Modern temporal network theory: a colloquium

By Petter Holme. European Physical Journal B : Condensed Matter Physics, 2015

698 citations recorded by the source corpus.

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

Subfields: Astronomy and Astrophysics, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/04ff6d8d8708aef4ebc45ebee132fcc6f055bbd4

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Citations in the corpus

Graph Neural Networks With Convolutional ARMA Filters (2019, background): “comes quickly dense (LP describes a fully connected graph if Pis the graph diameter, which is usually small in real-world networks). The ARMA layer can naturally deal with time-varying graph signals (Holme, 2015;Grattarola et al.,2018) by replacing the constant term X in (10) with a time-dependent i”
The fundamental advantages of temporal networks (2016, background): “A substantial body of research has found that such temporality [8–12] has profound effects on most dynamical processes taking place on networks [8–10, 13], and raising barriers to accessibility [9].”
Statistical physics of vaccination (2016, background): “The first important observation is that contact networks are timevarying [219, 652, 675, 676].”
Nine quick tips for analyzing network data (2019, background): “ to this case - in particular dynamic community detection [25]. If the edges are observed continuously (appear and disappear 6 continuously with time), this flow of edges is called a temporal network [26]. When networks are observed at different spatial locations, the concept of metanetwork has emer”
How demographic processes shape animal social networks (2019, background): “We refer readers to several insightful reviews on the topic (e.g., Blonder et al. 2012; Pinter- Wollman et al. 2014; Holme 2015) for more details on causes and consequences of edge dynamics in social networks.”
Random walks and diffusion on networks (2016, methodology): “See the review [58] for a discussion of several models of 860”
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, methodology): “Dynamic networks suffer from a known terminology problem [6].”
Statistical clustering of temporal networks through a dynamic stochastic block model (2015, methodology): “Other approaches for model-based clustering of dynamic random graphs do not rely directly on SBMs but rather on variants of the SBM. We mention the random subgraph model that combines SBMs with the a priori knowledge of a nodes partition (inducing subgraphs), by authorizing the group proportions to ”