Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
This work addresses the challenge of leveraging multiple meta-graphs for HIN embedding, which is important for researchers in network analysis and machine learning, though it appears incremental as it builds on existing spectral graph theory and HIN methods.
The paper tackles the problem of selecting and combining meta-graphs for heterogeneous information network (HIN) embedding by proposing an unsupervised framework that includes meta-graph assessment and an autoencoder-based compression method, resulting in effective and efficient high-quality embeddings as demonstrated in experiments.
In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner. Motivated by prolific research on homogeneous networks, especially spectral graph theory, we firstly conduct a systematic empirical study on the spectrum and embedding quality of different meta-graphs on 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 of HINs and indicates a practical way of selecting useful embedding dimensions. Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with $\ell_{2,1}$-loss, which finds the most informative meta-graphs and embeddings in an end-to-end unsupervised manner. Finally, empirical analysis suggests a unified workflow to close the gap between our meta-graph assessment and combination methods. To the best of our knowledge, this is the first research effort to provide rich theoretical and empirical analyses on the utility of meta-graphs and their combinations, especially regarding HIN embedding. Extensive experimental comparisons with various state-of-the-art neural network based embedding methods on multiple real-world HINs demonstrate the effectiveness and efficiency of our framework in finding useful meta-graphs and generating high-quality HIN embeddings.