Modelling Temporal Networks with Markov Chains, Community Structures and Change Points

Peixoto, Tiago P.; Rosvall, Martin

doi:10.1007/978-3-030-23495-9_4

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…There are several modeling challenges, for example with synchronous and asynchronous events or relations, see [5]. Several methods have been proposed, for example, modeling with stream graphs [6], [7], Markov chains [8], [9], with network snapshots [10], or with a discrete set of time points that may contain snapshots. Most of these approaches are equivalent [11].…”

Section: Related Workmentioning

confidence: 99%

Towards modelling and analysis of longitudinal social networks

Dörpinghaus,

Weil,

Sommer

2023

Annals of Computer Science and Information Systems

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There are currently several approaches to managing longitudinal data in graphs and social networks. All of them influence the output of algorithms that analyse the data. We present an overview of limitations, possible solutions and open questions for different data schemas for temporal data in social networks, based on a generic RDF-inspired approach that is equivalent to existing approaches. While restricting the algorithms to a specific time point or layer does not affect the results, applying these approaches to a network with multiple time points requires either adapted algorithms or reinterpretation. Thus, with a generic definition of temporal networks as one graph, we will answer the question of how we can analyse longitudinal social networks with centrality measures. In addition, we present two approaches to approximate the change in degree and betweenness centrality measures over time.

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Section: Related Workmentioning

confidence: 99%

Towards modelling and analysis of longitudinal social networks

Dörpinghaus,

Weil,

Sommer

2023

Annals of Computer Science and Information Systems

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“…Then the data structure has to be estimated for effective representation, processing, analysis, or visualization of the data. For example, there exist many algorithms to detect community structures in a network, that is, groups of nodes such that the nodes are densely connected within the same group and relatively sparsely connected across different groups (Greene, Doyle, & Cunningham, 2010 ; Palla, Barabási, & Vicsek, 2007 ; Peixoto & Rosvall, 2017 , 2019 ). Another crucial task is to infer a graph topology that describes the characteristics of data observations, hence capturing the underlying relationship between these entities.…”

Section: Introductionmentioning

confidence: 99%

Analyzing spatial mobility patterns with time‐varying graphical lasso: Application to COVID‐19 spread

Degano

Lotito

2021

Transactions in GIS

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This work applies the time‐varying graphical lasso (TVGL) method, an extension of the traditional graphical lasso approach, to address learning time‐varying graphs from spatiotemporal measurements. Given georeferenced data, the TVGL method can estimate a time‐varying network where an edge represents a partial correlation between two nodes. To achieve this, we use a COVID‐19 data set from the Argentine province of Chaco. As an application, we use the estimated network to study the impact of COVID‐19 confinement measures and evaluate whether the measures produced the expected result.

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Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Sattar,

Buluc,

Ibrahim

et al. 2023

Appl Netw Sci

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Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar, which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

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Modelling Temporal Networks with Markov Chains, Community Structures and Change Points

Cited by 4 publications

References 24 publications

Towards modelling and analysis of longitudinal social networks

Towards modelling and analysis of longitudinal social networks

Analyzing spatial mobility patterns with time‐varying graphical lasso: Application to COVID‐19 spread

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

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