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

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. 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Advances in Social Circles

[EN] Social circles arised out of a need to organize the contacts in personal networks, within the current social networking services. The automatic detection of these social circles still remains an understudied problem, and is currently attracting a growing interest in the research community. This task is related to the classical problem of community detection in networks, albeit it presents some peculiarities, like overlap and hierarchical inclusion of circles. The usual community detection techniques cease to be the most appropiate, due to these characteristics. Prediction is performed from two data sources: the network graph and node attributes corresponding to users’ profile features. In this thesis, new approaches to this task are discussed and the results obtained from a thorough experimentation are presented. We provide a review of the state-of-the-art in the fields of community detection in graphs, community detection in social networks and social circles detection. We describe the datasets employed in our experiments, both retrieved from Facebook, and we design a variety of feature representations, both from the structural network information and the users’ profile information. We define and comment the prediction techniques in which our work is based: multi-assignment clustering, restricted Boltzmann machines and k-means. We describe some evaluation measures that have been proposed for social circles detection, and provide a critical commentary of some of them, as they present some flaws which lead to degenerate optimal performance. The core of this work is the presentation of the experiments that we have designed, along with the obtained results. There are two blocks of experiments, depending on the prediction technique employed: the first block considers multi-assignment clustering, a clustering method allowing for the inclusion of an element into several different clusters; whereas the second block considers a two-step method in which the data samples are mapped by restricted Boltzmann machines before feeding a k-means algorithm. We provide a discussion of the results, which have been satisfactory and have led to the publication of two articles, while a third one is awaiting revision. Our work opens the door to several lines of future work.[ES] Los círculos sociales han surgido de la necesidad de organizar los contactos en las redes personales, dentro de los servicios actuales de red social. La detección automática de estos círculos sociales es todavía un problema poco estudiado, y actualmente está atrayendo un interés creciente en la comunidad investigadora. Esta tarea está relacionada con el problema clásico de detección de comunidades en redes, aunque presenta ciertas peculiaridades, como el solape y la inclusión jerárquica de círculos. Los métodos habituales de detección de comunidades dejan de ser apropiados debido a estas características. La predicción se obtiene de dos fuentes de datos: el grafo de la red y atributos de nodo correspondientes a características de los perfiles de los usuarios. En esta tesis, se comentan nuevas aproximaciones a la tarea y se presentan los resultados obtenidos a partir de una investigación exhaustiva. Se proporciona una revisión del estado del arte en los campos de detección de comunidades en grafos, detección de comunidades en redes sociales y detección de círculos sociales. Se describen los conjuntos de datos utilizados en los experimentos, ambos extraídos de Facebook, y se diseñan diversas representaciones de los datos, tanto de la información estructural de la red como de la información procedente de los perfiles de los usuarios. Se definen y comentan las técnicas de predicción empleadas en nuestro trabajo: multi-assignment clustering, restricted Boltzmann machines y k-medias. Se describen algunas medidas de evaluación propuestas para la detección de círculos sociales y se incluye un comentario crítico sobre algunas de ellas, puesto que presentan algunos defectos conducentes a un comportamiento óptimo degenerado. El núcleo de este trabajo es la presentación de los experimentos que se han diseñado, junto con los resultados que se han obtenido. Dos grupos de experimentos se han llevado a cabo, dependiendo de la técnica de predicción empleada: en el primer grupo se ha utilizado el multi-assignment clustering, una técnica de análisis de conglomerados que permite la clasificación de un elemento en varios conglomerados diferentes; para el segundo grupo se ha utilizado un método en dos etapas por el que los vectores de datos se proyectan por medio de restricted Boltzmann machines antes de ser clasificados por un algoritmo k-medias. Se facilita un comentario de los resultados, que han sido satisfactorios y han llevado a la publicación de dos artículos, mientras un tercero está esperando la revisión. Nuestro trabajo abre nuevas líneas de trabajo futuro.Alonso Nanclares, JA. (2015). Advances in Social Circles. http://hdl.handle.net/10251/61558Archivo delegad

A review of research into the development of radiologic expertise: Implications for computer-based training

Rationale and Objectives. Studies of radiologic error reveal high levels of variation between radiologists. Although it is known that experts outperform novices, we have only limited knowledge about radiologic expertise and how it is acquired.Materials and Methods. This review identifies three areas of research: studies of the impact of experience and related factors on the accuracy of decision-making; studies of the organization of expert knowledge; and studies of radiologists' perceptual processes.Results and Conclusion. Interpreting evidence from these three paradigms in the light of recent research into perceptual learning and studies of the visual pathway has a number of conclusions for the training of radiologists, particularly for the design of computer-based learning programs that are able to illustrate the similarities and differences between diagnoses, to give access to large numbers of cases and to help identify weaknesses in the way trainees build up a global representation from fixated regions

Data Mapping by Restricted Boltzmann Machines for Social Circles Detection

©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Social 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 a two-step technique, making emphasis on the mapping of the data by Restricted Boltzmann Machines (RBMs). Social circles are subsequently inferred by k-means over the preprocessed data. We define different vectorial representations from both structural egonet information and user profile features, and perform a set of tests to adjust the optimal parameters of the RBMs. We study and compare the performance on the ego-Facebook dataset of social circles from Facebook from the Stanford Large Network Dataset Collection. 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), funded by the US Army Research Office (ARO).Alonso Nanclares, JA.; Paredes Palacios, R.; Rosso, P. (2015). Data Mapping by Restricted Boltzmann Machines for Social Circles Detection. IEEE. https://doi.org/10.1109/IJCNN.2015.7280653

Perceptual Pluralism

Perceptual systems respond to proximal stimuli by forming mental representations of distal stimuli. A central goal for the philosophy of perception is to characterize the representations delivered by perceptual systems. It may be that all perceptual representations are in some way proprietarily perceptual and differ from the representational format of thought (Dretske 1981; Carey 2009; Burge 2010; Block ms.). Or it may instead be that perception and cognition always trade in the same code (Prinz 2002; Pylyshyn 2003). This paper rejects both approaches in favor of perceptual pluralism, the thesis that perception delivers a multiplicity of representational formats, some proprietary and some shared with cognition. The argument for perceptual pluralism marshals a wide array of empirical evidence in favor of iconic (i.e., image-like, analog) representations in perception as well as discursive (i.e., language-like, digital) perceptual object representations