Skip to main content
SpringerLink
Log in

Inferring Social Relationships Through Network: A Systematic Literature Review

  • Conference paper
  • First Online:
Information Science and Applications 2018 (ICISA 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 514))

Included in the following conference series:

  • 1504 Accesses

Abstract

Nowadays networks are developing extensively in size, intricacy, and diversity. Due to modification in social networks, advanced and distinctive kind of networks is emerging such as wireless networks, social networks, criminal networks and ego networks. Social network identification is the key to gather significant details from networks. Systematic Literature Review has been discerned to distinguish 31 papers from 2010 to 2018 to provide the set of frameworks that researchers could focus on. The aim is to organize the main categories of community discovery based on their definition of community and to identify algorithms, models, methods, and approaches that have been proposed. Consequently, 7 different categories of social networks have been identified. Furthermore, 20 algorithms, 4 approaches, 4 methods and 3 models for identifying social relationships from the network have been proposed. Based on the results obtained from the systematic review, we conclude that most of the work has been done on inferring community detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bindu PV, Thilagam PS, Ahuja D (2017) Discovering suspicious behavior in multilayer social networks. Comput Hum Behav 73:568–582

    Article  Google Scholar 

  2. Liu T, Qin H (2016) Detecting and tagging users’ social circles in social media. Multimedia Syst 22(4):423–431

    Article  Google Scholar 

  3. Yang J, Leskovec J (2015) Defining and evaluating network communities based on ground-truth. Knowl Inf Syst 42(1):181–213

    Article  Google Scholar 

  4. Zhang X, Butts CT (2017) Activity correlation spectroscopy: a novel method for inferring social relationships from activity data. Soc Netw Anal Mining 7(1):1

    Article  Google Scholar 

  5. Kitchenham B (2004) Procedures for performing systematic reviews, vol 33. Keele, UK, Keele University, pp 1–26

    Google Scholar 

  6. Tang X, Yang C, Gong X (2011) A spectral analysis approach for social media community detection. Soc Inf 127–134

    Google Scholar 

  7. Ferreira LN, Pinto AR, Zhao L (2012) QK-means: a clustering technique based on community detection and K-means for deployment of cluster head nodes. In: 2012 International Joint Conference on Neural Networks (IJCNN), June 2012, IEEE, pp 1–7

    Google Scholar 

  8. Ramezani M, Khodadadi A, Rabiee HR (2018) Community detection using diffusion information. ACM Trans Knowl Discov Data (TKDD) 12(2):20

    Article  Google Scholar 

  9. Banati H, Arora N (2016) Detecting communities in complex networks-a discrete hybrid evolutionary approach. Int J Comput Appl 38(1):29–40

    Google Scholar 

  10. Hu L, Chan KC (2016) Fuzzy clustering in a complex network based on content relevance and link structures. IEEE Trans Fuzzy Syst 24(2):456–470

    Article  Google Scholar 

  11. Shen Q, Boongoen T (2012) Fuzzy orders-of-magnitude-based link analysis for qualitative alias detection. IEEE Trans Knowl Data Eng 24(4): 649–664

    Article  Google Scholar 

  12. Miao Q, Tang X, Quan Y, Deng K (2014) Detecting circles on ego network based on structure. In: 2014 Tenth International Conference on Computational Intelligence and Security (CIS), Nov 2014. IEEE, pp 213–217

    Google Scholar 

  13. Reid F, McDaid A, Hurley N (2012) August. Percolation computation in complex networks. In: 2012 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), Aug 2012. IEEE, pp 274–281

    Google Scholar 

  14. Chong WH, Teow LN (2013) An incremental batch technique for community detection. In: 2013 16th international conference on information fusion (FUSION), July 2013. IEEE, pp 750–757

    Google Scholar 

  15. Varamesh A, Akbari MK, Fereiduni M, Sharifian S, Bagheri A (2013) Distributed Clique Percolation based community detection on social networks using MapReduce. In: 2013 5th Conference on Information and Knowledge Technology (IKT), May 2013. IEEE, pp 478–483

    Google Scholar 

  16. Ahmedi L (2012) AuthorRank + FOAF: ranking for co-authorship networks on the web. In Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012) Aug 2012, IEEE Computer Society, pp 315–321

    Google Scholar 

  17. Ozgul F, Erdem Z, Bowerman C, Bondy J (2010) Combined detection model for criminal network detection. Intell Secur Inf 1–14

    Google Scholar 

  18. Xu L, Lin L, Wen S (2015) November. First-priority relation graph-based malicious users detection in mobile social networks. In: International conference on network and system security, Nov 2015. Springer International Publishing, pp 459–466

    Chapter  Google Scholar 

  19. Atay Y, Koc I, Babaoglu I, Kodaz H (2017) Community detection from biological and social networks: a comparative analysis of metaheuristic algorithms. Appl Soft Comput 50:194–211

    Article  Google Scholar 

  20. Gómez D, Zarrazola E, Yáñez J, Montero J (2015) A divide-and-link algorithm for hierarchical clustering in networks. Inf Sci 316:308–328

    Article  Google Scholar 

  21. Li J, Wang X, Cui Y (2014) Uncovering the overlapping community structure of complexnetworks by maximal cliques. Phys A 415:398–406

    Article  MathSciNet  Google Scholar 

  22. Dutta R, Gupta S, Das MK (2014) Low-energy adaptive unequal clustering protocol using fuzzy c-means in wireless sensor networks. Wirel Pers Commun 79(2):1187–1209

    Article  Google Scholar 

  23. Mcauley J, Leskovec J (2014) Discovering social circles in ego networks. ACM Trans Knowl Discov Data (TKDD) 8(1):4

    Google Scholar 

  24. Xie J, Kelley S, Szymanski BK (2013) Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Comput Surv (CSUR) 45(4):43

    Article  Google Scholar 

  25. Wang R, Rho S, Cai W (2017) High-performance social networking: microblog community detection based on efficient interactive characteristic clustering. Clust Comput 1–13

    Google Scholar 

  26. Soundarajan S, Hopcroft JE (2015) Use of local group information to identify communities in networks. ACM Trans Knowl Discov Data (TKDD) 9(3):21

    Google Scholar 

  27. Jakalan A, Gong J, Su Q, Hu X, Abdelgder AM (2016) Social relationship discovery of IP addresses in the managed IP networks by observing traffic at network boundary. Comput Netw 100:12–27

    Article  Google Scholar 

  28. Symeonidis P, Tiakas E, Manolopoulos Y (2010) Transitive node similarity for link prediction in social networks with positive and negative links. In: Proceedings of the fourth ACM conference on recommender systems Sept 2010. ACM, pp 183–190

    Google Scholar 

  29. Coscia M, Rossetti G, Giannotti F, Pedreschi D (2012) Demon: a local-first discovery method for overlapping communities. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining Aug 2012. ACM, pp 615–623

    Google Scholar 

  30. Dev H (2014) A user interaction based community detection algorithm for online social networks. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data, June 2014. ACM, pp 1607–1608

    Google Scholar 

  31. Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on web search and data mining, Feb 2011. ACM, pp 635–644

    Google Scholar 

  32. Chin A, Chignell M, Wang H (2010) Tracking cohesive subgroups over time in inferred social networks. New Rev 16(1–2):113–139

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of E&ME, National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan

    Fauqia Ilyas, Farooque Azam, Wasi Haider Butt & Kinza Zahra

Authors
  1. Fauqia Ilyas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farooque Azam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wasi Haider Butt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kinza Zahra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fauqia Ilyas .

Editor information

Editors and Affiliations

  1. iCatse, Seongnam, Gyeonggi, Korea (Republic of)

    Kuinam J. Kim

  2. School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea (Republic of)

    Nakhoon Baek

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ilyas, F., Azam, F., Butt, W.H., Zahra, K. (2019). Inferring Social Relationships Through Network: A Systematic Literature Review. In: Kim, K., Baek, N. (eds) Information Science and Applications 2018. ICISA 2018. Lecture Notes in Electrical Engineering, vol 514. Springer, Singapore. https://doi.org/10.1007/978-981-13-1056-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1056-0_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1055-3

  • Online ISBN: 978-981-13-1056-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation