Efficient and effective influence maximization in social networks: A hybrid-approach
- Authors
- Ko, Yun-Yong; Cho, Kyung-Jae; Kim, Sang-Wook
- Issue Date
- Oct-2018
- Publisher
- ELSEVIER SCIENCE INC
- Keywords
- Social network; Information diffusion; Influence maximization; Monte-Carlo simulations
- Citation
- INFORMATION SCIENCES, v.465, pp.144 - 161
- Indexed
- SCIE
SCOPUS
- Journal Title
- INFORMATION SCIENCES
- Volume
- 465
- Start Page
- 144
- End Page
- 161
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/149233
- DOI
- 10.1016/j.ins.2018.07.003
- ISSN
- 0020-0255
- Abstract
- Influence Maximization (IM) is the problem of finding a seed set composed of k nodes that maximize their influence spread over a social network. Kempe et al. showed the problem to be NP-hard and proposed a greedy algorithm (referred to as SimpleGreedy) that guarantees 63% influence spread of its optimal solution. However, SimpleGreedy has two performance issues: at a micro level, it estimates the influence spread of a single node by running Monte-Carlo (MC) simulations that are fairly expensive; at a macro level, after selecting one seed at each step, it re-evaluates the influence spread of every node in a social network, leading to significant computational overhead. In this paper, we propose Hybrid-IM that addresses the two issues in both micro and macro levels by combining PB-IM (Path Based Influence Maximization) and CB-IM (Community Based Influence Maximization). Furthermore, we identify two technical issues that could improve the performance of Hybrid-IM more and propose two strategies to address those issues. Through extensive experiments with four real-world datasets, we show that Hybrid-IM achieves great improvement (up to 43 times) in performance over state-of-the-art methods and finds the seed set that provides the influence spread very close to that of the state-of-the-art methods.
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