5,213 research outputs found

    Advances in Social Circles

    Full text link
    [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

    Digging Deeper into Egocentric Gaze Prediction

    Full text link
    This paper digs deeper into factors that influence egocentric gaze. Instead of training deep models for this purpose in a blind manner, we propose to inspect factors that contribute to gaze guidance during daily tasks. Bottom-up saliency and optical flow are assessed versus strong spatial prior baselines. Task-specific cues such as vanishing point, manipulation point, and hand regions are analyzed as representatives of top-down information. We also look into the contribution of these factors by investigating a simple recurrent neural model for ego-centric gaze prediction. First, deep features are extracted for all input video frames. Then, a gated recurrent unit is employed to integrate information over time and to predict the next fixation. We also propose an integrated model that combines the recurrent model with several top-down and bottom-up cues. Extensive experiments over multiple datasets reveal that (1) spatial biases are strong in egocentric videos, (2) bottom-up saliency models perform poorly in predicting gaze and underperform spatial biases, (3) deep features perform better compared to traditional features, (4) as opposed to hand regions, the manipulation point is a strong influential cue for gaze prediction, (5) combining the proposed recurrent model with bottom-up cues, vanishing points and, in particular, manipulation point results in the best gaze prediction accuracy over egocentric videos, (6) the knowledge transfer works best for cases where the tasks or sequences are similar, and (7) task and activity recognition can benefit from gaze prediction. Our findings suggest that (1) there should be more emphasis on hand-object interaction and (2) the egocentric vision community should consider larger datasets including diverse stimuli and more subjects.Comment: presented at WACV 201

    RiskTrack: a new approach for risk assessment of radicalisation based on social media data

    Full text link
    Proceedings of the Workshop on Affective Computing and Context Awareness in Ambient Intelligence (AfCAI 2016) Murcia, Spain, November 24-25, 2016The RiskTrack project aims to help in the prevention of terrorism through the identi cation of online radicalisation. In line with the European Union priorities in this matter, this project has been designed to identify and tackle the indicators that raise a red ag about which individuals or communities are being radicalised and recruited to commit violent acts of terrorism. Therefore, the main goals of this project will be twofold: On the one hand, it is needed to identify the main features and characteristics that can be used to evaluate a risk situation, to do that a risk assessment methodology studying how to detect signs of radicalisation (e.g., use of language, behavioural patterns in social networks...) will be designed. On the other hand, these features will be tested and analysed using advanced data mining methods, knowledge representation (semantic and ontology engineering) and multilingual technologies. The innovative aspect of this project is to not offer just a methodology on risk assessment, but also a tool that is build based on this methodology, so that the prosecutors, judges, law enforcement and other actors can obtain a short term tangible results.This work has been supported by the RiskTrack project: "Tracking tool based on social media for risk assessment on radicalisation" under the EU Justice Action Grant: JUST-2015-JCOO-AG-72318

    Feature representation for social circles detection using MAC

    Full text link
    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
    corecore