Benjamin Renoust

Detangler: Visual Analytics for Multiplex Networks

A multiplex network has links of different types, allowing it to express many overlapping types of relationships. A core task in network analysis is to evaluate and understand group cohesion; that is, to explain why groups of elements belong together based on the underlying structure of the network. We present Detangler, a system that supports visual analysis of group cohesion in multiplex networks through dual linked views. These views feature new data abstractions derived from the original multiplex network: the substrate network and the catalyst network. We contribute two novel techniques that allow the user to analyze the complex structure of the multiplex network without the extreme visual clutter that would result from simply showing it directly. The harmonized layout visual encoding technique provides spatial stability between the substrate and catalyst views. The pivot brushing interaction technique supports linked highlighting between the views based on computations in the underlying multiplex network to leapfrog between subsets of catalysts and substrates. We present results from the motivating application domain of annotated news documents with a usage scenario and preliminary expert feedback. A second usage scenario presents group cohesion analysis of the social network of the early American independence movement.

Visual Analytics of Political Networks From Face-Tracking of News Video

The rich nature of news makes it a classic subject of visual analytics research. Such analysis is often based on rich textual data. However, we want to test how much we can understand the news from video information through face detection and tracking. Towards this goal, we propose a visual analytics system and discuss its design and implementation to support media experts in understanding political interactions in an archive of 12 years of the Japanese public broadcaster NHKs News 7 program. After identifying the tasks and abstraction required for our analysis, we construct links from face detection and tracking to derive multiple political networks. Our proposed design embeds this rich data into a visual analytics framework that presents four levels of abstraction: time period, network, timeline, and face-tracks within video. We present how the exploration of the archive with our system results in good understanding of the Japanese politico-media scene during these 12 years while finding evidence of “presidentialization” of the media.

Flows of Knowledge in Citation Networks

Knowledge is created and transmitted through generation. Innovation is often seen as a generative process from collective intelligence, but how does innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherit? A citation network can be seen as a perfect example of a generative process leading to innovation. Inspired by the notion of “stream of knowledge”, we propose to look at the question of production of knowledge under the lens of DAGs. Although many works look for the evaluation of publications, we propose to look for production of knowledge within a framework for analyzing DAGs. In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. We propose an efficient dynamic algorithm for integration with modern graph databases, conducting our experiment with the Arxiv HEP-TH dataset. Our results validate the use of DAG flows for citation flows and show evidence of the relevance of the h-index.

When face-tracking meets social networks: a story of politics in news videos

In the age of data processing, news videos are rich mines of information. After all, the news are essentially created to convey information to the public. But can we go beyond what is directly presented to us and see a wider picture? Many works already focus on what we can discover and understand from the analysis of years of news broadcasting. These analysis bring monitoring and understanding of the activity of public figures, political strategies, explanation and even prediction of critical media events. Such tools can help public figures in managing their public image, as well as support the work of journalists, social scientists and other media experts. News analysis can also be seen from the lens of complex systems, gathering many types of entities, attributes and interactions over time. As many public figures intervene in different news stories, a first interesting task is to observe the social interactions between these actors. Towards this goal, we propose to use video analysis to automatise the process of constructing social networks directly from news video archives. In this paper we are introducing a system deriving multiple social networks from face detections in news videos. We present preliminary results obtained from analysis of these networks, by monitoring the activity of more than a hundred public figures. We finally use these networks as a support for political studies and we provide an overview of the political landscape presented by the Japanese public broadcaster NHK over a decade of the 7 PM news archives.

Multiplex flows in citation networks

Knowledge is created and transmitted through generations, and innovation is often seen as a process generated from collective intelligence. There is rising interest in studying how innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherits. A citation network can be seen as a perfect example of one generative process leading to innovation. However, the impact and influence of scientific publication are always difficult to capture and measure. We offer a new take on investigating how the knowledge circulates and is transmitted, inspired by the notion of “stream of knowledge”. We propose to look at this question under the lens of flows in directed acyclic graphs (DAGs). In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. From this point on, we can take a finer look at the diffusion of knowledge through the lens of a multiplex network. In this network, each citation of a specific work constitutes one layer of interaction. Within our framework, we design three measures of multiplex flows in DAGs, namely the aggregated, sum and selective flow, to better understand how citations are influenced. We conduct our experiments with the arXiv HEP-Th dataset, and find insights through the visualization of these multiplex networks.

Testing for the signature of policy in online communities

Most successful online communities employ professionals, sometimes called “community managers”, for a variety of tasks including on boarding new participants, mediating conflict, and policing unwanted behaviour. We interpret the activity of community managers as network design: they take action oriented at shaping the network of interactions in a way conducive to their community’s goals. It follows that, if such action is successful, we should be able to detect its signature in the network itself. Growing networks where links are allocated by a preferential attachment mechanism are known to converge to networks displaying a power law degree distribution. Our main hypothesis is that managed online communities would deviate from the power law form; such deviation constitutes the signature of successful community management. Our secondary hypothesis is that said deviation happens in a predictable way, once community management practices are accounted for. We investigate the issue using empirical data on three small online communities and a computer model that simulates a widely used community management activity called on boarding. We find that the model produces in-degree distributions that systematically deviate from power law behavior for low-values of the in-degree; we then explore the implications and possible applications of the finding.

Exploring Temporal Communities in Mass Media Archives

One task key to the analysis of large multimedia archive over time is to dynamically monitor the activity of concepts and entities with their interactions. This is helpful to analyze threads of topics over news archives (how stories unfold), or to monitor evolutions and development of social groups. Dynamic graph modeling is a powerful tool to capture these interactions over time, while visualization and finding communities still remain difficult, especially with a high density of links. We propose to extract the backbone of dynamic graphs in order to ease community detection and guide the exploration of trends evolution. Through the graph structure, we interactively coordinate node-link diagrams, Sankey diagrams, time series, and animations in order to extract patterns and follow community behavior. We illustrate our system with the exploration of the role of soccer in 6 years of TV/radio magazines in France, and the role of North Korea in about 10 years of Japanese news.

Estimating political leanings from mass media via graph-signal restoration with negative edges

Politicians in the same political party often share the same views on social issues and legislative agendas. By mining patterns in TV news co-appearances and Twitter followers, in this paper we estimate political leanings (left / right) of unknown individuals, and detect outlier politicians who have views different from their colleagues in the same party, from a graph signal processing (GSP) perspective. Specifically, we first construct a similarity graph with politicians as nodes, where a positive edge connects two politicians with sizable shared Twitter followers, and a negative edge connects two politicians appearing in the same TV news segment (and thus likely take opposite stands on the same issue). Given a graph with both positive and negative edges, we propose a new graph-signal smoothness prior based on a constructed generalized graph Laplacian matrix that is guaranteed to be positive semi-definite. We formulate a graph-signal restoration problem that can be solved in closed form. Experimental results show that political leanings of unknown individuals can be reliably estimated and outlier politicians can be detected.

Online community management as social network design: testing for the signature of management activities in online communities

Online communities are used across several fields of human activities, as environments for large-scale collaboration. Most successful ones employ professionals, sometimes called “community managers” or “moderators”, for tasks including onboarding new participants, mediating conflict, and policing unwanted behaviour. Network scientists routinely model interaction across participants in online communities as social networks. We interpret the activity of community managers as (social) network design: they take action oriented at shaping the network of interactions in a way conducive to their community’s goals. It follows that, if such action is successful, we should be able to detect its signature in the network itself.Growing networks where links are allocated by a preferential attachment mechanism are known to converge to networks displaying a power law degree distribution. Growth and preferential attachment are both reasonable first-approximation assumptions to describe interaction networks in online communities. Our main hypothesis is that managed online communities are characterised by in-degree distributions that deviate from the power law form; such deviation constitutes the signature of successful community management. Our secondary hypothesis is that said deviation happens in a predictable way, once community management practices are accounted for. If true, these hypotheses would give us a simple test for the effectiveness of community management practices.We investigate the issue using (1) empirical data on three small online communities and (2) a computer model that simulates a widely used community management activity called onboarding. We find that onboarding produces in-degree distributions that systematically deviate from power law behaviour for low-values of the in-degree; we then explore the implications and possible applications of the finding.

News Archive Exploration Combining Face Detection and Tracking with Network Visual Analytics

Visual analytics helps analytical reasoning and exploration of complex systems, for which it combines the means of interactive visualization, with the power of data analytics. The recent progress in computer vision techniques opens wide applications in real world video archives. Particularly, recent advances in face detection and recognition have been put under the spotlight. The applications of such techniques are often concern intelligence, or peer recognition in photo posted in social networks. We propose to combine those two domains by demonstrating a visual exploration of over a decade of the news program from the Japanese broadcaster NHK News 7. We derive social networks from face detection and tracking of this large dataset. With the help of a little domain knowledge, we monitor the activity of political public figures and explore the archive. This allows understanding and comparison of the politico-media scene presented by NHK under different Prime Ministers governance. The social networks are interactive, and also allow to explore the multimedia database and explore its video content.