Stable visualization of connected components in dynamic graphs

Abstract

One of the primary goals of many systems for the visual analysis of dynamically changing networks is to maintain the stability of the drawing throughout the sequence of graph changes. We investigate the scenario where the changes are determined by a stream of events, each being either an edge addition or an edge removal. The visualization must be updated immediately after each new event is received. Our main goal is to provide the user with an intuitive visualization that highlights the different connected components of the graph while preserving the user’s mental map after each event. The drawing stability is measured in terms of changes in the orthogonal relationships between vertices of two consecutive drawings. We describe two different visualization models, one for the 1-dimensional space and the other for the 2-dimensional space. In both models the connected components are drawn inside rectangular regions. To validate our approach, we report the results of an experimental analysis that compares the drawing stability of the online algorithm with that of an offline algorithm that knows in advance the whole sequence of events. We also present a case study of our online algorithm on a collaboration network.

Incremental Connected Component Detection for Graph Streams on GPU

“…Many studies have developed methods for quickly and accurately analyzing graph streams, in which numerous changes occur in real time [9][10][11]. Connected component (CC) detection is one of the most basic algorithms used in graph analysis [12][13][14][15][16]. A CC must have paths that can connect all vertices to each other in an undirected graph and must not have paths connected to vertices belonging to other CCs.…”