Biased random walk on a graph

In network science, a biased random walk on a graph is a time path process in which an evolving variable jumps from its current state to one of various potential new states; unlike in a pure random walk, the probabilities of the potential new states are unequal.

Biased random walks on a graph provide an approach for the structural analysis of undirected graphs in order to extract their symmetries when the network is too complex or when it is not large enough to be analyzed by statistical methods. The concept of biased random walks on a graph has attracted the attention of many researchers and data companies over the past decade especially in the transportation and social networks.[1]

  1. ^ Roberta Sinatra; Jesús Gómez-Gardeñes; Renaud Lambiotte; Vincenzo Nicosia; Vito Latora (March 2011). "Maximal-entropy random walks in complex networks with limited information". Physical Review E. 83 (3): 030103. arXiv:1007.4936. Bibcode:2011PhRvE..83c0103S. doi:10.1103/PhysRevE.83.030103. PMID 21517435. S2CID 6984660.