Papers Node Clustering
“Node Clustering” 태그가 달린 논문 130편 · 필터 해제
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network
Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the p…
Graph Representation LearningNode ClassificationNode ClusteringRepresentation LearningMitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention
Graph Neural Networks (GNNs) update node representations through message passing, which is primarily based on the homophily principle, assuming that adjacent nodes share similar features. However, in real-world graphs wi…
FairnessGraph Representation LearningNode ClassificationNode Clustering+2Adjusted Count Quantification Learning on Graphs
Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances are vertices. Previously, this problem ha…
Node ClusteringTHESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplings
Graph node clustering is a fundamental unsupervised task. Existing methods typically train an encoder through selfsupervised learning and then apply K-means to the encoder output. Some methods use this clustering result …
ClusteringGraph ClusteringNode ClusteringScalable Deep Metric Learning on Attributed Graphs
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased c…
Contrastive LearningGraph EmbeddingLink PredictionMetric Learning+2TDGCN-Based Mobile Multiuser Physical-Layer Authentication for EI-Enabled IIoT
Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Indus…
Node ClusteringClustering Based on Density Propagation and Subcluster Merging
We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering…
ClusteringNode ClusteringG-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning
Graphs play a central role in modeling complex relationships across various domains. Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes wi…
Graph LearningLink PredictionNode ClassificationNode ClusteringGeographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning
Federated learning (FL) is a decentralized AI mechanism suitable for a large number of devices like in smart IoT. A major challenge of FL is the non-IID dataset problem, originating from the heterogeneous data collected …
ClusteringFederated LearningNode ClusteringCluster-wise Graph Transformer with Dual-granularity Kernelized Attention
In the realm of graph learning, there is a category of methods that conceptualize graphs as hierarchical structures, utilizing node clustering to capture broader structural information. While generally effective, these m…
Graph LearningNode ClusteringA 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+2Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
Contrastive learning is a significant paradigm in graph self-supervised learning. However, it requires negative samples to prevent model collapse and learn discriminative representations. These negative samples inevitabl…
Contrastive LearningNode ClassificationNode ClusteringRepresentation Learning+1HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning
Despite the success of Heterogeneous Graph Neural Networks (HGNNs) in modeling real-world Heterogeneous Information Networks (HINs), challenges such as expressiveness limitations and over-smoothing have prompted research…
Graph Representation LearningNode ClusteringRepresentation LearningGraphFM: A Comprehensive Benchmark for Graph Foundation Model
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream tasks. Despite extensive research into …
GPUGraph Neural NetworkLink Predictionmodel+3HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity
Clustering nodes in heterophilous graphs presents unique challenges due to the asymmetric relationships often overlooked by traditional methods, which moreover assume that good clustering corresponds to high intra-cluste…
ClusteringNode ClusteringCluster-based Graph Collaborative Filtering
Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signa…
ClusteringCollaborative FilteringNode ClusteringRecommendation Systems+1Graph Parsing Networks
Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memor…
Graph ClassificationGraph ReconstructionNode ClassificationNode ClusteringUnsupervised Optimisation of GNNs for Node Clustering
Graph Neural Networks (GNNs) can be trained to detect communities within a graph by learning from the duality of feature and connectivity information. Currently, the common approach for optimisation of GNNs is to use com…
Clusteringgraph partitioningModel SelectionNode ClusteringA Contrastive Variational Graph Auto-Encoder for Node Clustering
Variational Graph Auto-Encoders (VGAEs) have been widely used to solve the node clustering task. However, the state-of-the-art methods have numerous challenges. First, existing VGAEs do not account for the discrepancy be…
ClusteringContrastive LearningInductive BiasNode ClusteringMitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
This study utilizes community structures to address node degree biases in message-passing (MP) via learnable graph augmentations and novel graph transformers. Recent augmentation-based methods showed that MP neural netwo…
Node ClassificationNode ClusteringSelf-Supervised Learning