IMVis: Visual analytics for influence maximization algorithm evaluation in hypergraphs  

作　　者：Jin Xu Chaojian Zhang Ming Xie Xiuxiu Zhan Luwang Yan Yubo Tao Zhigeng Pan 

机构地区：[1]Hangzhou Normal University,Hangzhou,China [2]Zhejiang University,Hangzhou,China [3]Nanjing University of Information Science and Technology,Nanjing,China

出　　处：《Visual Informatics》2024年第2期13-26,共14页可视信息学（英文）

基　　金：Zhejiang Provincial Natural Science Foundation of China(LQ22F020017);National Natural Science Foundation of China(62302137);Open Project Program of the State Key Lab of CAD&CG of Zhejiang University(A2104).

摘　　要：Influence maximization(IM)algorithms play a significant role in hypergraph analysis tasks,such as epidemic control analysis,viral marketing,and social influence analysis,and various IM algorithms have been proposed.The main challenge lies in IM algorithm evaluation,due to the complexity and diversity of the spreading processes of different IM algorithms in different hypergraphs.Existing evaluation methods mainly leverage statistical metrics,such as influence spread,to quantify overall performance,but do not fully unravel spreading characteristics and patterns.In this paper,we propose an exploratory visual analytics system,IMVis,to assist users in exploring and evaluating IM algorithms at the overview,pattern,and node levels.A spreading pattern mining method is first proposed to characterize spreading processes and extract important spreading patterns to facilitate efficient analysis and comparison of IM algorithms.Novel visualization glyphs are designed to comprehensively reveal both temporal and structural features of IM algorithms’spreading processes in hypergraphs at multiple levels.The effectiveness and usefulness of IMVis are demonstrated through two case studies and expert interviews.

关 键 词：Influence maximization evaluation Comparative visual analysis Visual analytics 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]