Papers Graph Mining
“Graph Mining” 태그가 달린 논문 174편 · 필터 해제
Differentially Private Densest-$k$-Subgraph
Many graph datasets involve sensitive network data, motivating the need for privacy-preserving graph mining. The Densest-$k$-subgraph (D$k$S) problem is a key primitive in graph mining that aims to extract a subset of $k…
Graph MiningPrivacy PreservingSensitivityScalable Substructure Discovery Algorithm For Homogeneous Multilayer Networks
Graph mining analyzes real-world graphs to find core substructures (connected subgraphs) in applications modeled as graphs. Substructure discovery is a process that involves identifying meaningful patterns, structures, o…
Graph MiningTowards Unbiased Federated Graph Learning: Label and Topology Perspectives
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the dominant paradigm, with most work focuse…
FairnessGraph LearningGraph MiningNode Classification+1Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network
Evolutionary computing, particularly genetic algorithm (GA), is a combinatorial optimization method inspired by natural selection and the transmission of genetic information, which is widely used to identify optimal solu…
Combinatorial OptimizationGraph MiningLarge Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through th…
Community DetectionGraph LearningGraph MiningLink Prediction+1Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs
In the current global economy, supply chain transparency plays a pivotal role in ensuring this security by enabling companies to monitor supplier performance and fostering accountability and responsibility. Despite the a…
Graph MiningTowards Scalable and Deep Graph Neural Networks via Noise Masking
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. However, scaling them to large graphs is challenging due to the high computational and storage costs of repeated …
Graph MiningData-driven development of cycle prediction models for lithium metal batteries using multi modal mining
Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce a novel multi modal data-driven approach empl…
Graph MiningLanguage ModelingLanguage ModellingLarge Language ModelCorrelation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Although a few efforts have been made by uti…
ClassificationGraph MiningNode ClassificationWeb Scale Graph Mining for Cyber Threat Intelligence
Defending against today's increasingly sophisticated and large-scale cyberattacks demands accurate, real-time threat intelligence. Traditional approaches struggle to scale, integrate diverse telemetry, and adapt to a con…
Graph MiningKey DetectionHigher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have prima…
Graph MiningNode ClassificationAccurate and Fast Estimation of Temporal Motifs using Path Sampling
Counting the number of small subgraphs, called motifs, is a fundamental problem in social network analysis and graph mining. Many real-world networks are directed and temporal, where edges have timestamps. Motif counting…
GPUGraph MiningRefining Wikidata Taxonomy using Large Language Models
Due to its collaborative nature, Wikidata is known to have a complex taxonomy, with recurrent issues like the ambiguity between instances and classes, the inaccuracy of some taxonomic paths, the presence of cycles, and t…
Entity TypingGraph MiningTowards Graph Prompt Learning: A Survey and Beyond
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval.…
Graph MiningPrompt LearningQuestion AnsweringRecommendation Systems+1A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation
Attributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hype…
AttributeClusteringGPUGraph Clustering+2Covering a Graph with Dense Subgraph Families, via Triangle-Rich Sets
Graphs are a fundamental data structure used to represent relationships in domains as diverse as the social sciences, bioinformatics, cybersecurity, the Internet, and more. One of the central observations in network scie…
Graph MiningWhen Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node and edge types exist in the graph, and he…
BenchmarkingGraph LearningGraph MiningModel Selection+1Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representations of GNNs, existing explanation studie…
ClusteringGraph ClassificationGraph ClusteringGraph MiningIs Your Large Language Model Knowledgeable or a Choices-Only Cheater?
Recent work shows that large language models (LLMs) can answer multiple-choice questions using only the choices, but does this mean that MCQA leaderboard rankings of LLMs are largely influenced by abilities in choices-on…
Graph MiningLanguage ModelingLanguage ModellingLarge Language Model+1Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of …
ClusteringCommunity DetectionContrastive LearningGraph Clustering+1