Temporal motifs in time-dependent networks

By Lauri Kovanen, Márton Karsai, Kimmo K. Kaski, János Kertész, J. Saramaki. arXiv.org, 2011

311 citations recorded by the source corpus.

Temporal networks are commonly used to represent systems where connections between elements are active only for restricted periods of time, such as telecommunication, neural signal processing, biochemical reaction and human social interaction networks. We introduce the framework of temporal motifs to study the mesoscale topological–temporal structure of temporal networks in which the events of nodes do not overlap in time. Temporal motifs are classes of similar event sequences, where the similarity refers not only to topology but also to the temporal order of the events. We provide a mapping from event sequences to coloured directed graphs that enables an efficient algorithm for identifying temporal motifs. We discuss some aspects of temporal motifs, including causality and null models, and present basic statistics of temporal motifs in a large mobile call network.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/761331db984188e20b796c6f9079e433ea3d99fb

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In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

Citations in the corpus

Networks beyond pairwise interactions: structure and dynamics (2020, background): “Motifs also found widespread application in the study of social [58] and temporal [59, 60] systems.”
Motifs in Temporal Networks (2016, background): “Prior definitions of temporal network motifs either do not account for edge ordering [30], only have heuristic counting algorithms [7], or assume temporal edges in a motif must be consecutive events for a node [13].”
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (2021, background): “Temporal network motifs are defined as connected subgraphs that consist of links appearing within a restricted time range (Kovanen et al., 2011).”
Universal features of correlated bursty behaviour (2011, background): “[40] Kovanen L, Karsai M, Kaski K, Kertész J, Saramäki J (2011) Temporal motifs in time-dependent networks.”
Community Discovery in Dynamic Networks (2017, unknown): “Many works tried to transpose well-known graph mining problems from static networks to temporal networks: Temporal motifs mining (Kovanen et al., 2011), Diffusion (Lee et al.”