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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.

Our Goals

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 principal areas:

• novel algorithms for network clustering that are based on graph harmonic analysis and level-of-detail methods;
• the development of novel similarity measures for networks and network clusters for the purpose of comparing multiple network clusterings and the grouping (clustering) of different network clusterings; and
• a system for user-driven analysis of network clusterings supported by novel visual encodings and interaction techniques suitable for exploring dynamic networks and their clusterings in the presence of uncertainties due to noise and uncontrolled variations of network properties.

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.

While we are aiming to create tools for a variety of use cases, we specifically focus on two application areas &emdash; social networks such as Twitter as well as brain functional networks. These are two applications where the consortium has a lot of expertise, yet which are very different in terms of users and tasks. Hence, we hope to be able to generalize from these two specific applications.