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
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Modifications of Kleinberg's HITS algorithm using matrix exponentiation and web log records
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Minds and Machines
Graph-Theoretic Web Algorithms: An Overview
IICS '01 Proceedings of the International Workshop on Innovative Internet Computing Systems
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mining the Web to Discover the Meanings of an Ambiguous Word
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On a confidence gain measure for association rule discovery and scoring
The VLDB Journal — The International Journal on Very Large Data Bases
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Letting one's thoughts wander is not simply an arbitrary or rambling process. It can better be described as "associative thinking", where a complex chain of associative thoughts and ideas are linked. It is our contention that this seemingly chaotic process can be modeled by a random walk in a weighted directed graph. Furthermore, is it possible to predict mathematically the "steady state" of such a process, to determine where such wandering is leading. The random walk process uses rules of association, defined by the Local Confidence Gain (LCG) interestingness measure. Extracted concepts are used as nodes of a directed graph. The associative "forces" between any two concepts (measured by LCG) are used to weigh the edges connecting the nodes that create a graph of associations. It is common, yet not trivial, for people to look for data about a subject without knowing its exact nomenclature (for example, finding the name of a disease just by knowing its symptoms). Random walk in association graphs can discover highly informative phrases that can be used for query expansion in a way that better expresses the user's initial search goals. A different usage is to create a user profile representing his current interests. We used a modified version of the Turing Test to show that the random walk process discovers association rules that conform to a human associations generating process. By constructing the user associations we were able to build a profile representing the user's "line of thoughts". The suggested algorithm can be used in any database and can implement the ranking measures of other association rules.