GraphReformCD: Graph Reformulation for Effective Community Detection in Real-World Graphs
Pages 180 - 183
Abstract
Community detection, one of the most important tools for graph analysis, finds groups of strongly connected nodes in a graph. However, community detection may suffer from misleading information in a graph, such as a nontrivial number of inter-community edges or an insufficient number of intra-community edges. In this paper, we propose GraphReformCD that reformulates a given graph into a new graph in such a way that community detection can be conducted more accurately. For the reformulation, it builds a k-nearest neighbor graph that gives a node k opportunities to connect itself to those nodes that are likely to belong to the same community together with the node. To find the nodes that belong to the same community, it employs the structural similarities such as Jaccard index and SimRank. To validate the effectiveness of our GraphReformCD, we perform extensive experiments with six real-world and four synthetic graphs. The results show that our GraphReformCD enables state-of-the-art methods to improve their accuracy significantly up to 40.6% in community detection.
References
[1]
Vincent D. Blondel 2008. Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10(2008), P10008.
[2]
Leon Danon 2005. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment 2005, 09(2005), P09008.
[3]
Aditya Grover and Jure Leskovec. 2016. Node2vec: Scalable Feature Learning for Networks. In Proc. of ACM SIGKDD. 855–864.
[4]
Jiawei Han, Micheline Kamber, and Jian Pei. 2011. Data Mining Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems 5, 4 (2011), 83–124.
[5]
Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi. 2008. Benchmark Graphs for Testing Community Detection Algorithms. Physical Review E 78(2008), 046110. Issue 4.
[6]
Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.
[7]
Sungsu Lim, Junghoon Kim, and Jae-Gil Lee. 2016. BlackHole: Robust Community Detection Inspired by Graph Drawing. In Proc. of IEEE ICDE. 25–36.
[8]
Mark E. J. Newman. 2006. Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences 103, 23(2006), 8577–8582.
[9]
Mark E. J. Newman. 2013. Network Data. http://www-personal.umich.edu/~mejn/netdata/.
[10]
Martin Rosvall and Carl T. Bergstrom. 2008. Maps of Random Walks on Complex Networks Reveal Community Structure. Proceedings of the National Academy of Sciences 105, 4 (2008), 1118–1123.
[11]
Divya Sardana and Raj Bhatnagar. 2014. Graph Clustering using Mutual K-Nearest Neighbors. In International Conference on Active Media Technology. 35–48.
[12]
Jian Tang 2015. LINE: Large-Scale Information Network Embedding. In Proc. of WWW. 1067–1077.
Index Terms
- GraphReformCD: Graph Reformulation for Effective Community Detection in Real-World Graphs
Recommendations
High quality, scalable and parallel community detection for large real graphs
WWW '14: Proceedings of the 23rd international conference on World wide webCommunity detection has arisen as one of the most relevant topics in the field of graph mining, principally for its applications in domains such as social or biological networks analysis. Different community detection algorithms have been proposed ...
Hidden community detection in social networks
Propose a new conception of hidden community for network analysis.Provide a meta-approach called HICODE for finding the hidden communities.Several weakening methods are proposed to reduce the impact of the detected structure.The framework works ...
Community-Affiliation Graph Model for Overlapping Network Community Detection
ICDM '12: Proceedings of the 2012 IEEE 12th International Conference on Data MiningOne of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. ...
Comments
Information & Contributors
Information
Published In
April 2022
1338 pages
ISBN:9781450391306
DOI:10.1145/3487553
Copyright © 2022 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
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 August 2022
Check for updates
Author Tags
Qualifiers
- Poster
- Research
- Refereed limited
Funding Sources
- Institute of Information & Communications Technology Planning & Evaluation
- National Research Foundation of Korea
Conference
WWW '22
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 141Total Downloads
- Downloads (Last 12 months)34
- Downloads (Last 6 weeks)5
Reflects downloads up to 07 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format