报告主题：Identifying influential nodes from local and global perspectives in complex networks
报告时间：2021年11月5日 上午8:30
报告地点：计算机楼313
报告1摘要:
Complex networks are powerful methods for representing and studying the interactions among objects in the real world, it is an abstraction of complex systems. The topology of complex networks determines their node influence. Consistent research proposed various approaches like local-structure-based methods, e.g., degree centrality, PageRank, etc., and global-structure-based methods, e.g., betweenness, closeness centrality, etc., to evaluate the concerned nodes. Though their performance is amazingly well, these methods have undergone some intrinsic limitations. For instance, local-structure-based methods lose some sort of global information and global-structure-based methods are too complicated to measure the important nodes, particularly in networks where sizes become large. To tackle these challenges, we propose a Local-and-Global Centrality measuring algorithm to identify the vital nodes through handling local as well as global topological aspects of a network simultaneously. In order to assess the performance of the proposed algorithm with respect to the state-of-the-art methodologies, we performed experiments through the proposed approach, Betweenness (BNC), Closeness (CNC), Gravity (GIC), Page-Rank (PRC), Eigenvector (EVC), Global and Local Structure (GLS), Global Structure Model (GSM), and Profit-leader (PLC) methods on differently sized real-world networks. Our experiments disclose that the proposed approach outperformed many of the compared techniques.
报告二摘要：
The method proposed above has limitations. For example, it only considers the degree of a node in a network, but the position of the node in some complex networks is also very important (the importance of a node also depends on its position in a network and the number of neighbors it can influence.). Therefore, in order to solve this problem, we have proposed an extended version of LGC called LGC+ to deal with this problem.
报告大纲：
Ø Introduction and Background of complex networks (Influential nodes identification)
Ø Our main Contribution
Ø Related Work regarding
Ø Proposed Methodology
Ø Experimental Results
Ø Conclusions and Future Recommendation
报告人简介：
Aman Ullah has completed his BS degree in computer science from Gomal University DIkhan Pakistan and MS degree in software engineering from Abasyn University Peshawar Pakistan in 2013 and 2017 respectively. He is currently pursuing PhD degree with School of Computer Science and Engineering, Central South University, Changsha, China. He has published several JCR index by SCI research papers in reputed journals. His research interests include Complex Networks and Software Engineering.
