Node Clustering
46 papers with code • 7 benchmarks • 8 datasets
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Most implemented papers
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.
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or groups in networks.
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.
Search Efficient Binary Network Embedding
In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network.
Attributed Network Embedding via Subspace Discovery
In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.
RWR-GAE: Random Walk Regularization for Graph Auto Encoders
Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space.
