Constructing temporal networks with bursty activity patterns
Abstract Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patter...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-42868-1 |
| _version_ | 1797630092514426880 |
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| author | Anzhi Sheng Qi Su Aming Li Long Wang Joshua B. Plotkin |
| author_facet | Anzhi Sheng Qi Su Aming Li Long Wang Joshua B. Plotkin |
| author_sort | Anzhi Sheng |
| collection | DOAJ |
| description | Abstract Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time, namely the duration between successive pairwise interactions. Empirical studies have found inter-event time distributions that are heavy-tailed, for both physical and digital interactions. But it is difficult to construct theoretical models of time-varying activity on a network that reproduce the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures any desired target inter-event time distributions for individual nodes and links, provided the distributions fulfill a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions. |
| first_indexed | 2024-03-11T11:03:06Z |
| format | Article |
| id | doaj.art-44dfbf4cec304998b257df367f9a9583 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-11T11:03:06Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-44dfbf4cec304998b257df367f9a95832023-11-12T12:23:44ZengNature PortfolioNature Communications2041-17232023-11-0114111010.1038/s41467-023-42868-1Constructing temporal networks with bursty activity patternsAnzhi Sheng0Qi Su1Aming Li2Long Wang3Joshua B. Plotkin4Center for Systems and Control, College of Engineering, Peking UniversityDepartment of Automation, Shanghai Jiao Tong UniversityCenter for Systems and Control, College of Engineering, Peking UniversityCenter for Systems and Control, College of Engineering, Peking UniversityDepartment of Biology, University of PennsylvaniaAbstract Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time, namely the duration between successive pairwise interactions. Empirical studies have found inter-event time distributions that are heavy-tailed, for both physical and digital interactions. But it is difficult to construct theoretical models of time-varying activity on a network that reproduce the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures any desired target inter-event time distributions for individual nodes and links, provided the distributions fulfill a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions.https://doi.org/10.1038/s41467-023-42868-1 |
| spellingShingle | Anzhi Sheng Qi Su Aming Li Long Wang Joshua B. Plotkin Constructing temporal networks with bursty activity patterns Nature Communications |
| title | Constructing temporal networks with bursty activity patterns |
| title_full | Constructing temporal networks with bursty activity patterns |
| title_fullStr | Constructing temporal networks with bursty activity patterns |
| title_full_unstemmed | Constructing temporal networks with bursty activity patterns |
| title_short | Constructing temporal networks with bursty activity patterns |
| title_sort | constructing temporal networks with bursty activity patterns |
| url | https://doi.org/10.1038/s41467-023-42868-1 |
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