ArticleBioinformatics (Oxford, England)2015
Exploring the structure and function of temporal networks with dynamic graphlets.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Optimizing resource utilization for large scale problems through architecture aware scheduling.Scientific reports · 2024Article
- Motif discovery in hospital ward vital signs observation networks.Network modeling and analysis in health informatics and bioinformatics · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Temporal networks in biology and medicine: a survey on models, algorithms, and tools.Network modeling and analysis in health informatics and bioinformatics · 2023Review
- Multi-layer sequential network analysis improves protein 3D structural classification.Proteins · 2022Article
- Binding Networks Identify Targetable Protein Pockets for Mechanism-Based Drug Design.International journal of molecular sciences · 2022Article
- Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach.Journal of biomedical informatics · 2022Article
- Temporal motifs in patent opposition and collaboration networks.Scientific reports · 2022Article
- Encoding edge type information in graphlets.PloS one · 2022Article
- MODIT: MOtif DIscovery in Temporal Networks.Frontiers in big data · 2021Article
- Network-based protein structural classification.Royal Society open science · 2020Article
- The power of dynamic social networks to predict individuals' mental health.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Analyzing the Bills-Voting Dynamics and Predicting Corruption-Convictions Among Brazilian Congressmen Through Temporal Networks.Scientific reports · 2019Article
- Identification of co-evolving temporal networks.BMC genomics · 2019Article
- ClueNet: Clustering a temporal network based on topological similarity rather than denseness.PloS one · 2018Article
- Article
- GRAFENE: Graphlet-based alignment-free network approach integrates 3D structural and sequence (residue order) data to improve protein structural comparison.Scientific reports · 2017Article
- Alignment of dynamic networks.Bioinformatics (Oxford, England) · 2017Article
- Clone temporal centrality measures for incomplete sequences of graph snapshots.BMC bioinformatics · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationWith increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships.
resultsWe base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging. AVAILABILITY AND IMPLEMENTATION: http://www.nd.edu/∼cone/DG.
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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.