Opinion dynamics on tie-decay networks

Sugishita, Kashin; Porter, Mason A.; Beguerisse-Díaz, Mariano; Masuda, Naoki

doi:10.1103/physrevresearch.3.023249

“…With applications in as disparate branches of science as statistical physics [1], epidemiology [2][3][4][5][6][7][8][9], chemistry and systems biology [10,11], social science [12,13], and computer science [14][15][16], interacting particles on complex networks constitute an important class of models in the mathematician's and physicist's toolkit [17][18][19][20]. They describe systems where individual entities (particles), endowed with local states, interact with a subset of other entities (neighbors) and transition from one state to another as time evolves.…”

mentioning

confidence: 99%

Motif-based mean-field approximation of interacting particles on clustered networks

Cui

¹

,

KhudaBukhsh

²

,

Koeppl

³

2022

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Interacting particles on graphs are routinely used to study magnetic behavior in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature on mean-field approximations of such systems for large graphs typically remains limited to specific dynamics, or assumes cluster-free graphs for which standard approximations based on degrees and pairs are often reasonably accurate. Here, we propose a motif-based mean-field approximation that considers higher-order subgraph structures in large clustered graphs. Numerically, our equations agree with stochastic simulations where existing methods fail.

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“…With applications in as disparate branches of science as statistical physics [1], epidemiology [2][3][4][5][6][7][8][9], chemistry and systems biology [10,11], social science [12,13], and computer science [14][15][16], interacting particles on complex networks constitute an important class of models in the mathematician's and physicist's toolkit [17][18][19][20]. They describe systems where individual entities (particles), endowed with local states, interact with a subset of other entities (neighbors) and transition from one state to another as time evolves.…”

mentioning

confidence: 99%