Predictive Random Graph Ranking on the Web
Abstract
The incomplete information about the Web structure causes inaccurate results of various ranking algorithms. In this paper, we propose a solution to this problem by formulating a new framework called, Predictive Random Graph Ranking, in which we generate a random graph based on the known information about the Web structure. The random graph can be considered as the predicted Web structure, on which ranking algorithm are expected to be improved in accuracy. For this purpose, we extend some current ranking algorithms from a static graph to a random graph. Experimental results show that the Predictive Random Graph Ranking framework can improve the accuracy of the ranking algorithms such as PageRank, Common Neighbor, and Jaccard's Coefficient.
Authors: Haixuan Yang, Irwin King, Michael Rung-Tsong Lyu
Published in: International Joint Conference on Neural Networks (IJCNN) (2006)