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

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

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

A customisable pipeline for continuously harvesting socially-minded Twitter users

On social media platforms and Twitter in particular, specific classes of users such as influencers have been given satisfactory operational definitions in terms of network and content metrics. Others, for instance online activists, are not less important but their characterisation still requires experimenting. We make the hypothesis that such interesting users can be found within temporally and spatially localised contexts, i.e., small but topical fragments of the network containing interactions about social events or campaigns with a significant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profile discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profiles dataset includes key network and content-based users metrics, enabling experimentation with user-defined score functions that characterise specific classes of online users. The paper describes the design and implementation of the pipeline and its empirical evaluation on a case study consisting of healthcare-related campaigns in the UK, showing how it supports the operational definitions of online activism, by comparing three experimental ranking functions. The code is publicly available.Comment: Procs. ICWE 2019, June 2019, Kore