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Graph Mining
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Scalable Substructure Discovery Algorithm For Homogeneous Multilayer Networks
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs
Towards Scalable and Deep Graph Neural Networks via Noise Masking
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining
Web Scale Graph Mining for Cyber Threat Intelligence
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Accurate and Fast Estimation of Temporal Motifs using Path Sampling
Refining Wikidata Taxonomy using Large Language Models
Towards Graph Prompt Learning: A Survey and Beyond
A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation
Covering a Graph with Dense Subgraph Families, via Triangle-Rich Sets
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
Graph Mining under Data scarcity
Heterophilous Distribution Propagation for Graph Neural Networks
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
Revisiting the Message Passing in Heterophilous Graph Neural Networks
ChebMixer: Efficient Graph Representation Learning with MLP Mixer
MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute
Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networks
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,
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