Sébastien Heymann’s research while affiliated with Sorbonne University and other places

Gephi is a network visualization software used in various disciplines (social network analysis, biology, genomics…). One of its key features is the ability to display the spatialization process, aiming at transforming the network into a map, and ForceAtlas2 is its default layout algorithm. The latter is developed by the Gephi team as an all-around solution to Gephi users' typical networks (scale-free, 10 to 10,000 nodes). We present here for the first time its functioning and settings. ForceAtlas2 is a force-directed layout close to other algorithms used for network spatialization. We do not claim a theoretical advance but an attempt to integrate different techniques such as the Barnes Hut simulation, degree-dependent repulsive force, and local and global adaptive temperatures. It is designed for the Gephi user experience (it is a continuous algorithm), and we explain which constraints it implies. The algorithm benefits from much feedback and is developed in order to provide many possibilities through its settings. We lay out its complete functioning for the users who need a precise understanding of its behaviour, from the formulas to graphic illustration of the result. We propose a benchmark for our compromise between performance and quality. We also explain why we integrated its various features and discuss our design choices.

Complex networks may be studied in various ways, e.g., by analyzing the evolutions of their topologies over time, and in particular of their community structures. In this paper, we focus on another type of dynamics, related to diffusion processes on these networks. Indeed, our work aims at characterizing network dynamics from the diffusion point of view, and reciprocally, it evaluates the impact of graph dynamics on diffusion. We propose in this paper an innovative approach based on the notion of intrinsic time, where the time unit corresponds to the appearance of a new link in the graph. This original notion of time allows us to somehow isolate the diffusion phenomenon from the evolution of the network. The objective is to compare the diffusion features observed with this intrinsic time concept from those obtained with traditional (extrinsic) time, based on seconds. The comparison of these time concepts is easily understandable yet completely new in the study of diffusion phenomena. We experiment our approach on three real datasets and show the promising results of intrinsic time-based diffusion analysis.

Social bookmarking systems have recently received an increasing attention in both academic and industrial communities. This success is owed to their ease of use that relies on a simple intuitive process, allowing their users to label diverse resources with freely chosen keywords aka tags. The obtained collections are known under the nickname of Folksonomy. In this paper, we introduce a new approach dedicated to the visualization of large folksonomies, based on the ”intersecting” minimal transversals. The main thrust of such an approach is the proposal of a reduced set of ”key” nodes of the folksonomy from which the remaining nodes would be faithfully retrieved. Thus, the user could navigate in the folksonomy through a folding/unfolding process.

This paper describes the design of Knot, a digital tool for exploring historical social networks, developed within a multidisciplinary research context involving designers, humanities scholars and computer scientists. The goal of the tool is to provide scholars and researchers with an environment for exploring multi-dimensional and heterogeneous data, allowing them to discover and create explicit and implicit relationships between people, places and events. What distinguishes our approach to traditional network exploration and analysis is an emphasis on the construction of the network graph through the visual interface, rather than on its static observation. Knot aims to explore new opportunities for interface design and information visualization within the definition of novel research practices in the humanities, bringing together scholars, HCI, design, and computer science communities.

Diffusion phenomena occur in many kinds of real-world complex networks, e.g., biological, information or social networks. Because of this diversity, several types of diffusion models have been proposed in the literature: epidemiological models, threshold models, innovation adoption models, among others. Many studies aim at investigating diffusion as an evolving phenomenon but mostly occurring on static networks, and much remains to be done to understand diffusion on evolving networks. In order to study the impact of graph dynamics on diffusion, we propose in this paper an innovative approach based on a notion of intrinsic time, where the time unit corresponds to the appearance of a new link in the graph. This original notion of time allows us to isolate somehow the diffusion phenomenon from the evolution of the network. The objective is to compare the diffusion features observed with this intrinsic time concept from those obtained with traditional (extrinsic) time, based on seconds. The comparison of these time concepts is easily understandable yet completely new in the study of diffusion phenomena. We experiment our approach on synthetic graphs, as well as on a dataset extracted from the Github sofware sharing platform.

Platforms which combine data mining algorithms and interactive visualizations play a key role in the discovery process from complex networks data, e.g. Web and Online Social Networks data. Here we illustrate the use of Gephi, an open source software for networks visual exploration, for the visual analysis of Business Intelligence data modeled as complex networks.