Complex Networks
Multi-scale visualization of a collaboration dataset
Many real-world networks can be represented as large graphs. Computational manipulation of such large graphs is common, but current tools for graph visualization are limited to datasets of a few thousand nodes.
These graphs contain sets of highly connected nodes that we call “communitiesâ€Â. Furthermore, these communities often have their own parts which are more connected than the rest that can be viewed as “sub-communitiesâ€Â. We used the Louvain method to extract communities and sub-communities from a sample network obtained from Arxiv dataset. We also used GUESS which is a graph exploration tool that contains an interpreted language (Gython) combined with a graphical front-end.
Using extracted hierarchical clustering dendrogram from Louvain method, we developed a tool which visualizes different hierarchical partitions of graph. Also, it allows us to manually merge and unmerge nodes into and from a community.
The plot shows the five levels of the decomposition, the smallest graph being the one between the communitiues whose decomposition maximizes the modularity according to Louvain method.
Multi-scale visualization of a collaboration dataset
Many real-world networks can be represented as large graphs. Computational manipulation of such large graphs is common, but current tools for graph visualization are limited to datasets of a few thousand nodes.
These graphs contain sets of highly connected nodes that we call “communitiesâ€Â. Furthermore, these communities often have their own parts which are more connected than the rest that can be viewed as “sub-communitiesâ€Â. We used the Louvain method to extract communities and sub-communities from a sample network obtained from Arxiv dataset. We also used GUESS which is a graph exploration tool that contains an interpreted language (Gython) combined with a graphical front-end.
Using extracted hierarchical clustering dendrogram from Louvain method, we developed a tool which visualizes different hierarchical partitions of graph. Also, it allows us to manually merge and unmerge nodes into and from a community.
The plot shows the five levels of the decomposition, the smallest graph being the one between the communitiues whose decomposition maximizes the modularity according to Louvain method.