Graph is a prevalent data structure that enables many predictive tasks. How to engineer graph features is a fundamental question. Our concept is to go beyond nodes and edges, and explore richer structures (e.g., paths, subgraphs) for graph feature engineering. We call such richer structures as network functional blocks, because each structure serves as a network building block but with some different functionality. We use semantic proximity search as an example application to share our recent work on exploiting different granularities of network functional blocks. We show that network functional blocks are effective, and they can be useful for a wide range of applications.
Vincent is currently a tech lead in WeBank, China. He is also an adjunct Senior Research Scientist at Advanced Digital Sciences Center (ADSC), Singapore, and a research affiliate with University of Illinois at Urbana-Champaign, USA. He is the Associate Editor of Cognitive Computation. He has served as PCs in many leading data mining and artificial intelligence conferences such as KDD, IJCAI, AAAI, WWW, WSDM. He co-organized workshops, as well as contest, in ICDM 2007, IEEE CPSCOM 2013, ICDM 2018 and so on. He has published over 60 papers in the refereed conferences, journals and book chapters. He holds multiple patents and technical disclosures. He is a member of AAAI and ACM. He received his Ph.D. degree from the Hong Kong University of Science and Technology in 2011.