The Graph Whisperers

Grindrod, Peter; Higham, Desmond J.; Laflin, Peter

doi:10.1007/978-3-319-25454-8_35

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…What role did the spread of misinformation, 'fake news', and 'alternative facts' play in the 2016 US presidential election, and how did this role change with evolving network structure as communication channels became more polarized? How can one use information from social interactions to improve strategies for predicting and preventing criminal 1 See [35] (from the collection [6]) for an example application of network analysis in industry. 2 One example is Orgnet LLC [3], with which neither of us has an affiliation or collaboration, which 'provides software, training, consulting, and research in the application of network analysis in a wide variety of domains.'…”

Section: Introduction and A Few Basic Ideasmentioning

confidence: 99%

The Role of Network Analysis in Industrial and Applied Mathematics

Porter,

Howison

2017

Preprint

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Many problems in industry -and in the social, natural, information, and medical sciences -involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. The study of networks is concerned explicitly with connectivity between different entities, and it has become very prominent in industrial settings, an importance that has intensified amidst the modern data deluge. In this commentary, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry. We focus, in particular, on discussing a physical-applied-mathematics approach to the study of networks. We also discuss several of our own collaborations with industry on projects in network analysis.

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Section: Introduction and A Few Basic Ideasmentioning

confidence: 99%

The Role of Network Analysis in Industrial and Applied Mathematics

Porter,

Howison

2017

Preprint

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“…One common question that arises frequently when analyzing a network in scientific and industrial applications is the following: What are the most important nodes? [25] To examine this question, researchers have developed numerous centrality scores to quantify the importances of nodes according to different criteria [53].…”

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confidence: 99%

Tie-decay networks in continuous time and eigenvector-based centralities

Ahmad¹,

Porter²,

Beguerisse-Díaz³

2018

Preprint

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Network theory is a useful framework for studying interconnected systems of interacting agents. Many networked systems evolve continuously in time, but most existing methods for the analysis of time-dependent networks rely on discrete or discretized time. In this paper, we propose an approach for studying networks that evolve in continuous time by distinguishing between interactions, which we model as discrete contacts, and ties, which represent strengths of relationships as functions of time. To illustrate our tie-decay network formulation, we adapt the well-known Page-Rank centrality score to the tie-decay framework in a mathematically tractable and computationally efficient way. We demonstrate our framework on a synthetic example and then use it to study a network of retweets during the 2012 National Health Service controversy in the United Kingdom. Our work also provides guidance for similar generalizations of other tools from network theory to continuous-time networks with tie decay, including for applications to streaming data.

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