Download Algorithms and Models for the Web-Graph: 7th International by Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar PDF

By Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar (eds.)

This booklet constitutes the refereed complaints of the seventh overseas Workshop on Algorithms and types for the Web-Graph, WAW 2010, held in Stanford, CA, united states, in December 2010, which was once co-located with the sixth overseas Workshop on net and community Economics (WINE 2010).

The thirteen revised complete papers and the invited paper provided have been conscientiously reviewed and chosen from 19 submissions.

Show description

Read or Download Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010. Proceedings PDF

Similar algorithms books

Automated Planning: Theory & Practice (The Morgan Kaufmann Series in Artificial Intelligence)

Automatic making plans expertise now performs an important function in numerous hard purposes, starting from controlling area automobiles and robots to enjoying the sport of bridge. those real-world purposes create new possibilities for synergy among idea and perform: staring at what works good in perform ends up in larger theories of making plans, and higher theories bring about larger functionality of sensible purposes.

Web Data Management

The net and world-wide-web have revolutionized entry to info. clients now shop info throughout a number of structures from own pcs, to smartphones, to web content equivalent to Youtube and Picasa. thus, facts administration strategies, tools, and methods are more and more interested by distribution matters.

Algorithms and Data Structures for External Memory (Foundations and Trends(R) in Theoretical Computer Science)

Info units in huge purposes are usually too gigantic to slot thoroughly contained in the computer's inner reminiscence. The ensuing input/output conversation (or I/O) among quick inner reminiscence and slower exterior reminiscence (such as disks) could be a significant functionality bottleneck. Algorithms and information constructions for exterior reminiscence surveys the state-of-the-art within the layout and research of exterior reminiscence (or EM) algorithms and information constructions, the place the aim is to take advantage of locality and parallelism so as to lessen the I/O expenses.

Genetic Algorithms and Genetic Programming in Computational Finance

After a decade of improvement, genetic algorithms and genetic programming became a broadly permitted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering quantity dedicated fullyyt to a scientific and entire evaluation of this topic.

Extra info for Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010. Proceedings

Sample text

Let us consider all Θ(n2 ) pairs {v1 , v2 }, where v1 ∈ V1 , v2 ∈ V2 , which are independent in RIG, (but not in RIG), hence the probability that two nodes v1 , v2 ∈ V are connected in RIG is given by (1 − pˆ2w ) = 1 − 1− w (1 − p2γ w )= w p2γ w + o( w p2γ w ), (33) w since γ > 1 and pw = O(1/n) for any w. Given that w p2w = c/n, we choose 2 γ > 1 so that w p2γ w = ω(1/n ). Now, by the Markov inequality, whp there is a pair {v1 , v2 } such that v1 is connected to v2 in RIG, implying that V1 , V2 are connected, whp, forming one connected component within RIGnew .

While it is fair to assume some vertices might have been misclassified in the ground-truth data, there should be a penalty for such vertices. Thus we have two objectives while computing α: (i) justifying the location of each vertex (ii) maximizing the overall quality of the clustering. Justifying Locations of Individual Vertices. For each vertex v ∈ V we define the pull to each cluster C k in C = C 1 , C 2 , . . C K to be the cumulative weights of edges between v and its neighbors in C k , Computing an Aggregate Edge-Weight Function Pα (v, Ck ) = wi (α) 29 (2) wi =(u,v)∈E;u∈C k We further define the holding power, Hα (v) for each vertex, to be the pull of the cluster to which the vertex belongs in C ∗ minus the next largest pull among the remaining clusters.

More importantly, by only looking at the holding powers we can preserve the original modularity quality. The reason for this is that we have relatively small clusters, and almost all vertices have a connection with a cluster besides their own. , much larger clusters), then this would not have been the case, and having a separate quality of clustering metric would have made sense. However, we know that most complex networks have small communities no matter how big the graphs are [4]. Therefore, we expect that looking only at the holding powers of vertices will be sufficient to recover aggregation functions.

Download PDF sample

Rated 4.88 of 5 – based on 26 votes