Yuchen Li, Ju Fan, Yanhao Wang,et al published a paper at TKDE 2018

Feb 20th,2018

Yuchen Li, Ju Fan, Yanhao Wang and Kian-Lee Tan published a paper named “Influence Maximization on Social Graphs: A Survey” at IEEE Transaction on Knowledge and Data Engineering (TKDE) on February 20th,2018.

In this paper, they survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects (1) a review of well-accepted diffusion models that capture information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, they then outline the key challenges and research directions to expand the boundary of IM research.

IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. The scope includes the knowledge and data engineering aspects of computer science, artificial intelligence, electrical engineering, computer engineering, and other appropriate fields. This Transactions provides an international and interdisciplinary forum to communicate results of new developments in knowledge and data engineering and the feasibility studies of these ideas in hardware and software.