Generalized Reciprocal Perspective
Kevin Dick, Daniel G. Kyrollos, James R. Green

TL;DR
This paper introduces Reciprocal Perspective, a semi-supervised machine learning method that enhances link prediction accuracy by utilizing comprehensive prediction matrices, demonstrating broad applicability across various network-based problems.
Contribution
The paper generalizes a novel semi-supervised learning approach, Reciprocal Perspective, for all pairwise link prediction tasks, leveraging comprehensive prediction matrices to improve accuracy.
Findings
RP significantly improves link prediction accuracy.
Application of RP in a cascade yields consistent performance gains.
RP is applicable across diverse link prediction problems.
Abstract
Across many domains, real-world problems can be represented as a network. Nodes represent domain-specific elements and edges capture the relationship between elements. Leveraging high-performance computing and optimized link prediction algorithms, it is increasingly possible to evaluate every possible combination of nodal pairs enabling the generation of a comprehensive prediction matrix (CPM) that places an individual link prediction score in the context of all possible links involving either node (providing data-driven context). Historically, this contextual information has been ignored given exponentially growing problem sizes resulting in computational intractability; however, we demonstrate that expending high-performance compute resources to generate CPMs is a worthwhile investment given the improvement in predictive performance. In this work, we generalize for all pairwise…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Bioinformatics and Genomic Networks
