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Visual analysis framework for understanding missing links in bipartite networks

專利號(hào)
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

At 412, the flow provides ones from the set of missing links selected by the link prediction algorithm as predicted missing links of the bipartite network as illustrated in FIG. 3. This can involve presenting the bipartite network as a bi-adjacency matrix involving rows that represent a first type of node in the bipartite network, and columns that represent rows of a second type of node in the bipartite network, each of the entries in the matrix representing a link between the first type of node and the second type of node, as illustrated in FIG. 3. The link prediction algorithm can select the predicted missing links based on the score obtained by the link prediction algorithm for a particular link meeting a threshold, or all of the missing links can be displayed in the bi-adjacency matrix depending on the desired implementation. As illustrated in FIG. 3, the providing ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network can involve representing the entry as a color hue according to a score provided by the link prediction algorithm. Further as described in FIG. 3, the presenting the bipartite network can involve providing an interface configured to order the rows and columns of the bi-adjacency matrix according to a selected criteria (type of node, average score, etc.). Further as illustrated in FIG. 3, providing the ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network can involve presenting the predicted missing links linearly by probability.

權(quán)利要求

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