IVAN is an interdisciplinary project aimed at making novel algorithms accessible to a broad range of users and researchers to enable reliable and informed decisions based on the network analysis under uncertainty.
The main goal of IVAN is to create a visual analysis system for the exploration of dynamic or time-dependent networks (from small to large scale). Our contributions will be in three principle areas:
Our aim is to make these novel algorithms accessible to a broad range of users and researchers to enable reliable and informed decisions based on the network analysis.
Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be ‘trivial’, i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in representing brain functional dynamics.
We propose a new approach-called PK-clustering-to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering approach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4) evaluates the consensus between user-selected algorithms and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often randomly selected black-box clustering algorithms.
Null models are necessary for assessing whether a dataset exhibits
non-trivial statistical properties. These models have recently gained
interest in the neuroimaging community as means to explore dynamic
properties of functional Magnetic Resonance Imaging (fMRI) time series.
Interpretation of null-model testing in this context may not be
straightforward because (i) null hypotheses associated to different null
models are sometimes unclear and (ii) fMRI metrics might be `trivial', i.e.
preserved under the null hypothesis, and still be useful in neuroimaging
applications. In this commentary, we review several commonly used null
models of fMRI time series and discuss the interpretation of the
corresponding tests. We argue that, while null-model testing allows for a
better characterization of the statistical properties of fMRI time series
and associated metrics, it should not be considered as a mandatory
validation step to assess their relevance in neuroimaging applications.
Six clinicians from the Geneva University Hospital, including medical doctors and epilepsy experts, participated in the ‘Creativity workshop on EEG and Epilepsy’. Through several activities guided by workshop facilitators, we identified several key elements where novel modelling and visualization tools could lead to better treatment of epilepsy. These included, e.g., better description of differences and commonalities between different forms of epilepsy, and more intuitive ways to navigate the complex spatio-temporal space of electroencephalography (EEG) data.
Our paper on 'Extending Recommendations for Creative Visualization-Opportunities Workshops’ has been accepted to BELIV 2020 'Evaluation and Beyond - Methodological Approaches for Visualization'.
Abstract: Participatory design is an approach in human-computer interaction that involves all relevant stakeholders coequally in the design process. A recent participatory method for visualization design is the creative visualization-opportunities (CVO) workshop, which is used to efficiently develop visualization design requirements in the early stages of applied visualization work. In this paper we report on our experiences of running four CVO workshops in different domains with diverse participants to explore new methods and variations of workshop variables. Through reflection on our experiences, we propose two contributions that extend existing guidance for planning, executing, and analyzing CVO workshops: a set of 12 pragmatic recommendations that extend and complement existing ones; and a recommended method for analyzing workshop results, called user stories. Additionally, we report on the outcomes of our successful workshops to provide evidence for the efficacy of CVO workshops.
The paper 'Community-Aware Graph Signal Processing' by Miljan Petrovic, Raphael Liegeois, Thomas A. W. Bolton, Dimitri Van De Ville has just been accepted for publication in the IEEE SPM Special Issue on Graph Signal Processing: Foundations and Emerging Directions.
Abstract: Graph signal processing (GSP) extends usual signal processing tools to data living on graphs. This is classically done by exploiting the structure of the graph Laplacian which encodes diffusive properties. In this work, in contrast, we define GSP based on the graph modularity matrix which encodes the graph community structure thereby making GSP 'community aware'. Methodological challenges coming with this new definition are first discussed, and applications are then presented.
Our paper on 'Revisiting correlation-based functional connectivity and its relationship with structural connectivity' has just been accepted in Network Neuroscience.
Abstract: In network neuroscience, functional connectivity (FC) denotes statistical dependencies between brain function in different regions. It is classically evaluated from pairwise correlations between functional time series. This FC measure captures both ‘direct’ and ‘indirect’ statistical dependencies which makes the comparison to structural connectivity (SC), which encodes only ‘direct’ anatomical connections, of limited relevance. In this paper we revisit the use of alternative FC measures yielding more natural SC-FC comparisons. In particular, the precision matrix, defined as the inverse of the correlation matrix, is shown to provide a better SC-FC match as compared to correlation-based FC. Then, using a simple model of brain structure and function interactions, we show that the SC-FC match can also be used to explore different aspects of brain anatomy and function.
Eight researchers from the field of digital humanities participated in our creativity workshop in Paris. They are working on the analysis of historic contract data. In this full-day workshop, we wanted to gather important information on how we can support the analysts in their daily work through visualization. This process consisted of a set of consecutive activities guided by workshop facilitators.
We have designed a visual representation for medium-sized (about 200-1000 vertices and 10-200 time steps) dynamic networks that support aggregation in time and topology. We are currently improving it to scale to larger sizes, but we are targeting around 10,000 vertices or a bit more depending on the amount of clustering we can do. These visualizations will be soon available on our tool at https://aviz.fr/paohvis.
The IVAN team presented the project's first year developments at the annual CHIST-ERA meeting in Bucharest on 3rd-4th April, 2019. The next annual meeting will be held in Prague in April 2020.
We have developed a new spectral representation of graphs based on the Slepian framework. By combining minimization of embedded distance and maximization of information concentration, this decomposition offers a novel way to explore networks that allows to zoom in specific subnetworks such as specific social networks communities or brain networks. You can find the Matlab code for the analysis presented in the paper on https://c4science.ch/source/guidedGSE/.
Researcher in Computer Science, Scientific Leader of the Inria Research Lab Aviz, also part of Université Paris-Saclay. My research is about exploring and understanding data through interactive visualizations enhanced with interactive analysis methods.
Head of Research Group Visualization and Data Analysis at University of Vienna, speaker of research platform Data Science @ Uni Vienna. My Research Interests are in Visualization, Computer Graphics, Image Processing, and Data Science.
Professor of Bioengineering at the EPFL and the University of Geneva. I pursue the development of methodological tools in signal and image processing to probe into network organization and dynamics, at various stages of the acquisition, processing, and analysis pipeline.
Working at Research Group Visualization and Data Analysis of the University of Vienna. My research interests are in Visualization, Data Science and Human-Computer Interaction with focus on Participatory Design.
Postdoctoral fellow with Prof. Dimitri Van De Ville at EPFL. I develop methods related to dynamical systems and graph theory to characterize brain function in healthy and diseased subjects. My interests include spectral graph theory, topological data analysis, time series modelling, brain connectivity, and fMRI.
Postdoc researcher at the the Aviz Group at INRIA. My research interests are network visualization, graph signal visualization, visual analytics, and graph signal processing.
Raphaël Liégeois, B.T. Thomas Yeo, Dimitri Van De Ville,
IN: NeuroImage, Volume 243, 2021https://doi.org/10.1016/j.neuroimage.2021.118518.
Alexis Pister, Paolo Buono, Jean-Daniel Fekete, Catherine Plaisant, Paola Valdivia
IN: Initiative Social Network Clustering. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers,
2021, 27 (2), pp.1775 - 1785.⟨10.1109/TVCG.2020.3030347⟩ ⟨hal-02566438⟩
Raphaël Liégeois, B. T. Thomas Yeo, Dimitri Van De Ville
C. Knoll, A. Çetin, T. Möller and M. Meyer
IN: 2020 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV), Salt Lake City, UT, USA, 2020, pp. 81-88https://doi.org/10.1109/BELIV51497.2020.00017
Miljan Petrovic, Thomas A. W. Bolton, Maria Giulia Preti, Raphaël Liégeois and Dimitri Van De Ville
IN: Network Neuroscience, Volume 3 | Issue 3 | 2019, p.807-826https://doi.org/10.1162/netn_a_00084
J. Casorso, X. Kong, W. Chi, D. Van De Ville, T. Yeo, and R. Liégeois.
IN: Neuroimage 194, pp. 42-54, 2019https://doi.org/10.1016/j.neuroimage.2019.03.019
R. Liégeois, J. Li, R. Kong, D. Van De Ville, T. Ge, M. Sabuncu and T. Yeo.
IN: Nature Communications 10 (1), 2317, 2019.https://doi.org/10.1038/s41467-019-10317-7
P. Valdivia, P. Buono, C. Plaisant, N. Dufournaud and J.-D. Fekete.
IN: IEEE Transactions on Visualization and Computer Graphics. In Press.https://doi.org/10.1109/TVCG.2019.2933196
R. Liégeois, I. Merad, and D. Van De Ville.
Wavelets and Sparsity XVIII 1113810, 2019.
Paola Valdivia, Paolo Buono, Catherine Plaisant, Nicole Dufournaud, Jean-Daniel Fekete.
VIS 2018 - 3rd Workshop on Visualization for the Digital Humanities, Oct 2018, Berlin, Germany.