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Visual analysis framework for understanding missing links in bipartite networks

專(zhuān)利號(hào)
US11176460B2
公開(kāi)日期
2021-11-16
申請(qǐng)人
FUJI XEROX CO., LTD.(JP Tokyo)
發(fā)明人
Jian Zhao; Francine Chen; Patrick Chiu
IPC分類(lèi)
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(shuō)明書(shū)

FIG. 3 illustrates an example interface for facilitating the visual exploration of missing links, in accordance with an example implementation. In the example interface of FIG. 3, there are several views. First, the Network View 300 as shown in interface pane (a), displays the bi-adjacency matrix of a bipartite network, where the row and the column represent two different types of nodes respectively. The links can be represented as squares in the intersections of rows and columns. The existing links in the network can be shown in a first color scale (e.g., yellow-green), where the hue reflects the weight of a link. If it is an unweighted network, all the links are shown in the darkest hue (e.g., green). The predicted missing links are displayed in a second color scale (e.g., white-purple), where a darker color reflects higher probability or score determined by the link prediction algorithm.

Further, the Link List View 301 as shown in interface pane (b) is configured to present the missing links linearly by probability or score, where each link is visualized in a similar fashion with that in the Network View 300. Additional information such as the rank and the connecting nodes of the link is provided. This Link List View 301 works together with the Network View 300, allowing an analyst to better make sense of the missing link prediction in different perspectives.

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