Event Graphs: Advances and Applications of Second-Order Time-Unfolded Temporal Network Models

Mellor, Andrew

doi:10.1142/s0219525919500061

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…The incorporation of temporal information into models of hypergraphs and annotated hypergraphs is of substantial importance for modeling realistic dynamics on network substrates. One route may be to generalize temporal event graphs [31] for rich, polyadic data. Such a generalization, along with the development of associated metrics, would be of substantial theoretical and practical interest.…”

Section: Discussionmentioning

confidence: 99%

Annotated hypergraphs: models and applications

Chodrow

Mellor

2020

Appl Netw Sci

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Hypergraphs offer a natural modeling language for studying polyadic interactions between sets of entities. Many polyadic interactions are asymmetric, with nodes playing distinctive roles. In an academic collaboration network, for example, the order of authors on a paper often reflects the nature of their contributions to the completed work. To model these networks, we introduce annotated hypergraphs as natural polyadic generalizations of directed graphs. Annotated hypergraphs form a highly general framework for incorporating metadata into polyadic graph models. To facilitate data analysis with annotated hypergraphs, we construct a role-aware configuration null model for these structures and prove an efficient Markov Chain Monte Carlo scheme for sampling from it. We proceed to formulate several metrics and algorithms for the analysis of annotated hypergraphs. Several of these, such as assortativity and modularity, naturally generalize dyadic counterparts. Other metrics, such as local role densities, are unique to the setting of annotated hypergraphs. We illustrate our techniques on six digital social networks, and present a detailed case-study of the Enron email data set.

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Section: Discussionmentioning

confidence: 99%

Annotated hypergraphs: models and applications

Chodrow

Mellor

2020

Appl Netw Sci

Self Cite

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“…The dynamic-S 1 model [86] is a temporal extension of the static S 1 model [15] consisting of a sequence of independent samples with HVs partially inferred from real data and partially synthetically generated; the dynamics therein resembles THVMs with ω = 1 and σ = 0, but with varying average degree parameter across snapshots. Although it is common practice to extend static-model concepts to temporal settings [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66], many models of temporal networks are instead derived from first principles [127][128][129][130][131][132], and focus primarily on inference techniques, real-world applicability [133][134][135][136], and/or the effects of temporality on spreading [85,99].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Hidden-Variable Network Models

Hartle,

Papadopoulos,

Krioukov

2021

Preprint

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Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Realworld networks evolve over time, and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The rates of the hidden variable dynamics and link dynamics are controlled by two parameters, and snapshots of networks in the dynamic models may or may not be equivalent to a static model, depending on the location in the parameter phase diagram. We quantify deviations from static-like behavior, and examine the level of structural persistence in the considered models. We explore temporal versions of popular static models with community structure, latent geometry, and degree-heterogeneity. We do not attempt to directly model real networks, but comment on interesting qualitative resemblances, discussing possible extensions, generalizations, and applications.

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“…More prudent (existing) approaches to deal with the complexity of temporal contact networks entail setting a threshold to filter out the non-essential edges or edges that exist only by chance: for example, Grabowicz et al used a simple threshold based on the number of events (i.e., count of edges across time) between two nodes 29 ; Kobayashi defined a temporal null model to identify pairs of nodes having more interactions than expected given their activities 30 ; and in yet another thread of study, Mellor et al introduced the temporal event graph (TEG), which uses events (interactions between two individuals) as nodes and shared event attendees as edges in a directed graph 31 , 32 . Some of these efforts focus on only part of the complexity of temporal networks and provide possible ways to identify structures 29 , 30 , communities 4 , 22 , 33 and quantify connectivity 32 , 34 , 35 . Other related papers on the topic discuss temporal motifs 36 and percolation in light of weighted temporal event graphs 37 , or propose an event embedding technique to obtain a low-dimensional representation of temporal networks 35 .…”

Section: Introductionmentioning

confidence: 99%

Quantifying agent impacts on contact sequences in social interactions

Dekker

Blanken

Dablander

et al. 2022

Sci Rep

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Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions—since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time—analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called contact sequence centrality, which quantifies the impact of an individual on the contact sequences, reflecting the individual’s behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential ‘behavioral super-spreaders’. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.

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Event Graphs: Advances and Applications of Second-Order Time-Unfolded Temporal Network Models

Cited by 7 publications

References 37 publications

Annotated hypergraphs: models and applications

Annotated hypergraphs: models and applications

Dynamic Hidden-Variable Network Models

Quantifying agent impacts on contact sequences in social interactions

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