By Ravi Kumar, D Sivakumar
This e-book constitutes the refereed lawsuits of the seventh overseas Workshop on Algorithms and versions for the Web-Graph, WAW 2010, held in Stanford, CA, united states, in December 2010, which used to be co-located with the sixth foreign Workshop on net and community Economics (WINE 2010). The thirteen revised complete papers and the invited paper provided have been rigorously reviewed and chosen from 19 submissions.
Read Online or Download Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings PDF
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Additional resources for Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings
Org and considered abstract text similarity, title similarity, citation links, and shared authors as edge types for these articles. ) as a proxy for the region where the work was done. We used these regions as the ground-truth clustering. 0016 for titles. This means the shared authors edge type is almost entirely favored, with cross-citations coming a distant second. This is intuitive because a network of articles linked by common authors will be linked both by topic (we work with people in our field) but also by geography (we often work with people in nearby institutions) whereas edge types like abstract text similarity tend to encode only the topic of a paper, which is less geographically correlated.
Since the algorithm we used is nondeterministic, the number of function evaluations, hence runtimes vary even for different runs on the same problem, and thus are less informative. We are not presenting these results in detail due to space constraints. However, we want to reemphasize that the size of the optimization problem does not grow with the graph size, and we don’t expect the number of functions evaluations to cause any scalaFig. 6. Scalability of the proposed method bility problems. We also observed that the number of function evaluations increase linearly with the number of similarity metrics.
The first approach is based on inverse problems, and we try to find weighting parameters for which the clustering on the graph yields the ground-truth clustering. The second approach computes weighting parameters that maximizes the quality of the ground-truth clustering. 1 Solving an Inverse Problem Inverse problems arise in many scientific computing applications where the goal is to infer unobservable parameters from finite observations. Solutions typically involve iterations of taking guesses and then solving the forward problems to compute the quality of the guess.
Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings by Ravi Kumar, D Sivakumar