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Distributted algorithm for social graph influence maximization

hqsun0 - Posted on 02 December 2015

Project Description: 

Influence maximization (IM) is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. The majority of the literature focuses on designing more efficient IM algorithm. In our project, we propose a distributed algorithm, called CELF#, which can run in a more efficient way and without any accuracy compromise.

Researcher name: 
Sun Haiqi
Researcher position: 
MPhil student
Researcher department: 
Department of Computer Science
Researcher email: 
henrysun4@gmail.com
Researcher name: 
Qian yuqiu
Researcher position: 
PhD student
Researcher department: 
Department of Computer Science
Researcher email: 
qianyuqiu79@gmail.com
Researcher name: 
Zheng Yudian
Researcher position: 
PhD student
Researcher department: 
Department of Computer Science
Researcher email: 
zhydhkcws@gmail.com
Researcher name: 
Reynold Cheng
Researcher position: 
Associate Professor
Researcher department: 
Department of Computer Science
Researcher email: 
ckcheng@cs.hku.hk
Research Project Details
Project Duration: 
12/2015 to 12/2016
Project Significance: 
Motivated by applications such as viral marketing, per-sonalized recommendations, feed ranking, and the analysis of Twitter, the study of the propagation of influence exerted by users of an online social network on other users has received tremendous attention in the last years. However, the greatest challenge in these research is the efficiency of algorithm. In our work, we propose the algorithm CELF# which can work efficiently and distributively.
Results Achieved: 
A more efficient influence maximization algorithm which also provides approximation guarantee.
Remarks: 
High Efficiency and accuracy, also can work in distributed environment.