Random walk with restart: fast solutions and applications

By Hanghang Tong, Christos Faloutsos, Jia-Yu Pan. Knowledge and Information Systems, 2008

385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9ebf0f5f36d1504e96d147f38e5b9e21d7d28825

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

1. Hanghang Tong
2. Santo Fortunato
3. Jimeng Sun
4. Marco Saerens
5. U. Kang
6. Alain Pirotte
7. Luh Yen
8. François Fouss

In Computer Vision and Pattern Recognition

1. Santo Fortunato
2. Jimeng Sun
3. Marco Saerens
4. U. Kang
5. Alain Pirotte
6. Luh Yen
7. François Fouss
8. Kevin Françoisse

Citations in the corpus

• Network propagation: a universal amplifier of genetic associations (2017, neutral): “For example, graph theoreticians investigate random walks on graphs 11,15 ; the data science community applies variants of the Google PageRank search algorithm 12,16 ; statistical physicists study heat diffusion processes 17,18 ; electrical engineers compute minimum energy states within an…”
• Fast Random Walk Graph Kernel (2012, background): “, random walk with restart [27, 39], sinkaugmented delivered current [8], cycle free effective conductance [22], survivable network [13], direction-aware proximity [38], ObjectRank [2], RelationalRank [11], SimRank [18] and its fast approximation [24] Low-Rank Approximation.”
• EnrichNet: network-based gene set enrichment analysis (2012, neutral): “1) can be differentiated using their Xd-distances.”
• An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification (2012, methodology): “(2006) (see also Tong et al., 2006; Tong et al., 2008) introduced a similarity matrix inspired by the well-known PageRank algorithm (Brin & Page, 1998; Page et al.”
• Community detection in graphs (2009, methodology): “Extensions of k-means clustering to graphs have been proposed by some authors (Hlaoui and Wang, 2004; Rattigan et al., 2007; Schenker et al., 2003).”
• Stochastic resetting and applications (2019, background): “The idea is that a stochastic algorithm may get stuck before completing the intended task and therefore it is advantageous to simply restart the algorithm [20–24].”