Mining and Modeling Character Networks

Bonato, Anthony; D’Angelo, D.; Elenberg, Ethan R.; Gleich, David F.; Hou, Yangyang

doi:10.1007/978-3-319-49787-7_9

Cited by 27 publications

(19 citation statements)

References 18 publications

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“…However, their output is not necessarily relevant to this specific type of network, and when it is, some of them are used to 34/75 Vincent Labatut and Xavier Bost objectively describe these networks without providing any specific interpretation (e.g. centrality measures for automatic classification [264] or motifs for model fitting [29]). In this section, we review the topological measures used in the literature to describe character networks, focusing only on those for which authors propose a character network-related interpretation.…”

Section: Descriptive Toolsmentioning

confidence: 99%

“…Most authors examining the average distance or diameter in character networks find small values, e.g. [8,29,53,154].…”

Section: Shortest Path and Distancementioning

confidence: 99%

“…The small world property mentioned before (Section 6.1.3) requires that, in addition to a small average distance, the considered network exhibits a high transitivity when compared to an equivalent random (Erdös-Rényi -ER) network [263]. A number of authors observe such a high transitivity in character networks [8,11,29,154,232]. A weighted generalization of the transitivity exists, which is occasionally used, e.g.…”

Section: /75mentioning

confidence: 99%

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Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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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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Section: Descriptive Toolsmentioning

confidence: 99%

“…Most authors examining the average distance or diameter in character networks find small values, e.g. [8,29,53,154].…”

Section: Shortest Path and Distancementioning

confidence: 99%

Section: /75mentioning

confidence: 99%

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Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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“…Another interesting line of inquiry is grasping the intricacies of a literary work or a movie to get insights into their narrative structure. Various works focused on finding common patterns across several plot lines [28], making sense of intricate character relationships [24], or just having fun trying to predict how the plot itself will evolve [66]. Usually, the character interaction network is modeled with a graph, where a vertex represents a storyline character, and an edge points out an interaction between two characters.…”

Section: Mining and Modeling Social Relationships: Game Of Thronesmentioning

confidence: 99%

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Spagnuolo¹,

Cordasco²,

Szufel³

et al. 2020

Internet Mathematics

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Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.

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“…co-occurrences of several identical notes in several pieces [6]. Finally, they have been applied to the visualization of character co-occurrences in novels and movies [4].…”

mentioning

confidence: 99%

Visualizing undirected graphs and symmetric square matrices as overlapping sets

Lamy

2019

Multimed Tools Appl

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Undirected graphs and symmetric square matrices are frequently found in various domains. An example is character co-occurrence matrices in digital humanities. However, the visualization of these datasets is difficult, especially if the graph is highly connected. In this article, we propose a method for visualizing undirected graphs and symmetric square matrices, by transforming them into overlapping sets, and then by visualizing these overlapping sets using set visualization techniques such as Euler diagram or rainbow boxes. We also propose a clustering approach to simplify the visualization.We apply this method to the visualization of various character co-occurrence matrices extracted from novels or DBpedia, ranging from 21 to 114 characters. We show that this visualization allows the finding of several interesting insights. Finally, we discuss the advantages and drawbacks of this method, and we compare it to other approaches in the literature.

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Mining and Modeling Character Networks

Cited by 27 publications

References 18 publications

Extraction and Analysis of Fictional Character Networks

Extraction and Analysis of Fictional Character Networks

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Visualizing undirected graphs and symmetric square matrices as overlapping sets

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