Autocorrelation properties of temporal networks governed by dynamic node variables
We study synthetic temporal networks whose evolution is determined by stochastically evolving node variables—synthetic analogues of, e.g., temporal proximity networks of mobile agents. We quantify the long-timescale correlations of these evolving networks by an autocorrelative measure of network-str...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-01-01
|
| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.7.013083 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591160568184832 |
|---|---|
| author | Harrison Hartle Naoki Masuda |
| author_facet | Harrison Hartle Naoki Masuda |
| author_sort | Harrison Hartle |
| collection | DOAJ |
| description | We study synthetic temporal networks whose evolution is determined by stochastically evolving node variables—synthetic analogues of, e.g., temporal proximity networks of mobile agents. We quantify the long-timescale correlations of these evolving networks by an autocorrelative measure of network-structural memory. Several distinct patterns of autocorrelation arise, including power-law decay and exponential decay, depending on the choice of node-variable dynamics and connection probability function. Our methods are also applicable in wider contexts; our temporal network models are tractable mathematically and in simulation, and our long-term memory quantification is analytically tractable and straightforwardly computable from temporal network data. |
| format | Article |
| id | doaj-art-4ce4a14f519b444b9731fcaaf187b9e8 |
| institution | Kabale University |
| issn | 2643-1564 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-4ce4a14f519b444b9731fcaaf187b9e82025-01-22T16:08:46ZengAmerican Physical SocietyPhysical Review Research2643-15642025-01-017101308310.1103/PhysRevResearch.7.013083Autocorrelation properties of temporal networks governed by dynamic node variablesHarrison HartleNaoki MasudaWe study synthetic temporal networks whose evolution is determined by stochastically evolving node variables—synthetic analogues of, e.g., temporal proximity networks of mobile agents. We quantify the long-timescale correlations of these evolving networks by an autocorrelative measure of network-structural memory. Several distinct patterns of autocorrelation arise, including power-law decay and exponential decay, depending on the choice of node-variable dynamics and connection probability function. Our methods are also applicable in wider contexts; our temporal network models are tractable mathematically and in simulation, and our long-term memory quantification is analytically tractable and straightforwardly computable from temporal network data.http://doi.org/10.1103/PhysRevResearch.7.013083 |
| spellingShingle | Harrison Hartle Naoki Masuda Autocorrelation properties of temporal networks governed by dynamic node variables Physical Review Research |
| title | Autocorrelation properties of temporal networks governed by dynamic node variables |
| title_full | Autocorrelation properties of temporal networks governed by dynamic node variables |
| title_fullStr | Autocorrelation properties of temporal networks governed by dynamic node variables |
| title_full_unstemmed | Autocorrelation properties of temporal networks governed by dynamic node variables |
| title_short | Autocorrelation properties of temporal networks governed by dynamic node variables |
| title_sort | autocorrelation properties of temporal networks governed by dynamic node variables |
| url | http://doi.org/10.1103/PhysRevResearch.7.013083 |
| work_keys_str_mv | AT harrisonhartle autocorrelationpropertiesoftemporalnetworksgovernedbydynamicnodevariables AT naokimasuda autocorrelationpropertiesoftemporalnetworksgovernedbydynamicnodevariables |