ASIND algorithm alternately identifies self-dynamics, interactive functions, and networks sparsely without prior knowledge, claiming state-of-the-art identification and 100-step prediction on network dynamics.
Emergence of scaling in random networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Local Optima Networks model innovation as random walks on graphs of locally optimal configurations and jointly reproduce Heaps', Zipf's, Taylor's laws plus power-law inter-event times.
citing papers explorer
-
ASIND: Alternating Sparse Identification for Predicting Network Dynamics Without Knowledge
ASIND algorithm alternately identifies self-dynamics, interactive functions, and networks sparsely without prior knowledge, claiming state-of-the-art identification and 100-step prediction on network dynamics.
-
Adjacent Possible Innovation Dynamics on Local Optima Networks
Local Optima Networks model innovation as random walks on graphs of locally optimal configurations and jointly reproduce Heaps', Zipf's, Taylor's laws plus power-law inter-event times.