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In this paper, we present an algorithm for extracting keywords representing the asserted main point in a document, without relying on external devices such as natural language processing tools or a document corpus. Our algorithm KeyGraph is based on the seg-mentation of a graph, representing the co-occurrence between terms in a document, into clusters. Each(More)
—Experts of chance discovery have recognized a new class of problems where the previous methods fail to visualize a latent structure behind observation. There are invisible events that play an important role in the dynamics of visible events. An invisible leader in a communication network is a typical example. Such an event is named a dark event. A novel(More)
The small world topology is known widespread in biological, social and man-made systems. This paper shows that the small world structure also exists in documents, such as papers. A document is represented by a network; the nodes represent terms, and the edges represent the co-occurrence of terms. This network is shown to have the characteristics of being(More)
The small world topology is known widespread in biological, social and man-made systems. This paper shows that the small world structure also exists in documents , such as papers. A document is represented b y a network; the nodes represent terms, and the edges represent the co-occurrence of terms. This network is shown to have the characteristics of being(More)
In this paper, we see whether chance discovery in the form of KeyGraphs can be used to reveal deep building blocks to competent genetic algorithms, thereby speeding innovation in particularly difficult problems. On an intellectual level, showing the connection between Key-Graphs and genetic algorithms as related pieces of the innovation puzzle is both(More)