Entanglement in Multiplex Networks: Understanding Group Cohesion in Homophily Networks

Renoust, Benjamin; Mélançon, Guy; Viaud, Marie-Luce

doi:10.1007/978-3-319-12188-8_5

Cited by 22 publications

(38 citation statements)

References 37 publications

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“…Multiplex networks have been used to model citation networks mainly for clustering purpose (Boden et al 2012; Dong et al 2012; Renoust et al 2014; Speidel et al 2015). Boden et al (2012) model the multiplex network from the keyword-publication associations in order to create clusters of publications of similar topic.…”

Section: Related Workmentioning

confidence: 99%

“…Dong et al (2012) share the same goal but mixes different layers from research domains, or different similarity links across the papers (diverse textual similarities, author similarity, and citations). Renoust et al (2014) use an approach closer to Boden’s, with the different goal to find cohesive groups of co-authors in an author-publication network. Analysis of multiplex DAGs has also recently been proposed by Speidel et al (2015) using the same arXiv HEP-Th dataset that we use, with the difference that layers are defined by year of publication.…”

Section: Related Workmentioning

confidence: 99%

“…The multiplex definition of our citation network allows us many interesting studies, one of each is the analysis of edge entanglement (Renoust et al 2014). Edge entanglement measures the overlapping of edges in a multiplex network.…”

Section: The Citation Network As a Multiplex Networkmentioning

confidence: 99%

“…It offers interaction in the multiplex network allowing to identify groups of particular nodes and their associated layers — as well as the other way around, particular groups of layers with their associated nodes (Renoust et al 2015). Detangler also offers to study many other aspects of the multiplex network by changing metrics, taking weights into account, and measuring global cohesion of groups of publications (Renoust et al 2014). For the sake of simplicity, we will only look at the exploration of the layers in association with citing articles as it already brings insights.…”

Section: The Citation Network As a Multiplex Networkmentioning

confidence: 99%

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Multiplex flows in citation networks

2017

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Knowledge is created and transmitted through generations, and innovation is often seen as a process generated from collective intelligence. There is rising interest in studying how innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherits. A citation network can be seen as a perfect example of one generative process leading to innovation. However, the impact and influence of scientific publication are always difficult to capture and measure. We offer a new take on investigating how the knowledge circulates and is transmitted, inspired by the notion of “stream of knowledge”. We propose to look at this question under the lens of flows in directed acyclic graphs (DAGs). In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. From this point on, we can take a finer look at the diffusion of knowledge through the lens of a multiplex network. In this network, each citation of a specific work constitutes one layer of interaction. Within our framework, we design three measures of multiplex flows in DAGs, namely the aggregated, sum and selective flow, to better understand how citations are influenced. We conduct our experiments with the arXiv HEP-Th dataset, and find insights through the visualization of these multiplex networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Citation Network As a Multiplex Networkmentioning

confidence: 99%

Section: The Citation Network As a Multiplex Networkmentioning

confidence: 99%

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Multiplex flows in citation networks

2017

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“…Beyond classical metrics (Adamic and Glance 2005), networks have been shown to be efficient for topic and concept analysis (Martin et al 2013), and multiplex networks have been explored to analyse news data (Renoust et al 2014). In particular, character networks have been broadly analyzed from literature (Waumans et al 2015), from TV dramas (Nan et al 2015), and there is even a website dedicated to the social analysis of Game of Thrones (Mish 2015).…”

Section: Related Workmentioning

confidence: 99%

When face-tracking meets social networks: a story of politics in news videos

et al. 2016

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In the age of data processing, news videos are rich mines of information. After all, the news are essentially created to convey information to the public. But can we go beyond what is directly presented to us and see a wider picture? Many works already focus on what we can discover and understand from the analysis of years of news broadcasting. These analysis bring monitoring and understanding of the activity of public figures, political strategies, explanation and even prediction of critical media events. Such tools can help public figures in managing their public image, as well as support the work of journalists, social scientists and other media experts. News analysis can also be seen from the lens of complex systems, gathering many types of entities, attributes and interactions over time. As many public figures intervene in different news stories, a first interesting task is to observe the social interactions between these actors. Towards this goal, we propose to use video analysis to automatise the process of constructing social networks directly from news video archives. In this paper we are introducing a system deriving multiple social networks from face detections in news videos. We present preliminary results obtained from analysis of these networks, by monitoring the activity of more than a hundred public figures. We finally use these networks as a support for political studies and we provide an overview of the political landscape presented by the Japanese public broadcaster NHK over a decade of the 7 PM news archives.

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Multilayer Network Model of Movie Script

Mourchid¹,

Renoust²,

Cherifi³

2018

Studies in Computational Intelligence

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Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration's content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights on movies. We demonstrate this model on two very popular movies.

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Entanglement in Multiplex Networks: Understanding Group Cohesion in Homophily Networks

Cited by 22 publications

References 37 publications

Multiplex flows in citation networks

Multiplex flows in citation networks

When face-tracking meets social networks: a story of politics in news videos

Multilayer Network Model of Movie Script

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