Skip to main content Accessibility help

Characterizing ego-networks using motifs



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.



Hide All
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. 42664272.
Anderson, C. J., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social networks, 21 (1), 3766.
Antiqueira, L. & da Fontoura Costa, L. (2009). Characterization of subgraph relationships and distribution in complex networks. New Journal of Physics, 11 (013058).
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.
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. 1624.
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. 620627.
Borgatti, S., & Everett, M. (1989). The class of all regular equivalences: Algebraic structure and computation. Social Networks, 11, 6588.
Borgatti, S. P., & Everett, M. G. (1992). Notions of position in social network analysis. Sociological Methodology, 22 (1), 135.
Borgwardt, K. M., & Kriegel, H. P. (2005). Shortest-path kernels on graphs. Fifth IEEE International Conference on Data Mining, New York: IEEE, pp. 7481.
Boykin, P. O., & Roychowdhury, V. P. (2005). Leveraging social networks to fight spam. Computer, 38 (4), 6168.
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. 4754.
Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Cord, M. & Cunningham, P. (Eds.), Machine learning techniques for multimedia (pp. 2149). Berlin: Springer.
Davis, J. A. (1963). Structural balance, mechanical solidarity, and interpersonal relations. American Journal of Sociology, 68, 444462.
Faust, K. (2007). Very local structure in social networks. Sociological Methodology, 37 (1), 209256.
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7 (2), 179188.
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. 3547.
Gärtner, T., Flach, P., & Wrobel, S. (2003). On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, 2777, 129143.
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. 563570.
Holland, P. W., & Leinhardt, S. (1976). Local structure in social networks. Sociological Methodology, 7 (1).
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. 158167.
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.
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. 97105). Lecture Notes in Computer Science, vol. 5178. Berlin/Heidelberg: Springer.
Kalish, Y., & Robins, G. (2006). Psychological predispositions and network structure: The relationship between individual predispositions, structural holes and network closure. Social networks, 28 (1), 5684.
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. 919928.
Kashima, H., Tsuda, K., & Inokuchi, A. (2004). Kernels for graphs. Kernel Methods in Computational Biology, 39 (1), 101113.
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.
Krause, J., Croft, D. P., & James, R. (2007). Social network theory in the behavioural sciences: Potential applications. Behavioral Ecology and Sociobiology, 62 (1), 1527.
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), 91104.
Luo, B., Wilson, R. C., & Hancock, E. R. (2003). Spectral embedding of graphs. Pattern Recognition, 36 (10), 22132230.
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), 824827.
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.
Moreno, J. L. (1934). Who shall survive? A new approach to the problem of human interrelations. New York: Nervous and Mental Disease Publishing Co.
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.
Paton, K. (1969). An algorithm for finding a fundamental set of cycles of a graph. Communications of the ACM, 12 (9), 514518.
Pržulj, N. (2007). Biological network comparison using graphlet degree distribution. Bioinformatics, 23 (2), e177183.
Ramon, J., & Gärtner, T. (2003). Expressivity versus efficiency of graph kernels. First International Workshop on Mining Graphs, Trees and Sequences, Osaka, Japan, pp. 6574.
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. 6076). Lecture Notes in Computer Science, vol. 7472. Berlin, Heidelberg: Springer.
Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007a). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29 (2), 173191.
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), 192215.
Saul, Z. M., & Filkov, V. (2007). Exploring biological network structure using exponential random graph models. Bioinformatics, 23 (19), 2604.
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.
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. 2633.
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.
Wellman, B. (1993). An egocentric network tale: Comment on Bien et al. Social Networks, 15, 423436.
Welser, H., Gleave, E., Fisher, D., & Smith, M. (2007). Visualizing the signatures of social roles in online discussion groups. Journal of Social Structure, 8.
Wernicke, S., & Rasche, F. (2006). FANMOD: A tool for fast network motif detection. Bioinformatics, 22 (9), 1152.
White, H., Boorman, S., & Breiger, R. (1976). Social structure from multiple networks – blockmodels of roles and positions. American Journal of Sociology, 81, 730780.
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. 171182.

Related content

Powered by UNSILO

Characterizing ego-networks using motifs



Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.