On the Application of Link Analysis Algorithms for Ranking Bipartite Graphs
Antonia Korba

TL;DR
This paper introduces BipartiteRank, a novel algorithm for ranking items in bipartite graphs, leveraging a modified teleportation approach inspired by PageRank, supported by mathematical analysis and experiments on real data.
Contribution
The paper presents BipartiteRank, an innovative ranking algorithm tailored for bipartite graphs, with a new teleportation method based on graph structure, improving efficiency over existing methods.
Findings
BipartiteRank effectively ranks bipartite graph data.
The new teleportation method enhances ranking efficiency.
Experimental results confirm the algorithm's advantages.
Abstract
Recently bipartite graphs have been widely used to represent the relationship two sets of items for information retrieval applications. The Web offers a wide range of data which can be represented by bipartite graphs, such us movies and reviewers in recomender systems, queries and URLs in search engines, users and posts in social networks. The size and the dynamic nature of such graphs generate the need for more efficient ranking methods. In this thesis, at first we present the fundamental mathematical backround that we use subsequently and we describe the basic principles of the Perron-Frobebius theory for non negative matrices as well as the the basic principles of the Markov chain theory. Then, we propose a novel algorithm named BipartiteRank, which is suitable to rank scenarios, that can be represented as a bipartite graph. This algorithm is based on the random surfer model and…
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Taxonomy
TopicsComplex Network Analysis Techniques · Spam and Phishing Detection · Opinion Dynamics and Social Influence
