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

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

A Tentative Design of a Future Internet Networking Domain Landscape

International audienceThe Future Internet (FI) will dramatically broaden both the spectrum of available information and the user's possible contexts and situations. This will lead to the vital need of a more efficient use of the Internet resources for the benefit of all. While the Internet has already delivered huge economic and social benefits over its short lifespan, there must be a realignment of how Internet research and investments are made and value is captured for enabling a continuous growth. The increase of available online contents and networking complexity require the exploration, experimentation and evaluation of new performance optimisation approaches for delivering different types of contents to users within different contexts and situations. Several network research areas, such as peer-to-peer, autonomous, cognitive and ad hoc networking, have already demonstrated how to improve network performance and user experience. Interestingly, there are various Internet-networking research areas and corresponding technologies that were investigated, experimented and progressively deployed, while others emerged more recently. However, there are still open questions such as visualising the conceptual evolution and articulating the various FI networking and computing research areas and identifying appropriate concepts populating such a FI domain landscape. This paper presents a tentative FI domain landscape populated by Internet computing and networking research areas

Local Community Detection in Social Networks

Recent years have witnessed the rapid growth of social network services and consequently research problems investigated in this area. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the group. Community detection includes exploring the community partitioning in social networks. Regarding the fact that social networks are huge, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. This problem can be defined as the detection of a community for a given node by using local information. It is noteworthy that the focus of this study is on the problem of local community detection. One major question to the problem of community detection is how to assess different communities. The most widely used technique to evaluate the quality of communities is to compare them with ground-truth communities. However, for many networks, the ground-truth communities are not known. As a result, it is necessary to have a comprehensive metric to evaluate the quality of communities. In this study, a local quality metric noted as GDM is proposed, several local community detection algorithms are compared by assessing their detected communities. The experimental results, illustrate that the local community detection algorithms are fairly compared using GDM. It is also discussed how GDM covers the drawbacks of other existing local metrics. Moreover, it is shown that the judgment of GDM is almost the same as that of the F-score, i.e. the metric which compares the community with its ground-truth community. Furthermore, a new metric, called P, and a new local community detection algorithm, Alg P are proposed. To detect communities locally, researchers mostly utilize an evaluation metric along with an algorithm to explore communities. The proposed algorithm includes three different steps in which relevant nodes are added in the first step and irrelevant nodes are removed in the second and third steps. It should be mentioned that at each iteration, more than one node is added to the community. Thus, the algorithm is terminated faster than the other algorithms with near-complexity. Regarding the experimental results, it is shown that the proposed algorithm outperforms state-of-the-art local community detection algorithms. Real-world social networks are dynamic and change over time. In order to model dynamic social networks, network history is partitioned into a series of snapshots, each one of which shows the state of the network at a time. Regarding dynamic networks, the problem of local community detection is not widely investigated. In this concern, a dynamic local community detection algorithm noted as DevDynaP, is proposed. The main feature of the proposed algorithm is that it starts from a given node, explores the network incrementally, and detects communities simultaneously at each snapshot. The experimental results show that the community partitioning resulting from the proposed dynamic algorithm outperforms that of the other compared algorithm. Also, the proposed algorithm explores the network faster than the compared algorithm. Many networks contain both positive and negative relations. A community in signed networks is defined as a group of nodes that are densely connected by positive links within the community and negative links between communities. Considering the problem of local community detection in signed networks, a new algorithm, noted as Alg SP, is developed by extending the metric $P$ for signed networks. Experimental results show that the proposed algorithm can detect the ground-truth communities independently from the starting nodes

Egomunities, Exploring Socially Cohesive Person-based Communities

International audienc