报告题目：  

Fast mining the community structure based on leader location and dynamical system  

报告摘要：  

Mining communities in networks is valuable in analyzing, designing, and optimizing many natural and engineering complex systems, e.g. protein networks, power grid, and transportation systems. Most of the existing techniques view the community mining problem as an optimization problem based on a given quality function(e.g., modularity), however none of them are grounded with a systematic theory to identify the central nodes in the network. Moreover, how to reconcile the mining efficiency and the community quality still remains an open problem. Here, we attempt to address the above challenges by using a discrete-time dynamical system to describe the dynamical assignment of community membership; and formulate the serval conditions to guarantee the convergence of each node’s dynamic trajectory, by which the hierarchical community structure of the network can be revealed. The proposed algorithm is highly efficient: the computational complexity analysis shows that the execution time is nearly linearly dependent on the number of nodes in sparse networks. Finally the applications of the algorithm to a set of synthetic benchmark networks and real-world networks verify the algorithmic performance.。

报告时间：2017年7月10日（本周一）下午3:30-5:00

报告地点：7-208

 

李慧嘉博士简介：  

李慧嘉博士，博士毕业于中国科学院数学与系统科学研究院，现任中央财经大学管理科学与工程学院副教授。李慧嘉博士一直从事网络科学相关领域的研究，目前已在包括 IEEE Transactions on Knowledge and Data Engineering、Physical Review E、中国科学：数学、计算机学报等国内外期刊发表文章40余篇，现为多个国内外期刊的编委和审稿专家。