Node Clustering

60 papers with code • 19 benchmarks • 14 datasets

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Libraries

Use these libraries to find Node Clustering models and implementations

Most implemented papers

Attributed Network Embedding via Subspace Discovery

daokunzhang/attri2vec 14 Jan 2019

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

MysteryVaibhav/DW-GAE 12 Aug 2019

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 2 Oct 2019

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.

Is Performance of Scholars Correlated to Their Research Collaboration Patterns?

higd963/Collaboration2Vec Frontiers in Big Data 2019

Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles.

Heterogeneous Deep Graph Infomax

YuxiangRen/Heterogeneous-Deep-Graph-Infomax 19 Nov 2019

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 21 Jan 2020

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.

Graph Neighborhood Attentive Pooling

zekarias-tilahun/GAP 28 Jan 2020

Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs.

Gossip and Attend: Context-Sensitive Graph Representation Learning

zekarias-tilahun/goat 30 Mar 2020

In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models.

StructPool: Structured Graph Pooling via Conditional Random Fields

Nate1874/StructPool ICLR 2020

Learning high-level representations for graphs is of great importance for graph analysis tasks.

Adaptive Graph Encoder for Attributed Graph Embedding

thunlp/AGE 3 Jul 2020

Experimental results show that AGE consistently outperforms state-of-the-art graph embedding methods considerably on these tasks.