Node Clustering
Leaderboards
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Greatest papers with code
SINE: Scalable Incomplete Network Embedding
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
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.
Simple and Effective Graph Autoencoders with One-Hop Linear Models
• deezer/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.
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
• deezer/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.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
• cynricfu/MAGNN
• 
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.
SOTA for Node Clustering on IMDb
GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION 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.
#7 best model for
Heterogeneous Node Classification
on DBLP (PACT) 14k
GRAPH REPRESENTATION LEARNING HETEROGENEOUS NODE CLASSIFICATION NODE CLUSTERING
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.
#2 best model for
Graph Clustering
on Cora
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.
SOTA for Node Clustering on Facebook
LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION NODE CLUSTERING
Graph Neighborhood Attentive Pooling
ICLR 2020
• zekarias-tilahun/GAP
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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
Gossip and Attend: Context-Sensitive Graph Representation Learning
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.
COMMUNITY DETECTION GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLUSTERING