Interactive Activities Initiation through Retrieving Hidden Social Information Networks

被引:0
|
作者
Song, Yulong [1 ]
Fu, Bin [2 ]
Guo, Jianxiong [3 ]
Gao, Xiaofeng [1 ]
机构
[1] Shanghai Jiao Tong Univ, MoE Key Lab Artificial Intelligence, Dept Comp Sci & Engn, Shanghai, Peoples R China
[2] Univ Texas Rio Grande Valley, Dept Comp Sci, Edinburg, TX USA
[3] Beijing Normal Univ, Adv Inst Nat Sci, Zhuhai, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Social Network; Vertex Cover; Interactive Computation Model; Approximation Algorithm; DIFFUSION;
D O I
10.1109/ICDM58522.2023.00063
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The rise of social platforms based on online social networks has greatly enriched people's lives, resulting in various applications. Traditional research mainly focuses on users but pays less attention to the edges between users, and they all assume the topology of social networks is known in advance. Indeed, obtaining the network topology is challenging because of privacy protection and business competition. In this paper, we propose an activity initiation problem inspired by real business applications, such as Pinduoduo and Tencent, where each edge can be abstracted as an activity in which both ends (users) of the edge participate together, and the edge can be initiated by one of them. At this time, we hope to select as few users as possible to initiate activities and make the users of the whole network participate together. This problem can be reduced to the classic vertex cover problem, but the network information is hidden by social platforms as much as possible. To address this challenge, we put forward a solver-detector model. In each round of interaction, the solver uses a detector to obtain a small amount of edge information and achieves vertex coverage. This is a model in which a solver has limited access to input, but still gives a 2 -approximation that is as good as the conventional model. Finally, we can cover the whole network with very few edge samples, which is a brand-new research perspective.
引用
收藏
页码:538 / 547
页数:10
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