Graph Neural Networks

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.

Graph Neural NetworksRecommendation systemsWSDM 2025

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

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.

Graph Neural NetworksPretrainingPrompt Tuning

0 likes · 9 min read

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

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.

Graph Neural NetworksPositional EncodingTable Understanding

0 likes · 6 min read

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

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 IntelligenceGNNGraph Neural Networks

0 likes · 5 min read

Why Graph Neural Networks Are Suitable for Recommendation Systems

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.

Graph Neural Networkscombinatorial optimizationdeep learning

0 likes · 14 min read

Deep Learning Approaches for Solving Graph Optimization Problems

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 IntelligenceBERTGraph Neural Networks

0 likes · 10 min read

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

AntTech

Dec 5, 2024 · Artificial Intelligence

Simplifying Deep Learning: Research Overview by Prof. Yao Quanming

Prof. Yao Quanming presents a comprehensive overview of his research on simplifying deep learning, discussing scaling laws, data, compute and trust bottlenecks, and proposing minimalist approaches in model design, training, and interpretability, with a focus on drug interaction prediction using graph neural networks.

AI researchGraph Neural Networksdeep learning

0 likes · 17 min read

Simplifying Deep Learning: Research Overview by Prof. Yao Quanming

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.

Graph Neural NetworksRecommendation systemsTencent games

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

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.

AIGraph Neural NetworksLarge Models

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

DataFunTalk

Sep 2, 2024 · Artificial Intelligence

Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions

This article introduces graph foundation models, explains their relationship with large language models, reviews recent advances in graph neural networks and representation learning, presents the authors' own research on PT‑HGNN, Specformer and GraphTranslator, and discusses challenges, future research directions, and a Q&A session.

Artificial IntelligenceFoundation ModelsGraph Neural Networks

0 likes · 23 min read

Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions

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 GroupData Mining

0 likes · 31 min read

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

DataFunSummit

Aug 10, 2024 · Artificial Intelligence

Leveraging Large Language Models for Graph Recommendation System Optimization

This article reviews cutting‑edge research on integrating large language models with graph‑based recommendation systems, detailing four key strategies—LLM node embeddings, deep graph‑LLM fusion, model‑driven graph data training, and text‑modal enhancements—while analyzing representation learning, InfoNCE optimization, explainable recommendations, and extensive experimental validation.

Graph Neural NetworksInfoNCELLM

0 likes · 18 min read

Leveraging Large Language Models for Graph Recommendation System Optimization

DataFunSummit

Aug 4, 2024 · Artificial Intelligence

Graph Technology Overview and Applications – From GraphGPT to Graph Databases

This article presents a comprehensive overview of recent advances in graph technology, covering GraphGPT for large language models, knowledge transfer on complex graphs, financial fraud detection, telecom network optimization, graph foundation models, Baidu's multi‑domain recommendation, high‑availability graph databases, and Kuaishou's efficient recommendation architecture.

Graph Neural NetworksRecommendation systemsfinancial fraud detection

0 likes · 4 min read

Graph Technology Overview and Applications – From GraphGPT to Graph Databases

DataFunTalk

Aug 4, 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, covering the motivation behind recommending player groups, the challenges of cold‑start and data sparsity, the adaptive K‑Free community detection algorithm (DAG) with joint structural‑semantic learning, the constrained large‑scale ComRec algorithm, extensive offline and online experiments, and practical deployment insights.

Graph Neural NetworksTencent gamescommunity recommendation

0 likes · 20 min read

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

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.

Graph Neural Networkslarge language modelsmachine learning

0 likes · 14 min read

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

DataFunTalk

Jul 9, 2024 · Artificial Intelligence

Graph Knowledge Transfer and the Knowledge Bridge Learning Framework

This article presents an overview of graph knowledge transfer, discussing the data‑hungry problem, distribution shift in graph data, the Knowledge Bridge Learning (KBL) paradigm, the Bridged‑GNN implementation, experimental results across multiple scenarios, and future research directions.

Graph Neural Networksbridged-GNNdomain adaptation

0 likes · 19 min read

Graph Knowledge Transfer and the Knowledge Bridge Learning Framework

Alimama Tech

Jun 21, 2024 · Artificial Intelligence

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

CausalMMM introduces an encoder‑decoder framework that automatically discovers heterogeneous, interpretable causal graphs among advertising channels while modeling temporal decay and saturation, using Granger‑based variational inference, and achieves over 5.7% improvement in causal structure learning and significant GMV prediction gains on Alibaba’s data.

Causal InferenceGraph Neural Networksmarketing mix modeling

0 likes · 16 min read

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

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.

Artificial IntelligenceFoundation ModelsGraph Neural Networks

0 likes · 23 min read

Graph Foundation Models: Concepts, Progress, and Future Directions

DataFunSummit

May 23, 2024 · Artificial Intelligence

GraphGPT: Enabling Large Language Models as Zero‑Shot Graph Learners

GraphGPT integrates large language models with graph neural networks by introducing graph tokens and instruction tuning, enabling zero‑shot graph learning for tasks such as node classification and link prediction, and demonstrates superior performance and generalization across supervised and zero‑shot benchmarks.

Graph Neural NetworksGraphGPTZero-shot Learning

0 likes · 15 min read

GraphGPT: Enabling Large Language Models as Zero‑Shot Graph Learners