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Found 7 papers, 3 papers with code
Barlow Graph Auto-Encoder for Unsupervised Network Embedding
Motivated by this, we propose Barlow Graph Auto-Encoder, a simple yet effective architecture for learning network embedding.
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification.
Variational Embeddings for Community Detection and Node Representation
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
VECoDeR - Variational Embeddings for Community Detection and Node Representation
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
Metapath- and Entity-aware Graph Neural Network for Recommendation
In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures.
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Epitomic Variational Graph Autoencoder
Variational autoencoder (VAE) is a widely used generative model for learning latent representations.
Extended Affinity Propagation: Global Discovery and Local Insights
We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities.
