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Node Aggregation for Enhancing PageRank
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DOI：10.1109/ACCESS.2017.2750700.png)
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Abstract
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In this paper, we study the problem of node aggregation under different perspectives for increasing PageRank of some nodes of interest. PageRank is one of the parameters used by the search engine Google to determine the relevance of a web page. We focus our attention to the problem of finding the best nodes in the network from an aggregation viewpoint, i.e., what are the best nodes to merge with for the given nodes. This problem is studied from global and local perspectives. Approximations are proposed to reduce the computation burden and to overcome the limitations resulting from the lack of centralized information. Several examples are presented to illustrate the different approaches that we propose.
Keywords:
Networks
game theory
graphs
centrality measures
model reduction
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