By Anthony Bonato, Fan Chung Graham, Pawel Pralat
This booklet constitutes the refereed lawsuits of the eleventh foreign Workshop on Algorithms and types for the net Graph, WAW 2014, held in Beijing, China, in December 2014.
The 12 papers provided have been conscientiously reviewed and chosen for inclusion during this quantity. the purpose of the workshop used to be to additional the knowledge of graphs that come up from the internet and diverse consumer actions on the internet, and stimulate the advance of high-performance algorithms and functions that make the most those graphs. The workshop amassed the researchers who're engaged on graph-theoretic and algorithmic points of similar complicated networks, together with social networks, quotation networks, organic networks, molecular networks, and different networks bobbing 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
Random Walk with Jumps In , the authors introduced a process with artiﬁcial jumps. It is suggested in  to add artiﬁcial edges with weights a/n between each two nodes to the graph. This process creates self-loops as well. Thus, the new modiﬁed graph is a combination of the original graph and a complete graph with self-loops. Let us demonstrate that this is a particular case of the introduced generalized deﬁnition of Personalized PageRank. Speciﬁcally, we deﬁne the damping factors as αi = di , di + a i ∈ V, (31) and as the restart distribution we take the uniform distribution (v = 1/n).
Neural Networks 31, 53–72 (2012) 17. : Topic-Sensitive PageRank. In: Proceedings of WWW 2002 (2002) 18. : Random Graphs and Complex Networks, Lecture notes in preparation (2014) (preprint). nl/∼rhofstad/NotesRGCN. html 19. : Co-authorship networks in the digital library research community. Information Processing & Management 41, 1462–1480 (2005) 20. : Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys 2007), pp. 17–24 (2007) 21. : Numerical Computing with MATLAB.
Secondly, the generalized Personalized PageRank can be applied as a proximity measure between nodes in semi-supervised machine learning [5,16]. , nodes with very large degrees. And thirdly, the generalized Personalized PageRank can be applied for spam detection and control. It is known  that spam web pages are often designed to be ranked highly. By using the Location-of-Restart Personalized PageRank and penalizing the ranking of spam pages with small restart probability, one can push the spam pages from the top list produced by search engines.
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