Visualization of large-scale weighted clustered graph: a genetic approach
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Visualizing large hierarchically clustered graphs with a landscape metaphor
GD'12 Proceedings of the 20th international conference on Graph Drawing
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Fast layout computation of clustered networks: Algorithmic advances and experimental analysis
Information Sciences: an International Journal
Hi-index | 0.00 |
Visualization of clustered graphs has been a research area since many years. In this paper, we describe a new approach that can be used in real application where graph does not contain only topological information but also extrinsic parameters (i.e. user attributes on edges and nodes). In the case of force-directed algorithm, management of attributes corresponds to take into account edge weights. We propose an extension of the GRIP algorithm in order to manage edge weights. Furthermore, by using Voronoi diagram we constrained that algorithm to draw each cluster in a non overlapping convex region. Using these two extensions we obtained an algorithm that draw clustered weighted graphs. Experimentation has been done on data coming from biology where the network is the genesproteins interaction graph and where the attributes are gene expression values from microarray experiments.