Node Clustering
67 papers with code • 20 benchmarks • 15 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
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Most implemented papers
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or groups in networks.
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.
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
cynricfu/MAGNN
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A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
cmavro/Graph-InfoClust-GIC
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Motivated by this observation, we propose a graph representation learning method called Graph InfoClust (GIC), that seeks to additionally capture cluster-level information content.
Simple Spectral Graph Convolution
allenhaozhu/SSGC
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ICLR 2021
Our spectral analysis shows that our simple spectral graph convolution used in S^2GC is a low-pass filter which partitions networks into a few large parts.
Asymptotics of Network Embeddings Learned via Subsampling
aday651/embed-asym-experiments
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We prove, under the assumption that the graph is exchangeable, that the distribution of the learned embedding vectors asymptotically decouples.
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
zwt233/NAFS
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First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
xihongyang1999/graphlearner
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During the training procedure, we notice the distinct optimization goals for training learnable augmentors and contrastive learning networks.
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
