Abstract: Higher-order community detection has gained an increasing amount of attention in the past few years. In this tutorial, we will first introduce the definition of higher-order community detection, the existing approaches, and the advantages compared with the lower-order community detection approaches. Then, we will introduce some recent approaches for higher-order community detection, including the edge enhancement approach for overcoming the hypergraph fragmentation issue, and the harmonic motif modularity approach for multi-layer network community detection. Finally, we will briefly make a discussion on the challenges to be addressed in the future work.
Chang-Dong Wang received the Ph.D. degree in computer science in 2013 from Sun Yat-sen University, Guangzhou, China. He is a visiting student at University of Illinois at Chicago from Jan. 2012 to Nov. 2012. He joined Sun Yat-sen University in 2013 as an assistant professor with School of Mobile Information Engineering and now he is currently an associate professor with School of Data and Computer Science. His current research interests include machine learning and data mining. He has published over 100 scientific papers in international journals and conferences such as IEEE TPAMI, IEEE TKDE, IEEE TCYB, IEEE TNNLS, IEEE TSMC-C, IEEE/ACM TCBB, Pattern Recognition, KAIS, KDD, AAAI, IJCAI, CVPR, ICDM, CIKM, SDM, DASFAA and BIBM. His ICDM 2010 paper won the Honorable Mention for Best Research Paper Awards. He won 2012 Microsoft Research Fellowship Nomination Award. He was awarded 2015 Chinese Association for Artificial Intelligence (CAAI) Outstanding Dissertation. He is an Associate Editor in Journal of Artificial Intelligence Research (JAIR).