Unfolding Accessibility Provides a Macroscopic Approach to Temporal Networks

Lentz, Hartmut H. K.; Selhorst, Thomas; Sokolov, Igor M.

doi:10.1103/physrevlett.110.118701

Cited by 112 publications

(97 citation statements)

References 27 publications

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“…Previous work has considered temporal and memory effects on spreading by modelling timerespecting paths in temporal networks of contacts 22,23,52 and bidirectional paths in mobility networks of commuters 27,28,53,54 . Our objective is to quantify the full effect of second-order Markov dynamics in general mobility patterns.…”

Section: First-order Markovmentioning

confidence: 99%

“…Again, once random transmission occurs in a city, all memory effects are washed out in this metapopulation model. Therefore, the effect of a higher-order Markov process is primarily influential in the beginning of the outbreak during the introduction phase when the sequence of contacts matters 22,23,52 . Overall, we conclude that the first-and secondorder dynamics must be sufficiently different to show a clear difference on the spreading.…”

Section: First-order Markovmentioning

confidence: 99%

“…Shannon 10 introduced higher-order memory models in 1948, and there is a substantial body of work on analysing memory effects in, for example, time-series analysis for forecasting financial markets 11 , correlated random walks for predicting animal movements 12 and exponential random graph models for capturing social networks 13 . Moreover, there is recent evidence that memory is necessary for accurately predicting web traffic 14,15 , for improving search and navigation in information networks [16][17][18] and for capturing important phenomena in the spread of information [19][20][21][22][23] and epidemics [24][25][26][27][28][29] . Nevertheless, little is known about memory effects on community detection, ranking and spreading analysis, three principal methods in network science.…”

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confidence: 99%

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Memory in network flows and its effects on spreading dynamics and community detection

et al. 2014

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Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking and spreading analysis, although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and although we only observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has important consequences for community detection, ranking and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. These findings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.

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Section: First-order Markovmentioning

confidence: 99%

Section: First-order Markovmentioning

confidence: 99%

mentioning

confidence: 99%

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Memory in network flows and its effects on spreading dynamics and community detection

et al. 2014

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“…As such, both the order and timing of interactions affect time-respecting paths-and thus causality-in temporal networks. Compared with the rich literature on node activities, a relatively smaller number of studies empirically investigated effects of causality in temporal networks 22,[24][25][26][27][28][29][30] . Recent works have shown that order correlations in temporal networks lead to causality structures, which significantly deviate from what is expected based on paths in the corresponding time-aggregated networks [27][28][29] .…”

mentioning

confidence: 99%

“…Compared with the rich literature on node activities, a relatively smaller number of studies empirically investigated effects of causality in temporal networks 22,[24][25][26][27][28][29][30] . Recent works have shown that order correlations in temporal networks lead to causality structures, which significantly deviate from what is expected based on paths in the corresponding time-aggregated networks [27][28][29] . Studying time-respecting paths a-b-c from the perspective of a contact sequence a,b,c passing through node b, it was shown that the next contact c not only depends on the current contact b but also on the previous one [28][29][30][31] .…”

mentioning

confidence: 99%

Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks

et al. 2014

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Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.

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Exploring Concurrency and Reachability in the Presence of High Temporal Resolution

Lee¹,

Moody

Mucha³

2019

Computational Social Sciences

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Network properties govern the rate and extent of spreading processes on networks, from simple contagions to complex cascades. Recent advances have extended the study of spreading processes from static networks to temporal networks, where nodes and links appear and disappear. We review previous studies on the effects of temporal connectivity for understanding the spreading rate and outbreak size of model infection processes. We focus on the effects of accessibility, whether there is a temporally consistent path from one node to another, and reachability, the density of the corresponding accessibility graph representation of the temporal network. We study reachability in terms of the overall level of temporal concurrency between edges, quantifying the overlap of edges in time. We explore the role of temporal resolution of contacts by calculating reachability with the full temporal information as well as with a simplified interval representation approximation that demands less computation. We demonstrate the extent to which the computed reachability changes due to this simplified interval representation.

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Unfolding Accessibility Provides a Macroscopic Approach to Temporal Networks

Cited by 112 publications

References 27 publications

Memory in network flows and its effects on spreading dynamics and community detection

Memory in network flows and its effects on spreading dynamics and community detection

Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks

Exploring Concurrency and Reachability in the Presence of High Temporal Resolution

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