The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Characterizing and Mining the Citation Graph of the Computer Science Literature
Knowledge and Information Systems
SIAM Journal on Scientific Computing
Mining web multi-resolution community-based popularity for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A random walk on the red carpet: rating movies with user reviews and pagerank
Proceedings of the 17th ACM conference on Information and knowledge management
Automatic classification of citation function
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
SentiRank: Cross-Domain Graph Ranking for Sentiment Classification
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Link analysis, eigenvectors and stability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Answering opinion questions with random walks on graphs
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
User-level sentiment analysis incorporating social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiresolution Web Link Analysis Using Generalized Link Relations
IEEE Transactions on Knowledge and Data Engineering
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Measurement of graph centrality provides us with an indication of the importance or popularity of each vertex in a graph. When dealing with graphs that are not centrally controlled (such as the Web, social networks and academic citation graphs), centrality measure must 1) correlate with vertex importance/popularity, 2) scale well in terms of computation, and 3) be difficult to manipulate by individuals. The Random Surfer probability transition model, combined with Eigenvalue Centrality produced PageRank, which has shown to satisfy the required properties. Existing centrality measures (including PageRank) make the assumption that all directed edges are positive, implying an endorsement. Recent work on sentiment analysis has shown that this assumption is not valid. In this article, we introduce a new method of transitioning a graph, called Power Walk, that can successfully compute centrality scores for graphs with real weighted edges. We show that it satisfies the desired properties, and that its computation time and centrality ranking is similar to when using the Random Surfer model for non-negative matrices. Finally, stability and convergence analysis shows us that both stability and convergence when using the power method, are dependent on the Power Walk parameter β.