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In this work, we propose to study the utility of different meta-graphs, aswell as how to simultaneously leverage multiple meta-graphs for HIN embeddingin an unsupervised manner. Motivated by prolific research on homogeneousnetworks, especially spectral graph theory, we firstly conduct a systematicempirical study on the spectrum and embedding quality of different meta-graphson multiple HINs, which leads to an efficient method of meta-graph assessment.It also helps us to gain valuable insight into the higher-order organization ofHINs and indicates a practical way of selecting useful embedding dimensions.Further, we explore the challenges of combining multiple meta-graphs to capturethe multi-dimensional semantics in HIN through reasoning from mathematicalgeometry and arrive at an embedding compression method of autoencoder with$\ell_{2,1}$-loss, which finds the most informative meta-graphs and embeddingsin an end-to-end unsupervised manner. Finally, empirical analysis suggests aunified workflow to close the gap between our meta-graph assessment andcombination methods. To the best of our knowledge, this is the first researcheffort to provide rich theoretical and empirical analyses on the utility ofmeta-graphs and their combinations, especially regarding HIN embedding.Extensive experimental comparisons with various state-of-the-art neural networkbased embedding methods on multiple real-world HINs demonstrate theeffectiveness and efficiency of our framework in finding useful meta-graphs andgenerating high-quality HIN embeddings.

Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights