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      Extraction and Analysis of Fictional Character Networks: A Survey

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          Abstract

          A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.

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          Assortative Mixing in Networks

          M. Newman (2002)
          A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.
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            Structural balance: a generalization of Heider's theory.

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              Communication Patterns in Task‐Oriented Groups

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                Author and article information

                Journal
                05 July 2019
                Article
                1907.02704
                fc2050af-e6f0-4e31-980a-3dd684f483d3

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                ACM Computing Surveys, Association for Computing Machinery, In press
                cs.SI cs.CL cs.CV cs.IR cs.MM
                ccsd

                Computer vision & Pattern recognition,Social & Information networks,Theoretical computer science,Information & Library science,Graphics & Multimedia design

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