Egomunities, Exploring Socially Cohesive Person-based Communities

In the last few years, there has been a great interest in detecting overlapping communities in complex networks, which is understood as dense groups of nodes featuring a low outbound density. To date, most methods used to compute such communities stem from the field of disjoint community detection by either extending the concept of modularity to an overlapping context or by attempting to decompose the whole set of nodes into several possibly overlapping subsets. In this report we take an orthogonal approach by introducing a metric, the cohesion, rooted in sociological considerations. The cohesion quantifies the community-ness of one given set of nodes, based on the notions of triangles - triplets of connected nodes - and weak ties, instead of the classical view using only edge density. A set of nodes has a high cohesion if it features a high density of triangles and intersects few triangles with the rest of the network. As such, we introduce a numerical characterization of communities: sets of nodes featuring a high cohesion. We then present a new approach to the problem of overlapping communities by introducing the concept of ego-munities, which are subjective communities centered around a given node, specifically inside its neighborhood. We build upon the cohesion to construct a heuristic algorithm which outputs a node's ego-munities by attempting to maximize their cohesion. We illustrate the pertinence of our method with a detailed description of one person's ego-munities among Facebook friends. We finally conclude by describing promising applications of ego-munities such as information inference and interest recommendations, and present a possible extension to cohesion in the case of weighted networks

Triangles to Capture Social Cohesion

Although community detection has drawn tremendous amount of attention across the sciences in the past decades, no formal consensus has been reached on the very nature of what qualifies a community as such. In this article we take an orthogonal approach by introducing a novel point of view to the problem of overlapping communities. Instead of quantifying the quality of a set of communities, we choose to focus on the intrinsic community-ness of one given set of nodes. To do so, we propose a general metric on graphs, the cohesion, based on counting triangles and inspired by well established sociological considerations. The model has been validated through a large-scale online experiment called Fellows in which users were able to compute their social groups on Face- book and rate the quality of the obtained groups. By observing those ratings in relation to the cohesion we assess that the cohesion is a strong indicator of users subjective perception of the community-ness of a set of people

Maximizing the Cohesion is NP-hard

We show that the problem of finding a set with maximum cohesion in an undirected network is NP-hard.Comment: No. RR-7734 (2011

Biais dans les mesures obtenues par un réseau de capteurs sans fil

International audienceIn the area of complex networks, research has been stimulated by the availability of important data sets obtained through automatic measurement. In this article, we focus on interaction data in a hospital, gathered through the use of a wireless sensor network. We highlight the bias introduced by the measurement system and propose a method to reconstruct the original signal which evidences phenomenon which were not visible on the raw data

Finding cohesive communities with C³

Social communities have drawn a lot of attention in the past decades. We have previously introduced and validated the use of the cohesion, a graph metric which quantitatively captures the community-ness in a social sense of a set of nodes in a graph. Here we show that the problem of maximizing this quantity is NP-Hard. Furthermore, we show that the dual problem of minimizing this quantity, for a fixed set size is also NP-Hard. We then propose a heuristic to optimize the cohesion which we apply to the graph of voting agreement between U.S Senators. Finally we conclude on the validity of the approach by analyzing the resulting agreement communities.Les communautés sociales ont attiré beaucoup d'attention ces dernières années. Nous avions précédemment proposé et validé l'utilisation de la cohésion, une métrique de graphe qui capture quantitativement la qualité communautaire, au sens social, d'un ensemble de sommets d'un graphe. Nous montrons que le problème de trouver un ensemble de cohésion maximum dans un graphe non orienté est NP-dur. Par ailleurs, nous montrons que le problème dual de minimiser cette quantité, pour une taille donnée, est aussi NP-dur. Nous proposons ensuite une heuristique pour optimiser la cohésion que nous appliquons au graph d'agrément de vote entre Sénateurs des États-Unis. Finalement nous concluons sur la validité de l'approche en analysant les communautés résultantes

Communautés : Arrêtons de ne compter que les arêtes

International audienceDans cet article, nous souhaitons revenir sur la question de la définition d'une communauté en tant qu'ensemble de sommets U sans avoir à en juger la qualité au regard des autres communautés, recouvrantes ou non. Ce qui importe c'est uniquement l'ensemble U considéré et le graphe sous-jacent et ce indépendamment de tout découpage global. À dessein, nous introduisons la " cohésion " qui repose sur la relation forte qui existe entre des triplets de sommets lorsqu'ils forment un triangle ou au contraire sur la non présence de triangle traduisant la présence de lien faible (notion de "weak tie" introduite par A. Rapoport en 1957 et reprise par M.S. Granovetter en 1973). La notion de communauté découle de cette mesure confinée à un sous-ensemble de sommets plongé dans son graphe d'origine : une communauté est un ensemble de sommets offrant une forte cohésion. Après avoir introduit la métrique de cohésion, nous illustrons son application sur la découverte de communautés egocentrées dans des réseaux sociaux en utilisant un algorithme se basant sur la cohésion et donnons quelques résultats sur l'application de ce calcul d'egomunautés

Reconstructing Social Interactions Using an unreliable Wireless Sensor Network

International audienceIn the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed for their analysis. These works have led to the identification of similarities in the structures of such networks arising in very different fields, and to the development of a body of knowledge, tools and methods for their study. While many interesting questions remain open on the subject of static networks, challenging issues arise from the study of dynamic networks. In particular, the measurement, analysis and modeling of social interactions are first class concerns. In this article, we address the challenges of capturing physical proximity and social interaction by means of a wireless network. In particular, as a concrete case study, we exhibit the deployment of a wireless sensor network applied to the measurement of Health Care Workers' exposure to tuberculosis infected patients in a service unit of the Bichat-Claude Bernard hospital in Paris, France. This network has continuously monitored the presence of all HCWs in all rooms of the service during a 3 month period. We both describe the measurement system that was deployed and some early analysis on the measured data. We highlight the bias introduced by the measurement system reliability and provide a reconstruction method which not only leads to a significantly more coherent and realistic dataset but also evidences phe- nomena a priori hidden in the raw data. By this analysis, we suggest that a processing step is required prior to any adequate exploitation of data gathered thanks to a non-fully reliable measurement architecture

Electronic Sensors for Assessing Interactions between Healthcare Workers and Patients under Airborne Precautions

International audienceBackground: Direct observation has been widely used to assess interactions between healthcare workers (HCWs) and patients but is time-consuming and feasible only over short periods. We used a Radio Frequency Identification Device (RFID) system to automatically measure HCW-patient interactions. Methods: We equipped 50 patient rooms with fixed sensors and 111 HCW volunteers with mobile sensors in two clinical wards of two hospitals. For 3 months, we recorded all interactions between HCWs and 54 patients under airborne precautions for suspected (n=40) or confirmed (n=14) tuberculosis. Number and duration of HCW entries into patient rooms were collected daily. Concomitantly, we directly observed room entries and interviewed HCWs to evaluate their self- perception of the number and duration of contacts with tuberculosis patients. Results: After signal reconstruction, 5490 interactions were recorded between 82 HCWs and 54 tuberculosis patients during 404 days of airborne isolation. Median (interquartile range) interaction duration was 2.1 (0.8-4.4) min overall, 2.3 (0.8-5.0) in the mornings, 1.8 (0.8-3.7) in the afternoons, and 2.0 (0.7-4.3) at night (P,1024). Number of interactions/day/HCW was 3.0 (1.0-6.0) and total daily duration was 7.6 (2.4-22.5) min. Durations estimated from 28 direct observations and 26 interviews were not significantly different from those recorded by the network. Conclusions: The RFID was well accepted by HCWs. This original technique holds promise for accurately and continuously measuring interactions between HCWs and patients, as a less resource-consuming substitute for direct observation. The results could be used to model the transmission of significant pathogens. HCW perceptions of interactions with patients accurately reflected reality

Une théorie quantitative de la cohésion sociale

Community, a notion transversal to all areas of Social Network Analysis, has drawn tremendous amount of attention across the sciences in the past decades. Numerous attempts to characterize both the sociological embodiment of the concept as well as its observable structural manifestation in the social network have to this date only converged in spirit. No formal consensus has been reached on the quantifiable aspects of community, despite it being deeply linked to topological and dynamic aspects of the underlying social network. Presenting a fresh approach to the evaluation of communities, this thesis introduces and builds upon the cohesion, a novel metric which captures the intrinsic quality, as a community, of a set of nodes in a network. The cohesion, defined in terms of social triads, was found to be highly correlated to the subjective perception of communitiness through the use of a large-scale online experiment in which users were able to compute and rate the quality of their social groups on Facebook. Adequately reflecting the complexity of social interactions, the problem of finding a maximally cohesive group inside a given social network is shown to be NP-hard. Using a heuristic approximation algorithm, applications of the cohesion to broadly different use cases are highlighted, ranging from its application to network visualization, to the study of the evolution of agreement groups in the United States Senate, to the understanding of the intertwinement between subjects' psychological traits and the cohesive structures in their social neighborhood. The use of the cohesion proves invaluable in that it offers non-trivial insights on the network structure and its relation to the associated semantic.La notion de communauté, transverse à  l'analyse des réseaux sociaux, a attiré une attention grandissante à  travers les sciences ces dix dernières années. Les nombreuses tentatives pour modéliser aussi bien l'incarnation sociologiquedu concept aussi bien que sa manifestation structurelle dans le réseau social n'ont jusqu'à  présent que vaguement convergé. Aucun consensus formel n'a été atteint sur les aspects quantifiables de la communauté, et ceci malgré lesliens forts la reliant aux dimensions dynamique et topologique du réseau sous-jacent.Présentant une approche novatrice à  l'évaluation des communautés, cette thèse introduit et se base sur la cohésion, une métrique qui capture la qualitéintrinsèque, en tant que communauté, d'un ensemble de sommets dans un réseau. Il a été montré au travers d'une experience à  large échelle, dans laquelle les individus sondés ont pu noter l'aspect communautaires de groupes d'amis leur étant présentés, que la cohésion, définie en lien avec la notion de triades sociales, est fortement correlée à  la perception subjective de la communauté. Reflétant la complexité des interactions sociales, il est démontré que leproblème de trouver des communautés maximalement cohésive est NP-dur. En utilisant une heuristique approximant les résultats de ce problème, un certain nombre d'applications de la cohésion à  des données réelles sont mises en avant: de son application à  la visualisation de réseaux complexes, à  l'étude de l'évolution des groupes d'agrément du sénat états-unien, à  la compréhesion des liens entre psychologie et structure du réseau social.L'utilisation de la cohésion apporte un éclairage non trivial dans l'étude de la structure des grands réseaux de terrain et dans la relation entre structure et sémantique