Institute for Advanced Simulation (IAS)
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Institute for Advanced Simulation (IAS)
SimLab Contact
SimLab Team
Analysis, Visualization and Learning
A vast number of fields in science and engineering, including neuroscience, have benefitted from graph theoretic models. Insights are gained by modeling a system or data as a graph/network, and then performing analyses to identify interesting properties and relations. Such analyses are often based on algorithms borrowed from network science and/or computer science.
In terms of time and effort, this process can be very expensive due to the following reasons:
In addition, there is the monetary aspect, as some of the most commonly used existing tools for graph analysis in neuroscience rely on software with a proprietary license.
Therefore, a gap exists for a freetouse tool that minimizes programming and test effort.
The Visualizationguided Interactive Graph Analysis (VIGAN) tool is designed to minimize the programming effort required for the exploratory analysis of graph models. We aim to achieve this by providing as many of the standard network analysis algorithms outofthebox as possible. Moreover, much of the functionality of VIGAN utilizes the pointandclick method, which alleviates the need to write complex scripts. Also, VIGAN is being developed as free software.
The key features that make VIGAN stand out are:
Consider a scenario where one would like to study functional connectivity using fMRI data. Let us assume that in this case a single vertex in a graph represents aggregated functional activity in a particular region of the brain, while an edge represents the correlation of activity between two regions.
Figure 1: (Topleft): A fully connected weighted graph loaded from a GML file. (Topcenter): A subgraph created by applying the advanced filter on one of the vertex attributes of the graph on the left. The highlighted path is the shortest path between the two vertices at either end of the path. The labels in the subgraph correspond to the respective edge weights. (Topright) A scatter plot created to study the correlation between closeness centrality and weighted degree in the main graph. (Bottomleft): Tabular view for data in the main graph. (Bottomright): The advanced filter with Python code, used to create the subgraph. 
VIGAN allows graph data to be easily loaded via the Graphical User Interface (GUI), along with the option to perform one or more of the several available analyses. Once data has been loaded into the interactivevisualization application, it appears in the graph view (Figure 1, topleft), as well as in the tabular view (Figure 1, bottomleft). For each graph, the tabular view consists of two tables: 1) A table of vertex attributes, and 2) A table of edge attributes. Clicking a vertex in the graph view highlights the corresponding row in the vertex table, and vice versa.
The plot view (Figure 1, topright) offers the possibility to view data as an interactive chart. E.g., the correlation between closeness centrality and weighted degree in the graph can be viewed as a scatter plot (via a mere couple of mouse clicks). Clicking on a point in the plot highlights the corresponding vertex in the graph view and the corresponding row in the vertex table. This makes it possible to view the data from different perspectives on the same screen.
The bottomright panel in Figure 1 shows the advanced filter. Here, Python code can be used to retrieve and manipulate VIGAN’s data objects. E.g., a subgraph (Figure 1, topmiddle) can be created using a filter criterion to focus the analysis on a specific part of the graph. In addition to the Python filter, a filter based on user interface elements is available.
In the subgraph, shown in the topmiddle panel of Figure 1, the highlighted edges represent the shortest path between vertices on either end. Triggering the shortest path analysis is merely a matter of a couple of mouse clicks.
Note: For brevity, only a subset of available features has been presented in the above example.
We are actively developing VIGAN, and therefore look forward to hearing from interested users. Should you find VIGAN a potentially useful tool for your research, please do let us know.
This application is being developed in collaboration with the Computation in Neural Circuits group of the INM6.