Temporal motifs reveal homophily, gender-specific patterns and group talk in mobile communication networks
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Abstract (arXiv)
Electronic communication records provide detailed information about temporal aspects of human interaction. Previous studies have shown that individuals' communication patterns have complex temporal structure, and that this structure has system-wide effects. In this paper we use mobile phone records to show that interaction patterns involving multiple individuals have non-trivial temporal structure that cannot be deduced from a network presentation where only interaction frequencies are taken into account. We apply a recently introduced method, temporal motifs, to identify interaction patterns in a temporal network where nodes have additional attributes such as gender and age. We then develop a null model that allows identifying differences between various types of nodes so that these differences are independent of the network based on interaction frequencies. We find gender-related differences in communication patters, and show the existence of temporal homophily, the tendency of similar individuals to participate in interaction patterns beyond what would be expected on the basis of the network structure alone. We also show that temporal patterns differ between dense and sparse parts of the network. Because this result is independent of edge weights, it can be considered as an extension of Granovetter's hypothesis to temporal networks.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2013.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 0; 2025: 3.
7 papers.
- 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
- 2023Using Motif Transitions for Temporal Graph Generationcited by 7
- 2021odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networkscited by 3
- 2023Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platformscited by 3
- 2025Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphscited by 0
- 2025Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learningcited by 0
- 2025Temporal social network modeling of mobile connectivity data with graph neural networkscited by 0
- 2025Temporal social network modeling of mobile connectivity data with graph neural networks3 Sep 2025
- 2025Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning20 Aug 2025
- 2025Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs22 Apr 2025
- 2023Using Motif Transitions for Temporal Graph Generation19 Jun 2023
- 2023Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms18 Jan 2023
- 2021odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks19 Aug 2021
- 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks15 Jan 2021