日本データベース学会の皆様、 以下の講演会が5月30日(火)・東大生研にて行われます。 ふるってご参加ください。 　　　　　　　　　　　　　　　　　東京大学生研 　　　　　　　　　　　　　　　　　中野美由紀 　☆☆☆　5月30日　講演会のご案内　　　☆☆☆ 主催　最先端研究開発支援プログラム（FIRST）: 超巨大データベース時代に 　　　向けた最高速データベースエンジンの開発と当該エンジンを核とする 　　　戦略的社会サービスの実証・評価 後援　電子情報通信学会　データ工学研究専門委員会 日時　5月30日（水）　午後6時〜7時 場所　東京大学生産技術研究所　E棟　5階　会議室Ｂ（Ew-502） http://www.iis.u-tokyo.ac.jp/map/index.html Speaker : Prof. Ling Liu (Georgia Institute of Technology) Title : Social Influence based Approach to Graph Mining 参加費　無料 皆様のご参加をお待ちしております。 FIRST :　研究代表者　　　　　　　喜連川　優 電子情報通信学会データ工学研究会　委員長 　　　　　　　　　　　　　　　　　　　　　　　　　　　　 中野　美由紀 　　　　　　　　　　 ----------------------------------------------------------------- To: first_lecture@tkl.iis.u-tokyo.ac.jp 　講演会　参加申し込み 5月30日（水）の講演会に参加 ・名前　　　 ・ご所属 ------------------------------------------------------------------ プログラム Title : Social Influence based Approach to Graph Mining Abstract: There is a growing interest in clustering social network graph by considering both social interactions (such as friendship among people) within a single collaboration network (self influence) and the interactions between the collaboration network and other information networks (co-influence). Social influence analysis has great potential for understanding the ways in which information, ideas and experiences are spread or diffused across a social network. This talk presents an innovative social influence based graph clustering framework with three unique features. First, we define the concept of influence based distance in terms of propagating heat diffusion kernel on both collaboration graph and its associated influence graphs. Second, we introduce a weight function with an iterative update method to integrate vertex closeness scores on multiple social influence graphs through weight assignments. Third, we design an iterative learning algorithm SI-Cluster for social influence based graph clustering. It partitions a large collaboration network into k clusters by continuously quantify and adjusts the weighted contributions from different influence classes in the co-influence model as we recursively refine the clusters until reaching convergence. To make the SI-Cluster algorithm converge to a global maximum as faster as possible, we transformed a sophisticated nonlinear fractional programming problem of multiple weights into a straightforward nonlinear parametric programming problem of single variable. Experimental results on three real graphs demonstrate that our SI-Cluster approach not only achieves a very good balance between collaboration and influence similarities but also scales well for clustering large graphs in terms of time complexity while meeting high density and low entropy guarantee. Bio: Ling Liu is a full Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale data intensive systems. Prof. Ling Liu is an internationally recognized expert in the areas of Database Systems, Distributed Computing, Internet Systems, and Service oriented computing. She has published over 300 international journal and conference articles and is a co-recipient of the best paper award of ICDCS 2003, the best paper award of WWW 2004, 2005 Pat Goldberg Memorial Best Paper Award, and the best data engineering paper of 2008 International Conference on Software Engineering and Data Engineering. In 2012, Prof. Liu received an IEEE Computer Society Technical Achievement Award and an Outstanding Doctoral Thesis Advisor award for producing outstanding PhD students and her service and dedication to Georgia Institute of Technology. Prof. Liu served as general chair and PC chairs of several IEEE and ACM conferences in data engineering and distributed computing fields and served on editorial board of over a dozen international journals. Currently Prof. Liu is on the editorial board of Distributed and Parallel Databases (Springer), Journal of Parallel and Distributed Computing (JPDC), IEEE Transactions on Service Computing (TSC), and ACM Transactions on Web (TWEB). Dr. Liu’s current research is primarily sponsored by NSF, IBM, and Intel. -- ----------------------------------------------------------------------- 中野　美由紀 東京大学　生産技術研究所　戦略情報融合国際研究センタ Miyuki NAKANO Institute of Industrial Science, Univ. of Tokyo 　　　　　　　　Center for Information Fusion miyuki@tkl.iis.u-tokyo.ac.jp