Chen Chen received her PhD degree from Arizona State University in 2019, MSc degree from New York University in 2013, and BEng degree from Beihang University in 2011, all in computer science. She is currently a software engineer in Google LLC., working on the action graph in Google Assistant. Her research interests include large scale data mining in graphs and real-world network analysis. Her works have been recognized as Bests of SDM 2015, Bests of KDD 2016.
Networks naturally appear in many high-impact applications, ranging from epidemic studies, social network mining to infrastructure analysis. The simplest model of networks is single-layered networks, where the nodes are from the same domain and the links are of the same type. However, as the world is becoming increasingly connected and coupled, nodes from different application domains tend to be interdependent on each other, forming a more complex network model called multi-layered networks. Among the various aspects of network studies, network connectivity is the one that plays an important role in a myriad of applications (e.g. information dissemination, robustness analysis, community detection, etc.).
My research aims to study connectivity measures, inference, and optimization problems in complex networks. Specifically, we have proposed a unified connectivity measure model to unveil the commonality among existing connectivity measures. For the connectivity inference problems, we have developed an effective network inference method and connectivity tracking framework to address the incompleteness and evolution of the network. Last, to optimize the connectivity of the network, we have thoroughly studied the theoretical hardness of the optimization problem and built a generalized optimization framework to tackle the connectivity minimization/maximization problems on both single-layered and multi-layered networks.