Abstract
Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems.
Highlights
Large scale complex networks are ubiquitous in the modern era
We provide a visualization of the weighted network (Wt) at timestamp t generated as a result of the tracking procedure
In this paper we proposed a visualization toolkit Netgram which can be used to depict the evolution of communities in dynamic networks over time
Summary
Large scale complex networks are ubiquitous in the modern era. Their presence spans a wide range of domains including social networks [1], biological networks [2], collaboration networks [3], trust networks [4] and communication networks [5]. These complex networks have a natural temporal aspect. Social networks evolve over time with addition and deletion of members, formation of friendships between people in different social circles or disappearance of friendship between people over time. A group of researchers working on a particular topic might collaborate intensively if they are working on an emerging topic whereas a group of researchers working together on an outdated topic might completely disappear over time.
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