Fuzzy Descriptions to Identify Temporal Substructure Changes of Cooccurrence Graphs
نویسندگان
چکیده
Cooccurrence graphs easily grow very dense when applied to represent binary association patterns of large amounts of data. Therefore, postprocessing is needed to extract valuable information from them. We propose an approach to identify subgraphs of cooccurrence graphs that show a certain temporal behavior. This behavior is described with linguistic variables and fuzzy connectives defined over the change rate domains of certain graph measures. These measures assess graph properties whose change over time the user is interested in. To justify our proposed method, we are going to present evidence from a real-world dataset. Keywords— Cooccurrence graphs, Temporal change, Fuzzy description
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