Empirical evaluation of different feature representations for social circles detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_4Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. We propose in this paper an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks. We compare our results with several different baselines.This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), fundedby the US Army Research Office (ARO).Alonso, J.; Paredes Palacios, R.; Rosso, P. (2015). Empirical evaluation of different feature representations for social circles detection. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 31-38. https://doi.org/10.1007/978-3-319-19390-8_4S3138Buhmann, J., Kühnel, H.: Vector quantization with complexity costs. IEEE Trans. Inf. Theor. 39(4), 1133–1145 (1993)Dey, K., Bandyopadhyay, S.: An empirical investigation of like-mindedness of topically related social communities on microblogging platforms. In: International Conference on Natural Languages (2013)Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)Frank, M., Streich, A.P., Basin, D., Buhmann, J.M.: Multi-assignment clustering for boolean data. J. Mach. Learn. Res. 13(1), 459–489 (2012)Kaggle: Learning social circles in networks. http://www.kaggle.com/c/learning-social-circlesMcAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. Adv. Neural Inf. Process. Syst. 25, 539–547 (2012)McAuley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 4 (2014)Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)Pathak, N., DeLong, C., Banerjee, A., Erickson, K.: Social topic models for community extraction. In: The 2nd SNA-KDD Workshop (2008)Porter, M.A., Onnela, J.P., Mucha, P.J.: Communities in networks. Not. Amer. Math. Soc. 56(9), 1082–1097 (2009)Rose, K., Gurewitz, E., Fox, G.C.: Vector quantization by deterministic annealing. IEEE Transactions on Information Theory 38(4), 1249–1257 (1992)Sachan, M., Contractor, D., Faruquie, T.A., Subramaniam, L.V.: Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 331–340 (2012)Streich, A.P., Frank, M., Basin, D., Buhmann, J.M.: Multi-assignment clustering for Boolean data. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 969–976 (2009)Vaidya, J., Atluri, V., Guo, Q.: The role mining problem: finding a minimal descriptive set of roles. In: Proceedings of the 12th ACM Symposium on Access Control Models and Technologies, pp. 175–184 (2007)Zhou, D., Councill, I., Zha, H., Giles, C.L.: Discovering temporal communities from social network documents. In: Seventh IEEE International Conference on Data Mining, PP. 745–750 (2007

A General Framework for Complex Network Applications

Complex network theory has been applied to solving practical problems from different domains. In this paper, we present a general framework for complex network applications. The keys of a successful application are a thorough understanding of the real system and a correct mapping of complex network theory to practical problems in the system. Despite of certain limitations discussed in this paper, complex network theory provides a foundation on which to develop powerful tools in analyzing and optimizing large interconnected systems.Comment: 8 page

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems

Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

Feature representation for social circles detection using MAC

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2222-ySocial circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on two available labelled Facebook datasets and compare our results with several different baselines. In addition, we provide some insights of the evaluation metrics most commonly used in the literature.This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), funded by the US Army Research Office (ARO). The work of the first author is financed by Grant FPU14/03483, from the Spanish Ministry of Education, Culture and Sport.Alonso-Nanclares, JA.; Paredes Palacios, R.; Rosso, P. (2016). Feature representation for social circles detection using MAC. Neural Computing and Applications. 1-8. https://doi.org/10.1007/s00521-016-2222-yS18Alonso J, Paredes R, Rosso P (2015) Empirical evaluation of different feature representations for social circles detection. In: Pattern recognition and image analysis, lecture notes in computer science, vol. 9117, pp 31–38. Springer, Berlin. doi: 10.1007/978-3-319-19390-8_4Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theor Exp 2008:P10, 008Brandes U, Delling D, Gaertler M, Gaerke R, Hoefer M, Nikoloski Z, Wagner D (2006) On modularity-NP-completeness and beyond. Technical Report. 2006–19, ITI Wagner, Faculty of Informatics, Universität Karlsruhe (TH), GermanyBuhmann J, Kuhnel H (1993) Vector quantization with complexity costs. IEEE Trans Inf Theory 39(4):1133–1145Chen Y, Lin C (2006) Combining SVMs with various feature selection strategies. In: Feature extraction, pp 315–324Dey K, Bandyopadhyay S (2013) An empirical investigation of like-mindedness of topically related social communities on microblogging platforms. In: International conference on natural languagesDonath WE, Hoffman AJ (1973) Lower bounds for the partitioning of graphs. IBM J Res Dev 17(5):420–425Everitt BS, Hand DJ (1981) Finite mixture distributions. Chapman and Hall, LondonFortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174Frank M, Streich AP, Basin D, Buhmann JM (2012) Multi-assignment clustering for Boolean data. J Mach Learn Res 13(1):459–489Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, BerlinJaccard P (1908) Nouvelles recherches sur la distribution florale. Bulletin de la Socit Vaudoise des Sciences Naturelles 44(163):223–270Kaggle: Learning social circles in networks. http://www.kaggle.com/c/learning-social-circlesKernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291–307Leskovec J, Krevl A (2014) SNAP datasets: stanford large network dataset collection. http://snap.stanford.edu/dataLeskovec J, Mcauley J (2012) Learning to discover social circles in ego networks. In: Pereira F, Burges C, Bottou L, Weinberger K (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., Red Hook, pp 539–547Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of fifth Berkeley symposium on Mathematical Statistics and Probability, vol 1, pp 281–297McAuley J, Leskovec J (2014) Discovering social circles in ego networks. ACM Trans Knowl Discov Data 8(1):4Munkres J (1957) Algorithms for the assignment and transportation problems. J Soc Ind Appl Math 5(1):32–38Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582Newman ME, Girvan M (2014) Finding and evaluating community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(2):026,113Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818Pathak N, DeLong C, Banerjee A, Erickson K (2008) Social topic models for community extraction. In: The 2nd SNA-KDD workshopPorter MA, Onnela JP, Mucha PJ (2009) Communities in networks. Not Am Math Soc 56(9):1082–1097Rose K, Gurewitz E, Fox GC (1992) Vector quantization by deterministic annealing. IEEE Trans Inf Theory 38(4):1249–1257Sachan M, Contractor D, Faruqie TA, Subramaniam LV (2012) Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st international conference on World Wide Web, pp 331–340Streich AP, Frank M, Basin D, Buhmann JM (2009) Multi-assignment clustering for boolean data. In: Proceedings of the 26th annual international conference on machine learning, pp 969–976Suaris PR, Kedem G (1988) An algorithm for quadrisection and its applications to standard cell placement. IEEE Trans Circuits Syst 35(3):294–303Vaidya J, Atluri V, Guo Q (2007) The role mining problem: finding a minimal descriptive set of roles. In: Proceedings of the 12th ACM symposium on access control models and technologies, pp 175–184Yang J, McAuley J, Leskovec J (2013) Community detection in networks with node attributes. In: IEEE 13th international conference on data mining (ICDM), pp 1151–1156. IEEEZhou D, Councill I, Zha H, Giles CL (2007) Discovering temporal communities from social network documents. In: Seventh IEEE international conference on data mining, pp 745–75

Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks

Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks. In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1

Community Detection in Networks with Node Attributes

Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.Comment: Published in the proceedings of IEEE ICDM '1