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Discovering Chance Scenarios using Small-World KeyGraphs and Evolutionary Computation

A successful process of chance discovery using the visual maps proposed by KeyGraphs requires the usage of graphs with an appropriate degree of complexity. Complex KeyGraphs often prevent users from discovering chances because of the difficulties of interpretation. On the other hand, overly simplistic KeyGraphs seldom includes a chance because of the sparseness of information. In a useful KeyGraphs the concept clusters should be easy to find, the clusters should be easy to understand, and the relations among them should be easy to comprehend and help in the process of chance identification. This paper systematize the process of KeyGraph exploration by means of evolutionary computation, as well as structural graph properties—such as small-world topologies. The proposed techniques are successfully applied to create useful KeyGraphs for chance discovery from several documents.