Node Clustering
62 papers with code • 19 benchmarks • 14 datasets
Benchmarks
These leaderboards are used to track progress in Node Clustering
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Libraries
Use these libraries to find Node Clustering models and implementationsDatasets
IMDb Movie Reviews
Pubmed
Cora
Wiki Squirrel
Wiki-CS
Latest papers
Cluster-based Graph Collaborative Filtering
This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them.
Graph Parsing Networks
lumia-group/graphparsingnetworks
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GPN benefits from the discrete assignments generated by the graph parsing algorithm, allowing good memory efficiency while preserving node information intact.
A Contrastive Variational Graph Auto-Encoder for Node Clustering
nairouz/cvgae_pr
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Thanks to a newly identified term, our lower bound can escape Posterior Collapse and has more flexibility to account for the difference between the inference and generative models.
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
nslab-cuk/community-aware-graph-transformer
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In this paper, we propose Community-aware Graph Transformers, namely CGT, to learn degree-unbiased representations based on learnable augmentations and graph transformers by extracting within community structures.
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
6lyc/cdnmf
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Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability.
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
nslab-cuk/unified-graph-transformer
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In this paper, we propose Unified Graph Transformer Networks (UGT) that effectively integrate local and global structural information into fixed-length vector representations.
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
Classification of Edge-dependent Labels of Nodes in Hypergraphs
young917/edgedependentnodelabel
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Interestingly, many real-world systems modeled as hypergraphs contain edge-dependent node labels, i. e., node labels that vary depending on hyperedges.
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering
nustm/cpnc
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To address the two problems, we propose a new CSKG completion framework based on Contrastive Pretraining and Node Clustering (CPNC).
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years.
