skip to main content
10.1145/3097983.3097995acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

On Finding Socially Tenuous Groups for Online Social Networks

Published: 04 August 2017 Publication History

Abstract

Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph-theoretical approaches for solving MkTG on general graphs effectively and efficiently. Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.

Supplementary Material

MP4 File (shen_online_social_networks.mp4)

References

[1]
S. Seidman. Network structure and minimum degree. Social Networks, 1983.
[2]
J. Cohen. Trusses: cohesive subgraphs for social network analysis, 2008.
[3]
V. Batagelj and M. Zaversnik. Generalized cores. arXiv:cs/0202039, 2002.
[4]
A. Goldberg. Finding a maximum density subgraph. Technical Report, 1984.
[5]
D. Berlowitz, S. Cohen, B. Kimelfeld. Efficient enumeration of maximal k-plexes. SIGMOD, 2015.
[6]
K. Reid. Social work with groups, 1997.
[7]
J. Qiu, Z. Lin, C. Tang, and S. Qiao. Discovering organizational structure in dynamic social network. ICDM, 2009.
[8]
S. Wasserman and K. Faust. Social network analysis: methods and applications. Cambridge University Press, 1994.
[9]
Center for substance abuse treatment. Substance abuse treatment: group therapy. Treatment improvement protocol (TIP) series 41, 2005.
[10]
On finding socially tenuous groups for online social networks (online full version). http://www.cs.nthu.edu.tw/~chihya/KDD2017/paper.pdf.
[11]
U. Feige, G. Kortsarz, and D. Peleg. The dense k-subgraph problem. Algorithmica, 2001.
[12]
R. Mokken. Cliques, clubs and clans. Quality and Quantity: International Journal of Methodology, 1979.
[13]
J. Yang and J. Leskovec. Overlapping community detection at scale: a non negative matrix factorization approach. WSDM, 2013.
[14]
J. Xie, S. Kelley, and B. Szymanski. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Survey, 2013.
[15]
Q. Zhu, H. Hu, J. Xu, and W.-C. Lee. Geo-social group queries with minimum acquaintance constraint. Xiv:1406.7367v1,2014.
[16]
C.-Y. Shen, D.-N. Yang, L.-H. Huang, W.-C. Lee, and M.-S. Chen. Socio-spatial group queries for impromptu activity planning. TKDE, 2016.
[17]
H.-H. Shuai, D.-N. Yang, P. S. Yu, and M.-S. Chen.Willingness optimization for social group activity. VLDB, 2014.
[18]
Y.-L. Chen, M.-S. Chen, and P. Yu. Ensemble of diverse sparsifications for link prediction in large-scale networks, ICDM,2015.
[19]
V. Satuluri, S. Parthasarathy, and Y. Ruan. Local graph sparsification for scalable clustering. SIGMOD, 2011.
[20]
M. Mathioudakis, F. Bonchi, C. Castillo, A. Gionis, and A.Ukkonen. Sparsification of influence networks. KDD, 2011.
[21]
M. Ruan, R. Jin, and Y. Huang. Distance preserving graphs implification. ICDM, 2011.
[22]
P. Berman, S. Raskhodnikova, and G. Ruan. Finding sparser directed spanners. FSTTCS, 2010.
[23]
M. Hujter and Z. Tuza. The number of maximal independent sets in triangle-free graphs. SIAM Journal on Discrete Mathematics, 1993.
[24]
H. Hatami, J. Hladky, D. Kral, S. Norine, and A. Razborov. On the number of pentagons in triangle-free graphs. Journal of Combinatorial Theory, 2013.
[25]
A. Grzesik. On the maximum number of five-cycles in a triangle-free graph. Journal of Combinatorial Theory, 2012.
[26]
D. Brugmann, C. Komusiewicz, and H. Moser. On generating triangle-free graphs. Electronic Notes in Discrete Mathematics,2009.
[27]
M. Bougeret, N. Bousquet, R. Giroudeau, R. Watrigant. Parameterized complexity of the sparsest k-subgraph problem in chordalgraphs. SOFSEM, 2014.
[28]
A. Lee and I. Streinu. Pebble game algorithms and (k,l)-sparse graphs. DMTCS, 2005.
[29]
R. Watrigant, M. Bougeret, and R. Giroudeau. The k-sparsest subgraph problem. Tech. Rep., 2012.
[30]
J. Cheng, Z. Shang, H. Cheng, H. Wang, and J. Yu. K-reach: who is in your small world. VLDB, 2012.
[31]
N. Mahadev and U. Peled. Threshold graphs and related topics, New York, NY, USA: Elsevier, 1995.
[32]
S. Saha, N. Ganguly, and A. Mukherjee. Inter group networks as random threshold graphs. Physical Review E, 2014.
[33]
B. Viswanath, A. Mislove, M. Cha, and K. Gummadi. On the evolution of user interaction in Facebook. WOSN, 2009.
[34]
E. Ferrara, R. Interdonato, and A. Tagarelli. Online popularity and topical interests through the lens of Instagram. HT, 2014.
[35]
U. Feige. Approximating maximum clique by removing subgraphs. SIAM J. Discrete Math, 2004.
[36]
D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. Journal of the American Soceity for Information Science and Technology, 2007.
[37]
A. Clause, C. Moore, and M. Newman. Hierarchical structure and the prediction of missing links in network. Nature, 2008.
[38]
R. A. Rossi, D. F. Gleich, A. H. Gebremedhin, and Md. M. A. Patwary. Fast maximum clique algorithms for large graphs. WWW, 2014.
[39]
J. Xiang, C. Guo, and A. Aboulnaga. Scalable maximum clique computation using mapreduce. ICDE, 2013.

Cited By

View all
  • (2023)Kernel-wise difference minimization for convolutional neural network compression in metaverseFrontiers in Big Data10.3389/fdata.2023.12003826Online publication date: 4-Aug-2023
  • (2023)Density Personalized Group QueryProceedings of the VLDB Endowment10.14778/3574245.357424916:4(615-628)Online publication date: 21-Feb-2023
  • (2023)Keyword-based Socially Tenuous Group Queries2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00079(965-977)Online publication date: Apr-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2017
2240 pages
ISBN:9781450348874
DOI:10.1145/3097983
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: 04 August 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. k-triangles
  2. socially tenuous groups

Qualifiers

  • Research-article

Funding Sources

  • Ministry of Science and Technology (MOST) Taiwan

Conference

KDD '17
Sponsor:

Acceptance Rates

KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)24
  • Downloads (Last 6 weeks)6
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Kernel-wise difference minimization for convolutional neural network compression in metaverseFrontiers in Big Data10.3389/fdata.2023.12003826Online publication date: 4-Aug-2023
  • (2023)Density Personalized Group QueryProceedings of the VLDB Endowment10.14778/3574245.357424916:4(615-628)Online publication date: 21-Feb-2023
  • (2023)Keyword-based Socially Tenuous Group Queries2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00079(965-977)Online publication date: Apr-2023
  • (2023)PSPC: Efficient Parallel Shortest Path Counting on Large-Scale Graphs2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00074(896-908)Online publication date: Apr-2023
  • (2023)Similarity-Aware Sampling for Machine Learning-Based Goal-Oriented Subgraph ExtractionICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279825(5589-5594)Online publication date: 28-May-2023
  • (2022)A Novel Approach for Tenuous Community Detection in Social NetworksInternational Journal of Data Analytics10.4018/IJDA.2975183:1(1-12)Online publication date: 1-Jan-2022
  • (2022)Learning to Solve Task-Optimized Group Search for Social Internet of ThingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.305736134:11(5429-5445)Online publication date: 1-Nov-2022
  • (2022)The most tenuous group queryFrontiers of Computer Science10.1007/s11704-022-1462-517:2Online publication date: 8-Aug-2022
  • (2021)Towards De-Anonymization of Google Play Search Rank FraudIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.297517033:11(3648-3661)Online publication date: 5-Oct-2021
  • (2021)Distance labeling: on parallelism, compression, and orderingThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00694-131:1(129-155)Online publication date: 31-Aug-2021
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media