By Anthony Bonato, Fan Chung Graham, Pawel Pralat
This publication constitutes the refereed court cases of the eleventh overseas Workshop on Algorithms and types for the internet Graph, WAW 2014, held in Beijing, China, in December 2014.
The 12 papers offered have been conscientiously reviewed and chosen for inclusion during this quantity. the purpose of the workshop was once to extra the knowledge of graphs that come up from the internet and diverse person actions on the internet, and stimulate the advance of high-performance algorithms and functions that take advantage of those graphs. The workshop collected the researchers who're engaged on graph-theoretic and algorithmic elements of comparable complicated networks, together with social networks, quotation networks, organic networks, molecular networks, and different networks coming up from the Internet.
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Additional info for Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings
The paper is organised as follows. In Section 2, we investigate the Occupation-Time Personalized PageRank. In Section 3, we investigate the Location-of-Restart Personalized PageRank. In Section 4, we specify the results for some particular interesting cases. We close in Section 5 with a discussion of our results and suggestions for future research. All proofs can be found in the accompanying research report . 2 Occupation-Time Personalized PageRank The Occupation-Time Personalized PageRank can be calculated explicitly as follows: 26 K.
We summarize in the following the main contributions of our work: 1. We show how to obtain eﬃcient estimators for several standard deﬁnitions of weighted clustering coeﬃcient. Our sampling algorithm are easily parallelizable too. 2. We introduce a novel notion of weighted clustering coeﬃcient. We base our proposal on the observation that edges with large weights are more likely to play a role in the social network. Our model deﬁnes a family of unweighted random graphs with edges existing with diﬀerent probabilities.
423–430 (July 2007) 12. : Directed random graphs with given degree distributions. Stochastic Systems 3, 147–186 (electronic) (2013) 13. : Finding scientiﬁc gems with Google’s PageRank algorithm. Journal of Informetrics 1(1), 8–15 (2007) 14. : Using Polynomial Chaos to Compute the Inﬂuence of Multiple Random Surfers in the PageRank Model. K. ) WAW 2007. LNCS, vol. 4863, pp. 82–95. Springer, Heidelberg (2007) Personalized PageRank with Node-Dependent Restart 33 15. : Random alpha PageRank. Internet Mathematics 6(2), 189–236 (2010) 16.
Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings by Anthony Bonato, Fan Chung Graham, Pawel Pralat