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Node Clustering

11 papers with code ·

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SINE: Scalable Incomplete Network Embedding

ICDM 2018 benedekrozemberczki/karateclub

Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content.

COMMUNITY DETECTION LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION NODE CLUSTERING

Simple and Effective Graph Autoencoders with One-Hop Linear Models

21 Jan 2020deezer/linear_graph_autoencoders

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.

LINK PREDICTION NODE CLUSTERING

Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks

2 Oct 2019deezer/linear_graph_autoencoders

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.

LINK PREDICTION NODE CLUSTERING

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

5 Feb 2020cynricfu/MAGNN

A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING

Heterogeneous Deep Graph Infomax

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

The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.

GRAPH REPRESENTATION LEARNING HETEROGENEOUS NODE CLASSIFICATION NODE CLUSTERING

RWR-GAE: Random Walk Regularization for Graph Auto Encoders

12 Aug 2019MysteryVaibhav/DW-GAE

Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space.

GRAPH CLUSTERING LINK PREDICTION NODE CLUSTERING

Attributed Network Embedding via Subspace Discovery

14 Jan 2019daokunzhang/attri2vec

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.

LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION NODE CLUSTERING

Adaptive Graph Encoder for Attributed Graph Embedding

3 Jul 2020thunlp/AGE

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

GRAPH EMBEDDING LINK PREDICTION NODE CLUSTERING

Graph Neighborhood Attentive Pooling

ICLR 2020 zekarias-tilahun/GAP

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

COMMUNITY DETECTION LINK PREDICTION NODE CLUSTERING REPRESENTATION LEARNING