2019/09/29 by Carl Yang, Yang, Carl, Yichen Feng +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Artificial intelligence #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Computer science #Embedding #FOS: Computer and information sciences #Knowledge graph #Leverage (statistics) #Machine Learning (cs.LG) #Social and Information Networks (cs.SI) #Theoretical computer science #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.1910.00004
published in arXiv (Cornell University) (Cornell University) · 10 pages, published at ICDM 2018
arxiv created 2019/09/29 · openalex publication_date 2019/09/29 · arxiv updated 2019/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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 ℓ2,1-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.