Who Should Deserve Investment? Attractive Individual and Group Search in Dynamic Information Networks

Xinrui Wang, Hong Gao, Zhipeng Cai, Jianzhong Li

Abstract: Analyzing dynamic information networks, which contain evolving objects and links, to meet users’ various needs has attracted much attention in recent years. For sales companies, recruiting staff who are socialites would help to increase sales volume since such staff often sell more. For universities, employing active collaborators who are productive and well connected to many different scholars over time could bring many benefits to their development. However, no previous work has focused on the discovery of socialites and active collaborators who are worthy of investment in reality. In this paper, we advocate a new concept of <i>attractive individuals</i> to model such special objects. We also introduce the concept of <i>attractive groups</i> to represent groups of well-connected attractive individuals. We analyze the complexity of the attractive individual and group search problems. A time and space efficient algorithm is presented to detect attractive individuals. Furthermore, three algorithms are respectively proposed to find top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> representative attractive groups. Experiments on 6 real-world datasets show high performance of our methods and the significance of attractive individuals and groups in reality.