A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
Rayyan Ahmad Khan,
Martin Kleinsteuber
Abstract:HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification. In this work, we propose VaCA-HINE for joint learning of cluster embeddings as well as cluster-aware HIN embedding. We assume that the connected nodes are highly likely to fall in the same cluster, and adopt a variational approach to preserve… Show more
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