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Visual analysis framework for understanding missing links in bipartite networks

專利號
US11176460B2
公開日期
2021-11-16
申請人
FUJI XEROX CO., LTD.(JP Tokyo)
發(fā)明人
Jian Zhao; Francine Chen; Patrick Chiu
IPC分類
G06N5/02; G06N20/00
技術(shù)領(lǐng)域
bicliques,bipartite,missing,links,network,biclique,prediction,in,link,algorithm
地域: Tokyo

摘要

Example implementations described herein involve an interface for calculating and displaying missing links for data represented as a bipartite network, along with novel methods for improving link prediction algorithms in the related art. Through example implementations described herein, the accuracy of link prediction algorithms can be improved upon, thereby providing the user with a more accurate understanding of the data in the bipartite network.

說明書

FIG. 1 illustrates an example system diagram, in accordance with an example implementation. In example implementations described herein, data 100 is processed through a framework that involves an analysis module and a visualization module. The analysis module supports missing link prediction in bipartite networks 101 as well as two of the most common ways for observing networks, including node metrics 103 and sub-network motifs 102. The link prediction method as described herein leverages the structural information of bicliques in the networks, which can be integrated with any related art similarity-based link prediction algorithms. The visualization module displays all the outputs from the analysis module and enables analysts to explore the data with rich user interactions. An analyst can visually investigate the identified missing links 104, network motifs 105, and node metrics 106, and further examine the influence of particular links by comparing the analytical results on the original network and the one with these links added.

權(quán)利要求

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