dc.contributor.author | Gao, J | |
dc.contributor.author | Hao, F | |
dc.contributor.author | Min, G | |
dc.contributor.author | Cai, Z | |
dc.date.accessioned | 2021-07-14T12:36:14Z | |
dc.date.issued | 2021-07-11 | |
dc.description.abstract | The increasing of group polarization on social media seriously impacts on the health of public discourse and information dissemination. At present, detecting polarized structures in signed networks is well-motivated for studying the group polarization on social media. However, most studies restricted the number of polarized structures to only two, while neglecting the real-world scenario where signed networks consist of multiple polarized structures, that is an unreasonable assumption. To conquer the limitations of the existing work, in this paper, we present a novel cohesive subgraph model based on structural clusterable theory, named maximal multipolarized clique (MMC), which can be partitioned into k polarized subcliques such that the edges in subcliques are positive and the edges between subcliques are negative. This paper formulates the problem of Maximal Multipolarized Cliques Search (MMCS) in signed networks which is proved to be NP-hard. To address this problem, we first devise powerful pruning rules to reduce the signed network significantly and further develop an efficient algorithm to search all maximal multipolarized cliques in the reduced signed network. The experimental results on real-world signed networks demonstrate the efficiency and effectiveness of our algorithm. | en_GB |
dc.description.sponsorship | Fundamental Research Funds for the Central Universities | en_GB |
dc.identifier.citation | SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. Online, 11 - 15 July 2021, pp. 2227 – 2231 | en_GB |
dc.identifier.doi | 10.1145/3404835.3463014 | |
dc.identifier.grantnumber | GK202103080 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/126408 | |
dc.language.iso | en | en_GB |
dc.publisher | Association for Computing Machinery (ACM) | en_GB |
dc.rights | © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. | en_GB |
dc.subject | Group Polarization | en_GB |
dc.subject | Signed Networks | en_GB |
dc.subject | Maximal Multipolarized Clique | en_GB |
dc.title | Maximal Multipolarized Cliques Search in Signed Networks | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2021-07-14T12:36:14Z | |
dc.identifier.isbn | 9781450380379 | |
dc.description | This is the author accepted manuscript. The final version is available from ACM via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2021-04-14 | |
rioxxterms.funder | National Natural Science Foundation of China | en_GB |
rioxxterms.funder | European Union Horizon 2020 | en_GB |
rioxxterms.identifier.project | 61702317 | en_GB |
rioxxterms.identifier.project | 840922 | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2021-07-11 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2021-07-14T12:32:06Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2021-07-14T12:36:28Z | |
refterms.panel | B | en_GB |
rioxxterms.funder.project | b46c2fb1-96fc-4e6d-be34-0693ccc61afa | en_GB |
rioxxterms.funder.project | e4e40680-5a3e-4e7d-be59-2b310fb34b18 | en_GB |