Node Clustering
33 papers with code • 7 benchmarks • 7 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.
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.
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.
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
deezer/linear_graph_autoencoders
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Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.
Heterogeneous Deep Graph Infomax
YuxiangRen/Heterogeneous-Deep-Graph-Infomax
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The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Simple and Effective Graph Autoencoders with One-Hop Linear Models
deezer/linear_graph_autoencoders
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Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.
