0 likes · 14 min read

Machine Learning Platform and Risk‑Control Applications at DianRong Net