Improving Circular Layout Algorithm for Social Network Visualization Using Genetic Algorithm
Visualization is an important part of network analysis. It helps to find features of the network that are not easily identifiable. Graph data visualization tries to enable users to grasp difficult concepts or identify new patterns. One of the problems that prevents users from achieving this goal is the crossing number of a graph drawing. A crossing in graph data visualization is a point where two curves intersect. In the minimum crossing number problem, the goal is to find a drawing of G with minimum number of edge crossings. In particular, except for a few initial cases, the crossing number of graphs remains unknown. Circular layout is one of the graph drawing algorithms that is used for visualizing graph datasets. In this research we tried to minimize the number of edge crossings in a circular layout using genetic algorithm. The result is promising.
KeywordsVisualization Layout Genetic algorithm Minimum crossing number Social network analysis
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