Node Clustering
60 papers with code • 19 benchmarks • 14 datasets
Libraries
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Most implemented papers
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
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?
Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles.
Heterogeneous Deep Graph Infomax
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
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
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
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
Learning high-level representations for graphs is of great importance for graph analysis tasks.
Adaptive Graph Encoder for Attributed Graph Embedding
Experimental results show that AGE consistently outperforms state-of-the-art graph embedding methods considerably on these tasks.