Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김상욱 | - |
dc.date.accessioned | 2019-12-09T19:27:39Z | - |
dc.date.available | 2019-12-09T19:27:39Z | - |
dc.date.issued | 2018-10 | - |
dc.identifier.citation | INFORMATION SCIENCES, v. 465, page. 144-161 | en_US |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.issn | 1872-6291 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0020025518305176?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/120414 | - |
dc.description.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. (C) 2018 Elsevier Inc. All rights reserved. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2017R1A2B3004581). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ELSEVIER SCIENCE INC | en_US |
dc.subject | Social network | en_US |
dc.subject | Information diffusion | en_US |
dc.subject | Influence maximization | en_US |
dc.subject | Monte-Carlo simulations | en_US |
dc.title | Efficient and effective influence maximization in social networks: A hybrid-approach | en_US |
dc.type | Article | en_US |
dc.relation.volume | 465 | - |
dc.identifier.doi | 10.1016/j.ins.2018.07.003 | - |
dc.relation.page | 144-161 | - |
dc.relation.journal | INFORMATION SCIENCES | - |
dc.contributor.googleauthor | Ko, Yun-Yong | - |
dc.contributor.googleauthor | Cho, Kyung-Jae | - |
dc.contributor.googleauthor | Kim, Sang-Wook | - |
dc.relation.code | 2018002510 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
dc.identifier.pid | wook | - |
dc.identifier.orcid | http://orcid.org/0000-0002-6345-9084 | - |
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