Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources
Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or mo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/2058670 |
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| _version_ | 1850168614263455744 |
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| author | Jun Long Lei Zhu Zhan Yang Chengyuan Zhang Xinpan Yuan |
| author_facet | Jun Long Lei Zhu Zhan Yang Chengyuan Zhang Xinpan Yuan |
| author_sort | Jun Long |
| collection | DOAJ |
| description | Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network. |
| format | Article |
| id | doaj-art-a0bcb2dd9c9b45c8b30d19fd20fdae48 |
| institution | OA Journals |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-a0bcb2dd9c9b45c8b30d19fd20fdae482025-08-20T02:20:55ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/20586702058670Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia SourcesJun Long0Lei Zhu1Zhan Yang2Chengyuan Zhang3Xinpan Yuan4School of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer, Hunan University of Technology, Zhuzhou 412007, ChinaVast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.http://dx.doi.org/10.1155/2018/2058670 |
| spellingShingle | Jun Long Lei Zhu Zhan Yang Chengyuan Zhang Xinpan Yuan Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources Advances in Multimedia |
| title | Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources |
| title_full | Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources |
| title_fullStr | Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources |
| title_full_unstemmed | Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources |
| title_short | Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources |
| title_sort | temporal activity path based character correction in heterogeneous social networks via multimedia sources |
| url | http://dx.doi.org/10.1155/2018/2058670 |
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