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Analytical Models for Motifs in Temporal Networks by Alexandra Porter; Baharan Mirzasoleiman; Jure Leskovec is a scholarly article available to read on EtoBox.

What is Analytical Models for Motifs in Temporal Networks about?

Dynamic evolving networks capture temporal relations in domains such as social networks, communication networks, and financial transaction networks. In such networks, temporal motifs, which are repeated sequences of time-stamped edges/transactions, offer valuable information about the networks' evolution and function. However, calculating temporal motif frequencies is computationally expensive as it requires: First, identifying all instances of the static motifs in the static graph induced by the temporal graph. And second, counting the number of subsequences of temporal edges that correspond to a temporal motif and occur within a time window. Since the number of temporal motifs changes over time, finding interesting temporal patterns involves iterative application of the above process over many consecutive time windows. This makes it impractical to scale to large real temporal networks. Here, we develop a fast and accurate model-based method for counting motifs in temporal networks. We first develop the Temporal Activity State Block Model (TASBM), to model temporal motifs in temporal graphs. Then we derive closed-form analytical expressions that allow us to quickly calculate expec

Author

Alexandra Porter; Baharan Mirzasoleiman; Jure Leskovec

Publisher

ACM

Published

2022

Language

EN