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Showing 1 to 3 of 3 for “"Social network graph"”.
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Using Graph Clustering to Analyze the Spread of an Infectious Disease on a Random Large Social Network Graph
… of an infectious disease on a random large social network graph. The goal is to determine if graph clustering techniques are a viable option to reduce workload of analyzing of a large data set. A random graph generator was developed using characteristics from the Forest Fire Model. We then …
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Leaders, followers, and community detection
Communities in social interaction networks or graphs are sets of well-connected, and very often overlapping vertices. Formally, we view any maximal clique of the social network graph as a community. The problem of finding maximal cliques is known to be computationally hard. The goal of this work is …
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Uncovering latent structure in social networks using graph embeddings
The data from real-world social networks is huge, heterogeneous, and most of the attributes are unstructured. As a result, extensive investigation is necessary to transform this data into a useful and informative format. Failing to process these unstructured attributes results in a massive loss of …