TY - GEN
T1 - Influence maximization meets efficiency and effectiveness
T2 - 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
AU - Tang, Jing
AU - Tang, Xueyan
AU - Yuan, Junsong
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/7/31
Y1 - 2017/7/31
N2 - Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.
AB - Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.
KW - Hop-based influence estimation
KW - Influence maximization
KW - Online social networks
UR - https://www.scopus.com/pages/publications/85040238836
U2 - 10.1145/3110025.3110041
DO - 10.1145/3110025.3110041
M3 - Conference contribution
AN - SCOPUS:85040238836
T3 - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
SP - 64
EP - 71
BT - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
A2 - Diesner, Jana
A2 - Ferrari, Elena
A2 - Xu, Guandong
PB - Association for Computing Machinery, Inc
Y2 - 31 July 2017 through 3 August 2017
ER -