Networks naturally appear in many high-impact domains, ranging from social network analysis to disease dissemination studies to infrastructure system design. Within network studies, network connectivity plays an important role in a myriad of applications. The diversity of application areas has spurred numerous connectivity measures, each designed for some specific tasks. Depending on the complexity of connectivity measures, the computational cost of calculating the connectivity score can vary significantly. Moreover, the complexity of the connectivity would predominantly affect the hardness of connectivity optimization, which is a fundamental problem for network connectivity studies. This book presents a thorough study in network connectivity, including its concepts, computation, and optimization. Specifically, a unified connectivity measure model will be introduced to unveil the commonality among existing connectivity measures. For the connectivity computation aspect, the authors introduce the connectivity tracking problems and present several effective connectivity inference frameworks under different network settings. Taking the connectivity optimization perspective, the book analyzes the problem theoretically and introduces an approximation framework to effectively optimize the network connectivity. Lastly, the book discusses the new research frontiers and directions to explore for network connectivity studies. This book is an accessible introduction to the study of connectivity in complex networks. It is essential reading for advanced undergraduates, Ph.D. students, as well as researchers and practitioners who are interested in graph mining, data mining, and machine learning.
Chen Chen is currently a Research Assistant Professor at the University of Virginia. Before joining the University of Virginia, she was a software engineer at Google working on personalized recommendations for Google Assistant. Chen received her Ph.D. from Arizona State University. Her research has focused on the connectivity of complex networks, which has been applied to address pressing challenges in various high-impact domains, including social media, bioinformatics, recommendation, and critical infrastructure systems. Her research has appeared in top-tier conferences (including KDD, ICDM, SDM, WSDM, and DASFAA), and prestigious journals (including IEEE TKDE, ACM TKDD, and SIAM SAM). Chen has received several awards, including Bests of SDM’15, Bests of KDD’16, Rising Star in EECS’19, and Outstanding Reviewer of WSDM’21.
Hanghang Tong is currently an associate professor at the Department of Computer Science at University of Illinois at Urbana-Champaign. Before that, he was an associate professor at the School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. He received his M.Sc. and Ph.D. from Carnegie Mellon University in 2008 and 2009, respectively, both in Machine Learning. His research interest is in large-scale data mining for graphs and multimedia. He has received several awards, including SDM/IBM Early Career Data Mining Research award (2018), NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper award (2015), four best paper awards (TUP’14, CIKM’12, SDM’08, ICDM’06), seven “bests of conference,” one best demo, honorable mention (SIGMOD’17), and one best demo candidate, second place (CIKM’17). He has published over 100 refereed articles. He is the Editor-in-Chief of SIGKDD Explorations (ACM), an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Knowledge and Information Systems (Springer) and Neurocomputing Journal (Elsevier). He has served as a program committee member in multiple data mining, database, and artificial intelligence venues (e.g.,SIGKDD, SIGMOD, AAAI, WWW, CIKM, etc.).
