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Personalized Influential Community Search in Large Networks: A K-ECC-Based Model
- Source :
- Discrete Dynamics in Nature and Society, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Wiley, 2021.
-
Abstract
- Graphs have been widely used to model the complex relationships among entities. Community search is a fundamental problem in graph analysis. It aims to identify cohesive subgraphs or communities that contain the given query vertices. In social networks, a user is usually associated with a weight denoting its influence. Recently, some research is conducted to detect influential communities. However, there is a lack of research that can support personalized requirement. In this study, we propose a novel problem, named personalized influential k-ECC (PIKE) search, which leverages the k-ECC model to measure the cohesiveness of subgraphs and tries to find the influential community for a set of query vertices. To solve the problem, a baseline method is first proposed. To scale for large networks, a dichotomy-based algorithm is developed. To further speed up the computation and meet the online requirement, we develop an index-based algorithm. Finally, extensive experiments are conducted on 6 real-world social networks to evaluate the performance of proposed techniques. Compared with the baseline method, the index-based approach can achieve up to 7 orders of magnitude speedup.
- Subjects :
- Mathematics
QA1-939
Subjects
Details
- Language :
- English
- ISSN :
- 1607887X
- Volume :
- 2021
- Database :
- Directory of Open Access Journals
- Journal :
- Discrete Dynamics in Nature and Society
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0448558855446e0b51080f888fec8cf
- Document Type :
- article
- Full Text :
- https://doi.org/10.1155/2021/5363946