Visualize also this second output file with 'Visualization > General > GnuPlot':
Community Detection algorithms look for subgraphs where nodes are highly interconnected among themselves and poorly connected with nodes outside the subgraph. Many community detection algorithms are based on the optimization of the modularity - a scalar value between -1 and 1 that measures the density of links inside communities as compared to links between communities. The Blondel Community Detection finds high modularity partitions of large networks in short time and that unfolds a complete hierarchical community structure for the network, thereby giving access to different resolutions of community detection.
Run 'Analysis > Networks > Weighted & Undirected > Blondel Community Detection' with the following parameter:
This will generate a network named 'With community attributes' in the Data Manager. To visualize this network run 'Visualization > Networks > Circular Hierarchy' with the following parameters:
18.104.22.168 Cited Reference Co-Occurrence (Bibliographic Coupling) Network