Hostname: page-component-89b8bd64d-z2ts4 Total loading time: 0 Render date: 2026-05-06T02:00:02.874Z Has data issue: false hasContentIssue false
  • English
  • Français

Characterizing ego-networks using motifs

Published online by Cambridge University Press:  19 September 2013

PÁDRAIG CUNNINGHAM ,
MARTIN HARRIGAN ,
GUANGYU WU  and
DEREK O'CALLAGHAN
PÁDRAIG CUNNINGHAM
Affiliation:
School of Computer Science and Informatics, University College Dublin, Ireland (email: padraig.cunningham@ucd.ie)
MARTIN HARRIGAN
Affiliation:
School of Computer Science and Informatics, University College Dublin, Ireland (email: padraig.cunningham@ucd.ie)
GUANGYU WU
Affiliation:
School of Computer Science and Informatics, University College Dublin, Ireland (email: padraig.cunningham@ucd.ie)
DEREK O'CALLAGHAN
Affiliation:
School of Computer Science and Informatics, University College Dublin, Ireland (email: padraig.cunningham@ucd.ie)
Get access Rights & Permissions [Opens in a new window]

Abstract

We assess the potential of network motif profiles to characterize ego-networks in much the same way that a bag-of-words strategy allows text documents to be compared in a vector space framework. This is potentially valuable as a generic strategy for comparing nodes in a network in terms of the network structure in which they are embedded. In this paper, we consider the computational challenges and model selection decisions involved in network motif profiling. We also present three case studies concerning the analysis of Wikipedia edit networks, YouTube spam campaigns, and peer-to-peer lending in the Prosper marketplace.

Information

Type
Research Article
Information
Network Science , Volume 1 , Issue 2 , August 2013 , pp. 170 - 190
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Allan, Jr., Edward, G., Turkett, Jr., William, H., & Fulp, E. W. (2009). Using network motifs to identify application protocols. Proceedings of the 28th IEEE Conference on Global Telecommunications (GLOBECOM'09), Piscataway, NJ: IEEE Press, pp. 4266–4272.Google Scholar
Anderson, C. J., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social networks, 21 (1), 37–66.Google Scholar
Antiqueira, L. & da Fontoura Costa, L. (2009). Characterization of subgraph relationships and distribution in complex networks. New Journal of Physics, 11 (013058).Google Scholar
Artzy-Randrup, Y., Fleishman, S. J., Ben-Tal, N., & Stone, L. (2004). Comment on “Network motifs: Simple building blocks of complex networks” and “superfamilies of evolved and designed networks”. Science, 305 (5687), 1107.Google Scholar
Becchetti, L., Boldi, P., Castillo, C., & Gionis, A. (2008). Efficient semi-streaming algorithms for local triangle counting in massive graphs. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '08), New York: ACM, pp. 16–24.CrossRefGoogle Scholar
Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M. (2009). Detecting spammers and content promoters in online video social networks. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '09), New York: ACM, pp. 620–627.Google Scholar
Borgatti, S., & Everett, M. (1989). The class of all regular equivalences: Algebraic structure and computation. Social Networks, 11, 65–88.Google Scholar
Borgatti, S. P., & Everett, M. G. (1992). Notions of position in social network analysis. Sociological Methodology, 22 (1), 1–35.Google Scholar
Borgwardt, K. M., & Kriegel, H. P. (2005). Shortest-path kernels on graphs. Fifth IEEE International Conference on Data Mining, New York: IEEE, pp. 74–81.Google Scholar
Boykin, P. O., & Roychowdhury, V. P. (2005). Leveraging social networks to fight spam. Computer, 38 (4), 61–68.Google Scholar
Brandes, U., Lerner, J., Lubbers, M., McCarty, C., & Molina, J. (2008). Visual statistics for collections of clustered graphs. Proceedings of the IEEE VGTC Pacific Visualization Symp. (PacificVis'08), New York, pp. 47–54.Google Scholar
Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Cord, M. & Cunningham, P. (Eds.), Machine learning techniques for multimedia (pp. 21–49). Berlin: Springer.CrossRefGoogle Scholar
Davis, J. A. (1963). Structural balance, mechanical solidarity, and interpersonal relations. American Journal of Sociology, 68, 444–462.Google Scholar
Faust, K. (2007). Very local structure in social networks. Sociological Methodology, 37 (1), 209–256.Google Scholar
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7 (2), 179–188.Google Scholar
Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., & Zhao, B. Y. (2010). Detecting and characterizing social spam campaigns. Proceedings of the 10th Annual Conference on Internet Measurement (IMC '10). New York: ACM, pp. 35–47.Google Scholar
Gärtner, T., Flach, P., & Wrobel, S. (2003). On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, 2777, 129–143.Google Scholar
Harrigan, M., Archambault, D., Cunningham, P., & Hurley, N. (2012). EgoNav: Exploring networks through egocentric spatializations. Proceedings of the International Working Conference on Advanced Visual Interfaces (AVI 2012). New York: ACM, pp. 563–570.Google Scholar
Holland, P. W., & Leinhardt, S. (1976). Local structure in social networks. Sociological Methodology, 7 (1).CrossRefGoogle Scholar
Horváth, T., Gärtner, T., & Wrobel, S. (2004). Cyclic pattern kernels for predictive graph mining. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, pp. 158–167.Google Scholar
Jurgens, D., & Lu, T.-C. (2012). Temporal motifs reveal the dynamics of editor interactions in Wikipedia. In Breslin, J. G., Ellison, N. B., Shanahan, J. G., & Tufekci, Z. (Eds.), Proceedings of the Sixth International Conference on Weblogs and Social Media (ICWSM 2012). Palo Alto, CA: AAAI Press.Google Scholar
Juszczyszyn, K., Kazienko, P. & Musiał, K. (2008). Local topology of social network based on motif analysis. In Lovrek, I., Howlett, R., & Jain, L. (Eds.), Knowledge-based intelligent information and engineering Systems (pp. 97–105). Lecture Notes in Computer Science, vol. 5178. Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
Kalish, Y., & Robins, G. (2006). Psychological predispositions and network structure: The relationship between individual predispositions, structural holes and network closure. Social networks, 28 (1), 56–84.Google Scholar
Kamaliha, E., Riahi, F., Qazvinian, V., & Adibi, J. (2008). Characterizing network motifs to identify spam comments. Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, Washington, DC: IEEE Computer Society, pp. 919–928.Google Scholar
Kashima, H., Tsuda, K., & Inokuchi, A. (2004). Kernels for graphs. Kernel Methods in Computational Biology, 39 (1), 101–113.Google Scholar
Keegan, B., Gergle, D., & Contractor, N. (2012). Staying in the loop: Structure and dynamics of Wikipedia's breaking news collaborations. Proceedings of the 8th International Symposium on Wikis and Open Collaboration, Linz, Austria.Google Scholar
Krause, J., Croft, D. P., & James, R. (2007). Social network theory in the behavioural sciences: Potential applications. Behavioral Ecology and Sociobiology, 62 (1), 15–27.CrossRefGoogle ScholarPubMed
Lubbers, M., Molina, J., Lerner, J., Brandes, U., Ávila, J., & McCarty, C. (2010). Longitudinal analysis of personal networks: The case of Argentinean migrants in Spain. Social Networks, 32 (1), 91–104.Google Scholar
Luo, B., Wilson, R. C., & Hancock, E. R. (2003). Spectral embedding of graphs. Pattern Recognition, 36 (10), 2213–2230.Google Scholar
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298 (5594), 824–827.Google Scholar
Milo, R., Itzkovitz, S., Kashtan, N., Levitt, R., Shen-Orr, S., Ayzenshtat, I., Sheffer, M., & Alon, U. (2004). Superfamilies of evolved and designed networks. Science, 303 (5663), 1538.Google Scholar
Moreno, J. L. (1934). Who shall survive? A new approach to the problem of human interrelations. New York: Nervous and Mental Disease Publishing Co.Google Scholar
O'Callaghan, D., Harrigan, M., Carthy, J., & Cunningham, P. (2012). Network analysis of recurring YouTube spam campaigns. In Breslin, J. G., Ellison, N. B., Shanahan, J. G., & Tufekci, Z. (Eds.), Proceedings of the Sixth International Conference on Weblogs and Social Media (ICWSM 2012). Palo Alto, CA: The AAAI Press.Google Scholar
Paton, K. (1969). An algorithm for finding a fundamental set of cycles of a graph. Communications of the ACM, 12 (9), 514–518.Google Scholar
Pržulj, N. (2007). Biological network comparison using graphlet degree distribution. Bioinformatics, 23 (2), e177–183.Google Scholar
Ramon, J., & Gärtner, T. (2003). Expressivity versus efficiency of graph kernels. First International Workshop on Mining Graphs, Trees and Sequences, Osaka, Japan, pp. 65–74.Google Scholar
Redmond, U., Harrigan, M., & Cunningham, P. (2012). Mining dense structures to uncover anomalous behaviour in financial network data. In Atzmueller, M., Chin, A., Helic, D., & Hotho, A. (Eds.), Modeling and mining ubiquitous social media (pp. 60–76). Lecture Notes in Computer Science, vol. 7472. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007a). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29 (2), 173–191.CrossRefGoogle Scholar
Robins, G., Snijders, T., Wang, P., Handcock, M., & Pattison, P. (2007b). Recent developments in exponential random graph (p*) models for social networks. Social Networks, 29 (2), 192–215.Google Scholar
Saul, Z. M., & Filkov, V. (2007). Exploring biological network structure using exponential random graph models. Bioinformatics, 23 (19), 2604.Google Scholar
Shervashidze, N., Vishwanathan, S. V. N., Petri, T., Mehlhorn, K., & Borgwardt, K. (2009). Efficient graphlet kernels for large graph comparison. Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS'09), Cambridge, MA.Google Scholar
Stoica, A., & Prieur, C. (2009). Structure of neighborhoods in a large social network. Proceedings of the International Conference on Computational Science & Engineering (CSE'09), New York, pp. 26–33.Google Scholar
Vazquez, A., Dobrin, R., Sergi, D., Eckmann, J. P., Oltvai, Z. N., & Barabási, A. L. (2004). The topological relationship between the large-scale attributes and local interaction patterns of complex networks. Proceedings of the National Academy of Sciences of the United States of America, 101 (52), 17940.Google Scholar
Wellman, B. (1993). An egocentric network tale: Comment on Bien et al. Social Networks, 15, 423–436.Google Scholar
Welser, H., Gleave, E., Fisher, D., & Smith, M. (2007). Visualizing the signatures of social roles in online discussion groups. Journal of Social Structure, 8.Google Scholar
Wernicke, S., & Rasche, F. (2006). FANMOD: A tool for fast network motif detection. Bioinformatics, 22 (9), 1152.Google Scholar
White, H., Boorman, S., & Breiger, R. (1976). Social structure from multiple networks – blockmodels of roles and positions. American Journal of Sociology, 81, 730–780.Google Scholar
Xie, Y., Yu, F., Achan, K., Panigrahy, R., Hulten, G., & Osipkov, I. (2008). Spamming botnets: Signatures and characteristics. Proceedings of the ACM SIGCOMM 2008 Conference on Data Communication (SIGCOMM '08). New York: ACM, pp. 171–182.Google Scholar