Temporal Networks

@inproceedings{Holme2014TemporalN,
  title={Temporal Networks},
  author={Petter Holme and Jari Saram{\"a}ki},
  booktitle={Encyclopedia of Social Network Analysis and Mining},
  year={2014}
}
Disease Spreading in Time-Evolving Networked Communities
TLDR
The effective infectiousness of a disease taking place along the edges of this temporal network depends on the population size, the number of infected individuals in the population and the capacity of healthy individuals to sever contacts with the infected.
Chapter 13 Disease Spreading in Time-Evolving Networked Communities
TLDR
It is shown how dynamical networks strongly decrease the average time required to eradicate a disease, ultimately dictated by availability of information regarding each individual’s health status.
Epidemiologically Optimal Static Networks from Temporal Network Data
TLDR
This paper investigates conceptually simple methods to construct static graphs for network epidemiology from temporal contact data, and evaluates these methods on empirical and synthetic model data.
Random graph models for dynamic networks
TLDR
An introduction to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes.
Network structure from a characterization of interactions in complex systems
TLDR
Surprisingly, it is found that particularly key constituents of functional networks—as identified with betweenness and eigenvector centrality—coincide with ground truth to a high degree, while global topological and spectral properties—clustering coefficient, average shortest path length, assortativity, and synchronizability—clearly deviate.
Effect of Heterogeneity of Vertex Activation on Epidemic Spreading in Temporal Networks
TLDR
This work takes heterogeneous distribution of the node interactivation time as the research background to build an asynchronous communication model and derives the threshold of virus spreading on the communication mode and analyzes the reason the heterogeneous distributed of the vertex interactivating time suppresses the spread of virus.
The Time Element of Temporal Networks
TLDR
This work explores people interactions via analyzing mobile phone data in the quest of finding the average ratio of people that an individual can connect or influence, i.e., the diffusion of ideas within any given time window.
The fundamental advantages of temporal networks
TLDR
It is demonstrated that temporal networks can be controlled more efficiently and require less energy than their static counterparts, and have control trajectories that are considerably more compact than those characterizing static networks.
Coverage centralities for temporal networks
TLDR
This paper defines two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex, and reveals that distributions of these centrality values of real-world temporal networks are heterogeneous.
...
...

References

SHOWING 1-10 OF 219 REFERENCES
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
TLDR
A generalized framework of network quality functions was developed that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices.
Spatial Networks
  • M. Barthelemy
  • Computer Science
    Encyclopedia of Social Network Analysis and Mining
  • 2014
The structure of information pathways in a social communication network
TLDR
This work forms a temporal notion of "distance" in the underlying social network by measuring the minimum time required for information to spread from one node to another - a concept that draws on the notion of vector-clocks from the study of distributed computing systems.
Collective dynamics of ‘small-world’ networks
TLDR
Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Persistence and periodicity in a dynamic proximity network
TLDR
It is suggested that dynamic social networks exhibit a natural time scale \Delta_{nat}, and that the best conversion of such dynamic data to a discrete sequence of networks is done at this natural rate.
Communicability across evolving networks.
TLDR
It is shown that classic centrality measures from the static setting can be extended in a computationally convenient manner and communicability indices can be computed to summarize the ability of each node to broadcast and receive information.
Connectivity and inference problems for temporal networks
TLDR
This work defines and studies the class of inference problems, in which it seeks to reconstruct a partially specified time labeling of a network in a manner consistent with an observed history of information flow, and provides results on two types of problems for temporal networks.
Components in time-varying graphs
TLDR
The notion of connectedness, and the definitions of node and graph components, are extended to the case of time-varying graphs, which are represented as time-ordered sequences of graphs defined over a fixed set of nodes.
Networks: An Introduction
TLDR
This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
...
...