报告题目：Holistic Influence Maximization Computing over Large Social Networks
Dr Jianxin Li is an A/Professor in the School of IT, Deakin University. His research interests include social computing, query processing and optimization, big graph data analytics, and educational data computation. He has published 70 high quality research papers in top international conferences and journals, including PVLDB, IEEE ICDE, ACM WWW, IEEE ICDM, EDBT, ACM CIKM, IEEE TKDE, and ACM WWW. His professional service can be identified by different roles in academic committees, e.g., the technical program committee members in ACM SIGMOD, PVLDB, AAAI, PAKDD, IEEE ICDM, and ACM CIKM; the journal reviewer in IEEE TKDE, ACM TKDD, WWW Journal and VLDB Journal; the proceeding chairs in DASFAA 2018, ADMA 2016 and ADC 2015; and the program committee chair in the International Workshop on Social Computing 2017 and 2018; the tutorial chair in the 26th International Conference on WWW 2017; and the guest editors in international journals, such as Computational Intelligence, IET Intelligent Transport Systems, Complexity, Data Science and Engineering.
Social media has become an emerging platform for organizations to broadcast their policies, for companies to advertise their products, and for people to propagate their opinions. In the social media data analytics, one of the most significant problems is the influence maximization problem. Given a social network and a campaign budget, the goal of influence maximization problem is to identify a set of influential users that are most likely to influence the maximum number of users in the social network. Meanwhile, the selected user set size is limited to the specified campaign budget. Thus, the small set of selected users can help campaign organizers to improve their marketing, branding, and product adoption in a profitable way.
At this seminar, Jianxin will first overview the latest research on the problem from different perspectives – topic-aware, location-aware, community-aware and target-aware. And then, the presenter will mainly introduce their recent research work – holistic influence maximization set problem by presenting the motivation of this novel problem, the new insights, the procedure of defining this problem in an easy way, the optimization techniques in solving such problem, and the experimental evaluations. This presentation is suitable to a broad audience who have interest in data science.