ArticleScientific reports2019
Detecting sequences of system states in temporal networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 61 citations in OpenAlex.
- Fluid dynamics reduction methods for temporal networks.Scientific reports · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- States of dynamic connectivity flow in temporal multiplex networks: A case study in human epilepsy and postictal aphasia.Network neuroscience (Cambridge, Mass.) · 2026Article
- Random walk based snapshot clustering for detecting community dynamics in temporal networks.Scientific reports · 2025Article
- Compressing the Chronology of a Temporal Network with Graph Commutators.Physical review letters · 2024Article
- Discovering trends of social interaction behavior over time: An introduction to relational event modeling : Trends of social interaction.Behavior research methods · 2023Article
- Inferring cell cycle phases from a partially temporal network of protein interactions.Cell reports methods · 2023Article
- Characterization of interactions' persistence in time-varying networks.Scientific reports · 2023Article
- From temporal network data to the dynamics of social relationships.Proceedings. Biological sciences · 2021Article
- Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs).Scientific reports · 2021Article
- Recurrence in the evolution of air transport networks.Scientific reports · 2021Article
- A Path-Based Distribution Measure for Network Comparison.Entropy (Basel, Switzerland) · 2020Article
- Network comparison and the within-ensemble graph distance.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- Structure and function in human and primate social networks: implications for diffusion, network stability and health.Proceedings. Mathematical, physical, and engineering sciences · 2020Review
- Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering.Applied network science · 2020Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system's states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein interactions, ecological interspecific interactions, functional connectivity in the brain and adaptive social networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.