Frédéric Gilbert

Communities and hierarchical structures in dynamic social networks: analysis and visualization

Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals who play important roles in a society such as managers and decision makers. Detection and visualization of these networks that are changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it. In this paper, we address these issues by presenting a system to analyze dynamic social networks. The proposed system is based on dynamic graph discretization and graph clustering. The system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in social networks. We use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social networks.

Detecting Structural Changes and Command Hierarchies in Dynamic Social Networks

Community detection in social networks varying with time is a common yet challenging problem whereby efficient visualization of evolving relationships and implicit hierarchical structure are important task. The main contribution of this paper is towards establishing a framework to analyze such social networks. The proposed framework is based on dynamic graph discretization and graph clustering.The framework allows detection of major structural changes over time, identifies events analyzing temporal dimension and reveals command hierarchies in social networks.We use the Catalano/Vidro dataset for empirical evaluation and observe that our framework provides a satisfactory assessment of the social and hierarchical structure present in the dataset.

Zenon Modulo: When Achilles Outruns the Tortoise Using Deduction Modulo

We propose an extension of the tableau-based first order automated theorem prover Zenon to deduction modulo. The theory of deduction modulo is an extension of predicate calculus, which allows us to rewrite terms as well as propositions, and which is well suited for proof search in axiomatic theories, as it turns axioms into rewrite rules. We also present a heuristic to perform this latter step automatically, and assess our approach by providing some experimental results obtained on the benchmarks provided by the TPTP library, where this heuristic is able to prove difficult problems in set theory in particular. Finally, we describe an additional backend for Zenon that outputs proof certificates for Dedukti, which is a proof checker based on the λΠ-calculus modulo.

From Databases to Graph Visualization

The first step of any information visualization system is to enable end user to import their dataset into the system. However, non expert user are faced to the difficult task of choosing how their data should/could be transform to be used in these Infovis systems. In that paper we address the case where end users want to use dataset in tabular format. We propose a novel method for automatic graph generation from these datasets. That method consists in first building taxonomy of dimensions. Then, that taxonomy is used to provide to user a system that enables to interactively navigate into the set of possible data transformation.

Solving the traffic and flitter challenges with tulip

We present our visualization systems and findings for the badge and network traffic as well as the social network and geospatial challenges of the 2009 VAST contest. The summary starts by presenting an overview of our time series encoding of badge information and network traffic. Our findings suggest that employee 30 may be of interest. In the second part of the paper, we describe our system for finding subgraphs in the social network subject to degree constraints. Subsequently, we present our most likely candidate network which is similar to scenario B.