STACY: Strength of Ties Automatic-Classifier over the Years
DOI:
https://doi.org/10.5753/jidm.2018.1636Keywords:
Social Networks, Tie Strength, Co-authorship NetworksAbstract
With the evolution of Web technology and its worldwide use by regular people, there is now data about not only such people but also their relations. Database research has evolved as well to tackle the myriad of problems that arrive with such volumes of data. Here, we contribute to such a trend by proposing a new algorithm (STACY) to automatically classify tie strength (an intrinsic property of relationships) considering time. We show that each class has singular and different behavior, and analyze them over co-authorship networks. Also, STACY identifies strong relationships that persist more than the ones classified by a state of the art algorithm. Finally, we derive a computational model from STACY that is able to automatically identify relationships classes with low computational cost.
Downloads
References
Adams, B. N. Interaction theory and the social network. Sociometry, 1967.
Barabási, A.-L. Network science. Cambridge university press, 2016.
Bigonha, C., Cardoso, T. N. C., Moro, M. M., Gonçalves, M. A., and Almeida, V. A. F. Sentiment-based influence detection on twitter. Journal of the Brazilian Computer Society 18 (3): 169–183, 2012.
Brandão, M. A., de Melo, P. O. V., and Moro, M. M. Stacy: Um novo algoritmo para automaticamente classificar a força dos relacionamentos ao longo dos anos. In Procs. of SimpÃşsio Brasileiro de Bancos de Dados (SBBD). Uberlândia, Brazil, pp. 136–147, 2017a.
Brandão, M. A., de Melo, P. O. V., and Moro, M. M. Tie strength persistence and transformation. In Procs. of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web (AMW). Montevideo, Uruguay, pp. 1–4, 2017b.
Brandão, M. A. and Moro, M. M. Analyzing the strength of co-authorship ties with neighborhood overlap. In Procs. of the International Conference on Database and Expert Systems Applications (DEXA). Valencia, Spain, pp. 527–542, 2015.
Brandão, M. A. and Moro, M. M. Social professional networks: A survey and taxonomy. Computer Communications vol. 100, pp. 20 – 31, 2017.
Brandão, M. A., Moro, M. M., and Almeida, J. M. Experimental evaluation of academic collaboration recommendation using factorial design. Journal of Information and Data Management 5 (1): 52, 2014.
Castilho, D., de Melo, P. O. V., and Benevenuto, F. The strength of the work ties. Information Sciences vol. 375, pp. 155–170, 2017.
Chan, H. F., Önder, A. S., and Torgler, B. The first cut is the deepest: repeated interactions of coauthorship and academic productivity in nobel laureate teams. Scientometrics 106 (2): 509–524, 2016.
Chung, F. and Lu, L. Connected components in random graphs with given expected degree sequences. Annals of combinatorics 6 (2): 125–145, 2002.
Freire, V. P. and Figueiredo, D. R. Ranking in collaboration networks using a group based metric. Journal of the Brazilian Computer Society 17 (41): 255–266, 2011.
Gilbert, E. and Karahalios, K. Predicting tie strength with social media. In Procs. of Conference on Human Factors in Computing Systems (SIGCHI). Boston, USA, pp. 211–220, 2009.
Granovetter, M. S. The strength of weak ties. The American Journal of Sociology 78 (6): 1360–1380, 1973.
Guerra-Gomez, J. A., Wilson, A., Liu, J., Davies, D., Jarvis, P., and Bier, E. Network explorer: Design, implementation, and real world deployment of a large network visualization tool. In Procs. of International Working Conference on Advanced Visual Interfaces (AVI). Bari, Italy, pp. 108–111, 2016.
Huang, H., Dong, Y., Tang, J., Yang, H., Chawla, N. V., and Fu, X. Will triadic closure strengthen ties in social networks? ACM Trans. Knowl. Discov. Data 12 (3): 30:1–30:25, 2018.
Karsai, M., Perra, N., and Vespignani, A. Time varying networks and the weakness of strong ties. Scientific reports vol. 4, 2014.
Kostakos, V. Temporal graphs. Physica A: Statistical Mechanics and its Applications 388 (6): 1007–1023, 2009.
Laurent, G., Saramäki, J., and Karsai, M. From calls to communities: a model for time-varying social networks. The European Physical Journal B 88 (11): 1–10, 2015.
Levin, F. H. and Heuser, C. A. Evaluating the use of social networks in author name disambiguation in digital libraries. Journal of Information and Data Management 1 (2): 183, 2010.
Lopes, G. R., Moro, M. M., Wives, L. K., and de Oliveira, J. P. M. Collaboration Recommendation on Academic Social Networks. In ER Workshops. pp. 190–199, 2010.
Miller, J. C. and Hagberg, A. Efficient generation of networks with given expected degrees. In Procs. of International Workshop on Algorithms and Models for the Web-Graph (WAW). Barcelona, Spain, pp. 115–126, 2011.
Montolio, S. L., Dominguez-Sal, D., and Larriba-Pey, J. L. Research endogamy as an indicator of conference quality. ACM SIGMOD Record 42 (2): 11–16, 2013.
Nicosia et al., V. Graph metrics for temporal networks. In Temporal Networks, P. Holme and J. Saramäki (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 15–40, 2013.
Shi, M., Yang, B., and Chiang, J. Dyad calling behavior: Asymmetric power and tie strength dynamics. Journal of Interactive Marketing vol. 42, pp. 63–79, 2018.
Silva, T. H. P., Moro, M. M., Silva, A. P. C., Meira, Jr., W., and Laender, A. H. F. Community-based endogamy as an influence indicator. In Procs. of ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL). London, United Kingdom, pp. 67–76, 2014.
Vaz de Melo et al., P. O. S. Recast: Telling apart social and random relationships in dynamic networks. Performance Evaluation vol. 87, pp. 19–36, 2015.
Viana, W., da Silva, A. P. C., and Moro, M. M. Pick the Right Team and Make a Blockbuster: A Social Analysis Through Movie History. In Proceedings of the 31st Annual ACM Symposium on Applied Computing. Pisa, Italy, pp. 1108–1114, 2016.
Wang, X., Hoi, S. C., Ester, M., Bu, J., and Chen, C. Learning personalized preference of strong and weak ties for social recommendation. In Procs. of the 26th International Conference on World Wide Web (WWW). Perth, Australia, pp. 1601–1610, 2017.
Zaki, M. J. and Meira Jr, W. Data mining and analysis: Fundamental concepts and algorithms. Cambridge University Press, 2014.
Zignani, M., Gaito, S., and Rossi, G. P. Predicting the link strength of newborn links. In Procs. of International Conference on World Wide Web (WWW). Montreal, Canada, pp. 147–148, 2016.
