NDLI: Motifs in Temporal Networks

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Motifs in Temporal Networks

Content Provider	ACM Digital Library
Author	Leskovec, Jure Paranjape, Ashwin Benson, Austin R.
Abstract	Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. The counts of small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs for temporal networks, which contain many timestamped links between nodes, is not well understood. Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of edges, design several fast algorithms for counting temporal network motifs, and prove their runtime complexity. We also show that our fast algorithms achieve 1.3x to 56.5x speedups compared to a baseline method. We use our algorithms to count temporal network motifs in a variety of real-world datasets. Results show that networks from different domains have significantly different motif frequencies, whereas networks from the same domain tend to have similar motif frequencies. We also find that measuring motif counts at various time scales reveals different behavior.
Starting Page	601
Ending Page	610
Page Count	10
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018731
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Motifs Temporal networks
Content Type	Text
Resource Type	Article

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