A Map of Approaches to Temporal Networks

Holme, Petter; Saramäki, Jari

doi:10.1007/978-3-030-23495-9_1

Cited by 41 publications

(60 citation statements)

References 97 publications

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“…Outgoing contact chain (OCC) : The OCC counts the number of direct or indirect contacts through outgoing movements, taking into account the order of the movements. The OCC is sometimes also called the “set of influence” of a holding [5].…”

Section: Methodsmentioning

confidence: 99%

“…Reachability : The reachability ratio is the temporal counterpart of the connected components. The reachability ratio is defined as the fraction of node-pairs that are connected through a time-respecting path [5,30].…”

Section: Methodsmentioning

confidence: 99%

“…The first attempts at modelling animal transport networks with social network analysis were static network models [3,4]. The importance of dynamic (or temporal) network analysis has only recently been established [5–7]. Animal transport networks are inherently temporal, as a link between two holdings only exists at the time of the transport.…”

Section: Introductionmentioning

confidence: 99%

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The pig transport network in Switzerland: Structure, patterns, and implications for the transmission of infectious diseases between animal holdings

et al. 2019

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The topology of animal transport networks contributes substantially to how fast and to what extent a disease can transmit between animal holdings. Therefore, public authorities in many countries mandate livestock holdings to report all movements of animals. However, the reported data often does not contain information about the exact sequence of transports, making it impossible to assess the effect of truck sharing and truck contamination on disease transmission. The aim of this study was to analyze the topology of the Swiss pig transport network by means of social network analysis and to assess the implications for disease transmission between animal holdings. In particular, we studied how additional information about transport sequences changes the topology of the contact network. The study is based on the official animal movement database in Switzerland and a sample of transport data from one transport company. The results show that the Swiss pig transport network is highly fragmented, which mitigates the risk of a large-scale disease outbreak. By considering the time sequence of transports, we found that even in the worst case, only 0.34% of all farm-pairs were connected within one month. However, both network connectivity and individual connectedness of farms increased if truck sharing and especially truck contamination were considered. Therefore, the extent to which a disease may be transmitted between animal holdings may be underestimated if we only consider data from the official animal movement database. Our results highlight the need for a comprehensive analysis of contacts between farms that includes indirect contacts due to truck sharing and contamination. As the nature of animal transport networks is inherently temporal, we strongly suggest the use of temporal network measures in order to evaluate individual and overall risk of disease transmission through animal transportation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The pig transport network in Switzerland: Structure, patterns, and implications for the transmission of infectious diseases between animal holdings

et al. 2019

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“…The airline industry is highly susceptible to such socioeconomic and natural events, and these events may influence the structure of air transport networks. Regardless of the reason, air transport networks can be regarded as temporal networks in which nodes are airports and edges are direct flight connections 13 ; in various types of networks such as social and economic networks, nodes and/or edges change over time, constituting temporal networks 14 – 17 . In fact, air transport networks consist of multiple carriers.…”

Section: Introductionmentioning

confidence: 99%

Recurrence in the evolution of air transport networks

Sugishita

Masuda

2021

Sci Rep

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Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the present study, we propose a framework for analyzing evolution patterns in temporal networks in discrete time from the viewpoint of recurrence. Recurrence implies that the network structure returns to one relatively close to that in the past. We applied the proposed methods to four major carriers in the US from 1987 to 2019. We found that the carriers were different in terms of the autocorrelation, strength of periodicity, and changes in these quantities across decades. We also found that the network structure of the individual carriers abruptly changes from time to time. Such a network change reflects changes in their operation at their hub airports rather than famous socioeconomic events that look closely related to airline industry. The proposed methods are expected to be useful for revealing, for example, evolution of airline alliances and responses to natural disasters or infectious diseases, as well as characterizing evolution of social, biological, and other networks over time.

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“…A host of social, biological, and technological systems emerge and subsequently evolve as networks 1 – 6 . Network models parsimoniously represent the evolutionary dynamics of these complex systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of social network emergence explain network evolution

Pomeroy

Bond

Mucha

et al. 2020

Sci Rep

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Networked systems emerge and subsequently evolve. Although several models describe the process of network evolution, researchers know far less about the initial process of network emergence. Here, we report temporal survey results of a real-world social network starting from its point of inception. We find that individuals’ ties undergo an initial cycle of rapid expansion and contraction. This process helps to explain the eventual interactions and working structure in the network (in this case, scientific collaboration). We propose a stylized concept and model of “churn” to describe the process of network emergence and stabilization. Our empirical and simulation results suggest that these network emergence dynamics may be instrumental for explaining network details, as well as behavioral outcomes at later time periods.

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A Map of Approaches to Temporal Networks

Cited by 41 publications

References 97 publications

The pig transport network in Switzerland: Structure, patterns, and implications for the transmission of infectious diseases between animal holdings

The pig transport network in Switzerland: Structure, patterns, and implications for the transmission of infectious diseases between animal holdings

Recurrence in the evolution of air transport networks

Dynamics of social network emergence explain network evolution

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