A community based algorithm for deriving users' profiles from egocentrics networks: experiment on Facebook and DBLP

International audienceNowadays, social networks are more and more widely used as a solution for enriching users’ profiles in systems such as recommender systems or personalized systems. For an unknown user’s interest, the user’s social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the user’s social network and strongly depend on each author’s objective. To improve these techniques, we propose using a community-based algorithm that is applied to a part of the user’s social network (egocentric network) and that derives a user social profile that can be reused for any purpose (e.g., personalization, recommendation). We compute weighted user’s interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g., centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individual-based techniques and the influence of structural measures (related to communities) on the quality of derived profiles. A second experiment on DBLP and the author’s social network Mendeley confirms the results obtained on Facebook and shows the influence of the density of egocentrics network on the quality of results

Overlapping Community Discovery Methods: A Survey

The detection of overlapping communities is a challenging problem which is gaining increasing interest in recent years because of the natural attitude of individuals, observed in real-world networks, to participate in multiple groups at the same time. This review gives a description of the main proposals in the field. Besides the methods designed for static networks, some new approaches that deal with the detection of overlapping communities in networks that change over time, are described. Methods are classified with respect to the underlying principles guiding them to obtain a network division in groups sharing part of their nodes. For each of them we also report, when available, computational complexity and web site address from which it is possible to download the software implementing the method.Comment: 20 pages, Book Chapter, appears as Social networks: Analysis and Case Studies, A. Gunduz-Oguducu and A. S. Etaner-Uyar eds, Lecture Notes in Social Networks, pp. 105-125, Springer,201

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

Time-aware Egocentric network-based User Profiling

International audienc

Challenges in Community Discovery on Temporal Networks

International audienc

SONIC-MAN: A Distributed Protocol for Dynamic Community Detection and Management

International audienceThe study of complex networks has acquired great importance during the last years because of the diffusion of several phenomena which can be described by these networks. Community detection is one of the most investigated problem in this area, however only a few solutions for detecting communities in a distributed and dynamic environment have been presented. In this paper we propose SONIC-MAN, a distributed protocol to detect and manage communities in a peer-to-peer dynamic environment. Our approach is particularly targeted to distributed online social networks and its main goal is to discover communities in the ego-network of the users. SONIC-MAN is based on a Temporal Trade-off approach and exploits a set of super-peers for the management of the communities. The paper presents a set of evaluations proving that SONIC-MAN is able to detect dynamic communities in a distributed setting and to return results close a centralized approach based on the same basic algorithm for community discovering