Clustered Graphs Visualization Techniques

Question

I need to visualize a relatively large graph (6K nodes, 8K edges) that has the following properties:

• Distinct Clusters. Approximately 50-100 Nodes per cluster and moderate interconnectivity at the cluster level
• Minimal (5-10 inter-cluster edges per cluster) interconnectivity between clusters

Let global edge overlap = The edge overlaps caused by directly visualizing a graph of Clusters = {A, B, C, D, E}, Edges = {Pentagram of those clusters, which is non-planar by the way and will definitely generate edge overlap if you draw it out directly}

Let Local Edge Overlap = the above but { A, B, C, D, E } are just nodes.

I need to visualize graphs with the above in a way that satisfies the following requirements

• No global edge overlap (i.e. edge overlaps caused by inter-cluster properties is not okay)
• Local edge overlap within a cluster is fine

Anyone have thoughts on how to best visualize a graph with the requirements above?

One solution I've come up with to deal with the global edge overlap is to make sure a cluster A can only have a max of 1 direct edge to another cluster (B) during visualization. Any additional inter-cluster edges between cluster A -> C, A -> D, ... are disconnected and additional node/edges A -> A_C, C -> C_A, A -> A_D, D -> D_A... are created.

Anyone have any thoughts?

Answer 1

Prefuse has some good graph drawing link text algorithms built in and it seems to handle fairly large graphs relatively well. You might try Flow Map Layout which is built on top of Prefuse.

Answer 2

Given your objectives, I think that the Fruchterman-Reingold algorithm does a pretty decent job of preventing edge overlap. See for example this screenshot of a network consisting of multiple components drawn using the Fruchterman-Reingold algorithm. IGraph has built-in support for this algorithm (as does Networkx I believe) and is really fast.