LCHI: multiple, overlapping local communities
Pages 203 - 210
Abstract
Local community finding algorithms are helpful for finding communities around a seed node especially when the network is large and a global method is too slow. Most local methods find only a single community or are required to be run several times over different seed nodes to create multiple communities. In this paper, we present a new algorithm, LCHI that finds multiple, overlapping communities around a single node. Examples and analyses are presented support the effectiveness of LCHI.
References
[1]
James P Bagrow and Erik M Bollt. 2005. Local method for detecting communities. Physical Review E 72, 4 (2005), 046108.
[2]
Aaron Clauset. 2005. Finding local community structure in networks. Physical review E 72, 2 (2005), 026132.
[3]
G. Flake, K. Tsioutsiouliklis, and R. Tarjan. 2002. Graph clustering techniques based on minimum cut trees. Technical Report. NEC, Princeton, NJ.
[4]
M. Girvan and M. E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 12 (2002), 7821--7826.
[5]
Andrea Lancichinetti, Santo Fortunato, and János Kertész. 2009. Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11, 3 (2009), 033015.
[6]
Costas Panagiotakis, Harris Papadakis, and Paraskevi Fragopoulou. 2014. Flow-pro: A flow propagation method for single community detection. In Consumer Communications and Networking Conference (CCNC), 2014 IEEE 11th. IEEE, 17--22.
[7]
Symeon Papadopoulos, Andre Skusa, Athena Vakali, Yiannis Kompatsiaris, and Nadine Wagner. 2009. Bridge bounding: A local approach for efficient community discovery in complex networks. arXiv preprint arXiv:0902.0871 (2009).
[8]
Mason A Porter, Jukka-Pekka Onnela, and Peter J Mucha. 2009. Communities in networks. Notices of the AMS 56, 9 (2009), 1082--1097.
[9]
Jerry Scripps and Christian Trefftz. 2013. Community finding within the community set space. In Proceedings of the 7th Workshop on Social Network Mining and Analysis. ACM, 13.
[10]
Jierui Xie, Stephen Kelley, and Boleslaw K. Szymanski. 2011. Overlapping Community Detection in Networks: the State of the Art and Comparative Study. CoRR abs/1110.5813 (2011). http://arxiv.org/abs/1110.5813
[11]
Wayne W Zachary. 1977. An information flow model for conflict and fission in small groups. Journal of anthropological research 33, 4 (1977), 452--473.
Index Terms
- LCHI: multiple, overlapping local communities
Recommendations
Like-minded communities: bringing the familiarity and similarity together
Community detection in social networks is a well-studied problem. A community in social network is commonly defined as a group of people whose interactions within the group are more than outside the group. It is believed that people's behavior can be ...
Community finding within the community set space
SNAKDD '13: Proceedings of the 7th Workshop on Social Network Mining and AnalysisCommunity finding algorithms strive to find communities that have a higher connectivity within the communities than between them. Recently a framework called the community set space was introduced which provided a way to measure the quality of community ...
Using dynamic knowledge graphs to detect emerging communities of knowledge
Highlights- Methodology to represent dynamic knowledge and explore emerging communities from corpora.
- Analysis of communities and centrality over time is conducted, along with a proposed method for generating high-impact relations.
- A dynamic ...
AbstractKnowledge graphs represent relationships between entities. These graphs can take dynamic forms to trace changes along time through text models and further used by reasoning systems with the intent to answer queries. In this research we explore ...
Comments
Information & Contributors
Information
Published In
August 2017
1284 pages
ISBN:9781450349512
DOI:10.1145/3106426
- Conference Chair:
- Amit Sheth,
- General Chairs:
- Axel Ngonga,
- yin Wang,
- Elizabeth Chang,
- Dominik Ślęzak,
- Bogdan Franczyk,
- Program Chairs:
- Rainer Alt,
- Xiaohui Tao
Copyright © 2017 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
- Web Intelligence Consortium
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 August 2017
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
WI '17
Sponsor:
- SIGAI
- TCII
Acceptance Rates
WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
Overall Acceptance Rate 118 of 178 submissions, 66%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 52Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 18 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in