Community Search over Big Graphs

ISBN-13: 9783031007460 / Angielski / Miękka / 2019 / 208 str.

Xin Huang;Laks V.S. Lakshmanan;Jianliang Xu

Opis

Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area.

Spis treści

Acknowledgments.- Introduction.- Cohesive Subgraphs.- Cohesive Community Search.- Attributed Community Search.- Social Circle Analysis.- Geo-Social Group Search.- Datasets and Tools.- Further Readings and Future Directions.- Bibliography.- Authors' Biographies.

O autorze

Xin Huang is an Assistant Professor in the Department of Computer Science, Hong Kong Baptist University. He received his B.Eng. in computer science from Xiamen University in 2010, and Ph.D. in systems engineering and engineering management from Chinese University of Hong Kong in 2014. During 2015-2016, he worked as a postdoctoral research fellow at University of British Columbia. His research interests include graph data management, big graph mining and visualization, and social network analysis.

Laks V.S. Lakshmanan is a Professor of Computer Science at the University of British Columbia, Vancouver, BC, Canada. His research covers a wide spectrum of topics in data management and mining, including advanced data models for novel applications, OLAP and datawarehousing, data integration, data cleaning, semi-structured data and XML, information and social networks and social media, recommender systems, personalization, knowledge graphs,and fake news detection and mitigation. His publications appear in top-tier venues in these areas and he has served on the program and senior program committees of top-tier conferences in these areas, as well as on the editorial board of the VLDB Journal. He is an ACM Distinguished Scientist and currently serves on the editorial board of Distributed and Parallel Databases and Information Systems. His paper on concise representation and exploration of medical trajectories, in collaboration with colleagues, won the Best Research Paper Award at the IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018).

Jianliang Xu is a Professor in the Department of Computer Science, Hong Kong Baptist University. He received his B.Eng. in computer science and engineering from Zhejiang University, Hangzhou, China and his Ph.D. in computer science from Hong Kong University of Science and Technology. He held visiting positions at Pennsylvania State University and Fudan University. His research interests include data management, blockchain, mobile computing, and data security and privacy. He has published more than 200 technical papers in these areas, most of which appeared in leading journals and conferences including SIGMOD, VLDB, ICDE, TODS, TKDE, and VLDBJ. He has served as a program co-chair/vice chair for a number of major international conferences including IEEE ICDCS 2012, WAIM 2016, and IEEE MDM 2019. He has been an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) and Proceedings of the VLDB Endowment (PVLDB).