Research and Implementation of Network Data Visualization Optimization Method Based on Overlapping Community Discovery
With the surge in data volume,the relationship between data has become intricate and complicated,which brings challenges to network visualization.Through community discovery,highlighting the local clustering characteristics in the network can improve the visualization effect,and the discovery of overlapping communities can be closer to the actual network structure.The Louvain algorithm with simple,efficient and fast execution speed is currently one of the most commonly used community discovery algorithms,but the discovery of overlapping communities is its shortcoming.To this end,the paper is based on the Louvain algo-rithm,combined with the fuzzy C-means clustering algorithm based on spectral mapping to improve the community discovery algo-rithm.The improved algorithm uses spectral mapping to map data nodes to Euclidean space,the degree of membership is used to calculate the degree to which a data node belongs to a certain cluster,which allows the same data to belong to multiple different classes,thereby realizing the discovery of overlapping community structures.Finally,based on the proposed algorithm,the FR mod-el in the mainstream layout algorithm is used to visualize the network data.Using the modularity value as an evaluation indicator,the experimental results show that the method proposed in the paper can find overlapping communities and can clearly show the com-munity structure in the network,compared with the traditional overlapping community discovery algorithms COPRA and CPM on the classic data set,modularity values is improved.
community discoveryLouvain algorithmfuzzy clustering methodlayout algorithmgraph visualization
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