Exploring Concurrency and Reachability in the Presence of High Temporal Resolution

Lee, Eun; Moody, James; Mucha, Peter J.

doi:10.1007/978-3-030-23495-9_7

Cited by 6 publications

(14 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By definition, the reachability ignores infectivity and therefore does not directly translate into observed epidemics. However, since high average reachability is positively correlated with the final outbreak size, the results in [26,27,30,76] are consistent with the common claim that the concurrency positively contributes to the severity of outbreaks.…”

Section: Overlap Of Time Windows Of Edge Activationsupporting

confidence: 83%

“…Although theoretical epidemiology was also the first field to study other network structures such as degree assortativity [ 120 ] or heterogeneous degree distributions [ 60 , 126 ], strangely, the relationship between these structures is still not fully charted. Our understanding of how temporal structures are related to concurrency is even more limited [ 20 , 22 , 27 , 30 , 41 , 51 , 87 ]. One such example is the statistics of the interevent times [ 6 , 36 , 87 , 127 , 128 ].…”

Section: Discussionmentioning

confidence: 99%

“…Assuming that the event time and its duration are randomly generated and independent for different edges, different authors defined the temporal coherency as the probability that the time windows of activation on two edges overlap [26,30]. For empirical data of temporal networks, the same quantity can be measured as the fraction of edge pairs that have overlapping active time, where the edge may be optionally assumed to be active all the time between its first and last event times [27]. The temporal coherency is similar to a normalized variant of d. Note that one can consider both IIC and temporal coherency for individual nodes or the entire network (as the average of the quantity over all the nodes or all the edge pairs in the network).…”

Section: Overlap Of Time Windows Of Edge Activationmentioning

confidence: 99%

“…These early articles rarely state how one could hypothetically reduce the full information of a temporal network of sexual interactions to a network of partnerships-should one casual, non-recurring interaction count as a partnership? Furthermore, papers state a multitude of quantitative definitions of concurrency [23][24][25][26][27][28][29][30][31][32], and several studies point out the difficulty of studying an issue without an agreed definition [27,[33][34][35]. Some of the existing confusion and controversy probably stems from the vagueness of the partnership concept.…”

Section: Introduction 1temporal Network Epidemiology Partnership and Concurrencymentioning

confidence: 99%

See 3 more Smart Citations

Concurrency measures in the era of temporal network epidemiology: a review

Masuda

Miller

Holme

2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency—quantifying individuals’ tendency to form time-overlapping ‘partnerships’. Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches.

show abstract

Section: Overlap Of Time Windows Of Edge Activationsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Overlap Of Time Windows Of Edge Activationmentioning

confidence: 99%

Section: Introduction 1temporal Network Epidemiology Partnership and Concurrencymentioning

confidence: 99%

See 2 more Smart Citations

Concurrency measures in the era of temporal network epidemiology: a review

Masuda

Miller

Holme

2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…The temporal resolution plays an important role in the layout construction and, consequently, in the visual analysis. In several scenarios, as, for example, when the networks are temporally sparse, changing the resolution scale may facilitate the analysis and highlight patterns that would be difficult to see using the original resolution (LINHARES et al, 2017b;MOODY;MUCHA, 2019). The choice of the ideal temporal resolution, however, is not a trivial task.…”

Section: Introductionmentioning

confidence: 99%

Improving visual analysis of streaming networks

Ponciano¹

View full text Add to dashboard Cite

Temporal networks (also known as dynamic networks) are often used to model connections that occur over time between parts of a system by using nodes and edges. In temporal networks, all nodes, edges, and times, are known and available to be used in the analysis. However, in several real-world applications, data are produced in a massive and continuous way, which is known as data stream. In this case, the volume of data may be so large that the storage may be impossible and mining tasks become more challenging. In streaming temporal networks, edges are continuously arriving in non-stationary distribution. In both temporal and streaming temporal networks, patterns related to node and edge activity are typically irregular in time, which makes the visualization of such networks helpful to gain insights about network structure and dynamics. Nevertheless, the non-stationary distribution of incoming data increases complexity and turns the streaming temporal network visualization even more challenging. Several visualization layouts have been proposed, but they all have limitations. The main challenge in this context is the amount of visual information, that increases depending on the network size and density, and causes visual clutter due to edge overlap, fine temporal resolution, and node proximity. In this thesis, we propose methods to enhance the visualization of streaming temporal networks through the manipulation of the three network dimensions, namely node, edge, and time. Specifically, we propose: (i) CNO, a visual scalable node ordering method; (ii) SEVis, a streaming edge sampling method; and (iii) a streaming method that adapts the temporal resolution according to local levels of node activity. We also present a comparative study considering the combination of these methods. We show through case studies with real-world networks that each of these methods greatly improves layout readability, thus leading to a fast and reliable decision making.

show abstract

Effects of concurrency on epidemic spreading in Markovian temporal networks

Liu,

Ogura,

Dos Reis

et al. 2023

Eur. J. Appl. Math

View full text Add to dashboard Cite

The concurrency of edges, quantified by the number of edges that share a common node at a given time point, may be an important determinant of epidemic processes in temporal networks. We propose theoretically tractable Markovian temporal network models in which each edge flips between the active and inactive states in continuous time. The different models have different amounts of concurrency while we can tune the models to share the same statistics of edge activation and deactivation (and hence the fraction of time for which each edge is active) and the structure of the aggregate (i.e., static) network. We analytically calculate the amount of concurrency of edges sharing a node for each model. We then numerically study effects of concurrency on epidemic spreading in the stochastic susceptible-infectious-susceptible and susceptible-infectious-recovered dynamics on the proposed temporal network models. We find that the concurrency enhances epidemic spreading near the epidemic threshold, while this effect is small in many cases. Furthermore, when the infection rate is substantially larger than the epidemic threshold, the concurrency suppresses epidemic spreading in a majority of cases. In sum, our numerical simulations suggest that the impact of concurrency on enhancing epidemic spreading within our model is consistently present near the epidemic threshold but modest. The proposed temporal network models are expected to be useful for investigating effects of concurrency on various collective dynamics on networks including both infectious and other dynamics.

show abstract

Exploring Concurrency and Reachability in the Presence of High Temporal Resolution

Cited by 6 publications

References 40 publications

Concurrency measures in the era of temporal network epidemiology: a review

Concurrency measures in the era of temporal network epidemiology: a review

Improving visual analysis of streaming networks

Effects of concurrency on epidemic spreading in Markovian temporal networks

Contact Info

Product

Resources

About