Social network analysis: methods and applications

The paper describes the basic components of ISPRAS technology stack for social network data analysis. Particular attention is given to tasks, methods, and applications of network (social connections between users) and textual (user messages and profiles) data analysis: demographic attribute detection, event detection in messages corpora, user identity resolution, community detection, and influence measurement. Means for input data acquisition are also considered: collecting real data through web-interfaces of social services and generating random social graphs. For each of the developed tools we describe its functionality, use cases, basic steps of the underlying algorithms, and experimental results.

social networks, social data, user data, social analysis, social network analysis, content analysis, web-services, microblogs, computational linguistics, graph theory, machine learning, distributed algorithms and systems

1. Najork M., Wiener J. L. Breadth-first crawling yields high-quality pages. Proceedings of the 10th international conference on World Wide Web. – ACM, 2001. – С. 114-118.

2. Leskovec J., Faloutsos C. Sampling from large graphs. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. – ACM, 2006. – С. 631-636.

3. Gjoka M. et al. Practical recommendations on crawling online social networks. Selected Areas in Communications, IEEE Journal on. – 2011. – Т. 29. – №. 9. – С. 1872-1892.

4. Boyd, D. M. and Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), article 11

5. George Pallis, Demetrios Zeinalipour-Yazti, Marios D. Dikaiakos. Online Social Networks: Status and Trends. New Directions in Web Data Management 1, Studies in Computational Intelligence Volume 331, 2011, pp 213-234

6. Key Trends to Watch in Gartner 2012 Emerging Technologies Hype Cycle. http://www.forbes.com/sites/gartnergroup/2012/09/18/key-trends-to-watch-in-gartner-2012-emerging-technologies-hype-cycle-2/

7. Аnton Korshunov. Zadachi i metody opredeleniya atributov pol'zovatelej sotsial'nykh setej [Problems and methods for attribute detection of social network users]. Trudy 15-j Vserossijskoj nauchnoj konferentsii «EHlektronnye biblioteki: perspektivnye metody i tekhnologii, ehlektronnye kollektsii» [The Proceedings of the National Russian Research Conference «Digital Libraries: Advanced Methods and Technologies, Digital Collections»] - RCDL’2013. (in Russian)

8. Anton Korshunov, Ivan Beloborodov, Andrey Gomzin, Christina Chuprina, Nikita Astrakhantsev, Yaroslav Nedumod, Denis Turdakov Opredelenie demograficheskikh atributov pol'zovatelej mikroblogov [Detection of demographic attributes of microblog users]. Trudy ISP RAN [Proceedings of ISP RAS], vol. 25, 2013. DOI: 10.15514/ISPRAS-2013-25-10. (in Russian)

9. Francois Fleuret. Fast Binary Feature Selection with Conditional Mutual Information. JMLR, 5:1531–1555, 2004

10. Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer. Online Passive-Aggressive Algorithms. JMLR, 7(Mar):551–585, 2006

11. Delip Rao, David Yarowsky, Abhishek Shreevats, Manaswi Gupta. Classifying Latent User Attributes in Twitter. Proceedings of the 2nd International Workshop on Search and Mining User-generated Contents, 2010

12. Faiyaz Al Zamal, Wendy Liu, Derek Ruths. Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, 2012

13. Jianshu Weng, Bu-Sung Lee: Event Detection in Twitter. ICWSM 2011

14. Zhu, Xiaojin and Goldberg, Andrew and Gael, Jurgen Van and Andrzejewski, David. Improving Diversity in Ranking using Absorbing Random Walks. HLT-NAACL, 97--104, 2007

15. Sergey Bartunov, Anton Korshunov, Seung-Taek Park, Wonho Ryu, Hyungdong Lee. Joint Link-Attribute User Identity Resolution in Online Social Networks. Proceedings of The Sixth SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD’12)

16. Sergej Bartunov, Аnton Korshunov. Identifikatsiya pol'zovatelej sotsial'nykh setej v Internet na osnove sotsial'nykh svyazej [Joint Link-Attribute User Identity Resolution In Online Social Networks]. Doklady Vserossijskoj nauchnoj konferentsii «Аnaliz izobrazhenij, setej i tekstov» [Analysis of Images, Social Networks, and Texsts conference] АIST'2012. Ekaterinburg, March, 16-18 2012 (in Russian)

17. Nazar Buzun, Anton Korshunov. Innovative Methods and Measures in Overlapping Community Detection // Proceedings of the International Workshop on Experimental Economics and Machine Learning (EEML 2012), Brussel, Belgium

18. Nazar Buzun, Аnton Korshunov. Vyyavlenie peresekayushhikhsya soobshhestv v sotsial'nykh setyakh [Identifying overlapping communities in social networks]. Doklady Vserossijskoj nauchnoj konferentsii «Аnaliz izobrazhenij, setej i tekstov» [Analysis of Images, Social Networks, and Texsts conference] АIST'2012. Ekaterinburg, March, 16-18 2012 (in Russian)

19. Grzegorz Malewicz, Matthew Austern, Aart Bik, James Dehnert, Ilan Horn, Naty Leiser, Grzegorz Czajkowski. Pregel: a system for largescale graph processing. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

20. Andrea Lancichinetti, Santo Fortunato, Janos Kertesz. Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11 033015, 2009

21. Social Network Data Analytics. Editors: Charu C. Aggarwal. Springer, 2011