Random Walks and Electric Networks
By Peter G. Doyle, J. Snell. 2000
277 citations recorded by the source corpus.
A popular account of the connection between random walks and electric networks.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/5b842a19bf6c0feaa4559ac70d282957fd1ac87f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Christos Faloutsos
- Andrew Tomkins
- Thomas G. Dietterich
- Hanghang Tong
- Charu C. Aggarwal
- Gérard Biau
- Leman Akoglu
- Danai Koutra
In Statistical and Nonlinear Physics
- Christos Faloutsos
- Andrew Tomkins
- Thomas G. Dietterich
- Kevin S. McCurley
- Jure Leskovec
- Hanghang Tong
- Jon M. Kleinberg
- Jia-Yu Pan
Citations in the corpus
- Algorithms for Detecting Significantly Mutated Pathways in Cancer (2010, unknown): “Note that this diffusion process is related to the diffusion kernel (Kondor and Lafferty, 2002) or heat kernel (Chung, 2007), which models the diffusion of heat on a graph, and these diffusion processes are in turn related to certain random walks on graphs (Doyle and Snell, 1984; Lova´sz, 1993).”
- Adaptive computation and machine learning (1998, background): “Laplacian semi-supervised learning (SSL) is more closely related to random walks on networks (Doyle and Snell, 1984): each arc encodes the similarity between the nodes at its endpoints, and the goal is to use neighborhood structure to guide the choice of classification (or regression, clustering, or”
- Fast discovery of connection subgraphs (2004, background): “However, again, in all these references, we have not found even the definition of the connection subgraph problem, let alone an attempt to solve it.”
- Resistance distance and the normalized Laplacian spectrum (2007, unknown): “In this paper, we will use the intimate relations between random walks on graphs and electrical networks [6,18,3] to study the resistance distance.”
- Graph Sparsification by Effective Resistances (unknown): “The effective resistance of an edge is known to be equal to the probability that the edge appears in a random spanning tree of G (see, e.g., [8] or [5]), and was proven in [6] to be proportional to the commute time between the endpoints of the edge.”