Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes
The rise of graph-structured data has driven interest in graph learning and synthetic data generation. While successful in text and image domains, synthetic graph generation remains challenging -- especially for real-wor…
Synthetic Data GenerationGraph GenerationKnowledge GraphsGraph LearningAggregation-aware MLP: An Unsupervised Approach for Graph Message-passing
Graph Neural Networks (GNNs) have become a dominant approach to learning graph representations, primarily because of their message-passing mechanisms. However, GNNs typically adopt a fixed aggregator function such as Mea…
Graph LearningWho Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks
Graph-structured data is prevalent in many real-world applications, including social networks, financial systems, and molecular biology. Graph Neural Networks (GNNs) have become the de facto standard for learning from su…
Node ClassificationFederated LearningGraph LearningMulti-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting
Time-evolving traffic flow forecasting are playing a vital role in intelligent transportation systems and smart cities. However, the dynamic traffic flow forecasting is a highly nonlinear problem with complex temporal-sp…
Graph LearningFedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
Federated Graph Learning (FGL) is a distributed learning paradigm that enables collaborative training over large-scale subgraphs located on multiple local systems. However, most existing FGL approaches rely on synchronou…
Image ClassificationFederated LearningGraph LearningNot All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many fe…
Tabular Data GenerationGraph LearningLeveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-world heterophilic graphs. Existing model…
Node ClassificationGraph LearningGraph Learning Metallic Glass Discovery from Wikipedia
Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal …
Recommendation SystemsGraph LearningBipartite Patient-Modality Graph Learning with Event-Conditional Modelling of Censoring for Cancer Survival Prediction
Accurately predicting the survival of cancer patients is crucial for personalized treatment. However, existing studies focus solely on the relationships between samples with known survival risks, without fully leveraging…
Graph LearningA Comprehensive Data-centric Overview of Federated Graph Learning
In the era of big data applications, Federated Graph Learning (FGL) has emerged as a prominent solution that reconcile the tradeoff between optimizing the collective intelligence between decentralized datasets holders an…
Federated LearningGraph LearningPyG 2.0: Scalable Learning on Real World Graphs
PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0 (and its subsequent minor versions), a …
Graph LearningQuantizing Text-attributed Graphs for Semantic-Structural Integration
Text-attributed graphs (TAGs) have emerged as a powerful representation for modeling complex relationships across diverse domains. With the rise of large language models (LLMs), there is growing interest in leveraging th…
Node ClassificationTransfer LearningGraph LearningThe Origin of Self-Attention: Pairwise Affinity Matrices in Feature Selection and the Emergence of Self-Attention
The self-attention mechanism, now central to deep learning architectures such as Transformers, is a modern instance of a more general computational principle: learning and using pairwise affinity matrices to control how …
Graph LearningIncremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures
The escalating threat of cyberattacks on real-time critical infrastructures poses serious risks to public safety, demanding detection methods that effectively capture complex system interdependencies and adapt to evolvin…
Graph ClassificationAnomaly DetectionGraph LearningSGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks to harness high-order collaborative filt…
Collaborative FilteringContrastive LearningGraph LearningMulti-Task Learning+1A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction
Accurately predicting drug-target interactions (DTIs) is pivotal for advancing drug discovery and target validation techniques. While machine learning approaches including those that are based on Graph Neural Networks (G…
Drug DiscoveryGraph LearningInductive LearningTransductive LearningBioScore: A Foundational Scoring Function For Diverse Biomolecular Complexes
Structural assessment of biomolecular complexes is vital for translating molecular models into functional insights, shaping our understanding of biology and aiding drug discovery. However, current structure-based scoring…
Graph LearningDrug DiscoveryGraph Neural Networks Powered by Encoder Embedding for Improved Node Learning
Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or minimally informed initial feature represen…
Graph Neural NetworkNode ClassificationGraph LearningGraph World Model
World models (WMs) demonstrate strong capabilities in prediction, generation, and planning tasks. Existing WMs primarily focus on unstructured data and cannot leverage the ubiquitous structured data, often represented as…
Graph LearningmodelRetrieval-augmented GenerationT-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
Dynamic graph learning methods have recently emerged as powerful tools for modelling relational data evolving through time. However, despite extensive benchmarking efforts, it remains unclear whether current Temporal Gra…
Graph Learning