Random Walks on Graphs: A Survey

By L. Lov'asz. 2001

747 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/99ae64fdd0e1cf6ede9b4f4a18a4323fd05f29d1

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Citations in the corpus

Challenging the Long Tail Recommendation (2012, background): “ollowing part, we propose a basic solution called Hitfoundation to recommend long tail items. Intermsofthestationarydistributionmentionedabove,itiseasy to formulate the property of time-reversibility [18]: it is equivalent 898 to say that for every pair i;j 2 V , i pi;j = j pj;i , where pi;j denot”
Discovering popular routes from trajectories (2011, methodology): “Furthermore, we can consider a travel on such a transfer network based on the turning probability as a Random Walk [30] on a directed graph with the transition probability from node ni to nj equals to Prd(ni → nj).”
Cutoff phenomena for random walks on random regular graphs (2008, background): “In [5] the authors compare the SRW and this projection of the NBRW on regular expander graphs, showing that the NBRW has a faster mixing rate (see [22] for the definition of this spectral parameter, which for the SRW coincides with the largest nontrivial eigenvalue in absolute value).”
Deep Learning on Graphs: A Survey (2018, unknown): “n Eq. (35) with the positive pointwise mutual information (PPMI) [58] matrix of a random surﬁng probability. In this way, the raw features can associate with some random walk probability of the graph [87]. However, constructing the input matrix can take O(N2) time complexity, which is not scalable t”
Extremal Properties of Random Structures (2003, unknown): “For a concise review we refer the reader to [74].”
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs (2004, methodology): “Approaches to approximate counting were developed in order to cope with the complexity of exact counting in other types of problems (Lovasz, 1993; Jerrum and Sinclair, 1996; Jerrum, 2003).”