Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks
One of the main challenges in the study of time-varying networks is the interplay of memory effects with structural heterogeneity. In particular, different nodes and dyads can have very different statistical properties in terms of both link formation and link persistence, leading to a superposition...
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| Format: | Article |
| Language: | English |
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American Physical Society
2024-12-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.6.043257 |
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| author | Giulio Virginio Clemente Claudio J. Tessone Diego Garlaschelli |
| author_facet | Giulio Virginio Clemente Claudio J. Tessone Diego Garlaschelli |
| author_sort | Giulio Virginio Clemente |
| collection | DOAJ |
| description | One of the main challenges in the study of time-varying networks is the interplay of memory effects with structural heterogeneity. In particular, different nodes and dyads can have very different statistical properties in terms of both link formation and link persistence, leading to a superposition of typical timescales, suboptimal parametrizations, and substantial estimation biases. Here we develop an unbiased maximum-entropy framework to study empirical network trajectories by controlling for the observed structural heterogeneity and local link persistence simultaneously. An exact mapping to a heterogeneous version of the one-dimensional Ising model leads to an analytic solution that rigorously disentangles the hidden variables that jointly determine both static and temporal properties. Additionally, model selection via likelihood maximization identifies the most parsimonious structural level (either global, node specific, or dyadic) accounting for memory effects. As we illustrate on real-world social networks, this method enables an improved estimation of dyadic transition probabilities, relaxation times, and structural breaks between dynamical regimes. In the resulting picture, the graph follows a generalized configuration model with given degrees and given time-persisting degrees, undergoing transitions between empirically identifiable stationary regimes. |
| format | Article |
| id | doaj-art-6e6cc9ff3a9947fd94f9b8a84bba5345 |
| institution | Kabale University |
| issn | 2643-1564 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-6e6cc9ff3a9947fd94f9b8a84bba53452024-12-11T15:04:39ZengAmerican Physical SocietyPhysical Review Research2643-15642024-12-016404325710.1103/PhysRevResearch.6.043257Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaksGiulio Virginio ClementeClaudio J. TessoneDiego GarlaschelliOne of the main challenges in the study of time-varying networks is the interplay of memory effects with structural heterogeneity. In particular, different nodes and dyads can have very different statistical properties in terms of both link formation and link persistence, leading to a superposition of typical timescales, suboptimal parametrizations, and substantial estimation biases. Here we develop an unbiased maximum-entropy framework to study empirical network trajectories by controlling for the observed structural heterogeneity and local link persistence simultaneously. An exact mapping to a heterogeneous version of the one-dimensional Ising model leads to an analytic solution that rigorously disentangles the hidden variables that jointly determine both static and temporal properties. Additionally, model selection via likelihood maximization identifies the most parsimonious structural level (either global, node specific, or dyadic) accounting for memory effects. As we illustrate on real-world social networks, this method enables an improved estimation of dyadic transition probabilities, relaxation times, and structural breaks between dynamical regimes. In the resulting picture, the graph follows a generalized configuration model with given degrees and given time-persisting degrees, undergoing transitions between empirically identifiable stationary regimes.http://doi.org/10.1103/PhysRevResearch.6.043257 |
| spellingShingle | Giulio Virginio Clemente Claudio J. Tessone Diego Garlaschelli Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks Physical Review Research |
| title | Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks |
| title_full | Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks |
| title_fullStr | Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks |
| title_full_unstemmed | Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks |
| title_short | Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks |
| title_sort | temporal networks with node specific memory unbiased inference of transition probabilities relaxation times and structural breaks |
| url | http://doi.org/10.1103/PhysRevResearch.6.043257 |
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