Understanding Missing Links in Bipartite Networks With MissBiN
Understanding Missing Links in Bipartite Networks With MissBiN
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DOI：
10.1109/tvcg.2020.3032984
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发表时间：
2020-10
影响因子：
5.2
通讯作者：
Jian Zhao;Maoyuan Sun;Francine Chen;Patrick Chiu
中科院分区：
文献类型：
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作者：
Jian Zhao;Maoyuan Sun;Francine Chen;Patrick Chiu
在各种应用领域中，双方网络的分析至关重要，例如在智能分析中探索实体共发生并研究生物信息学中的基因表达。一项重要的任务是缺少链接预测，该预测基于当前观察到的链接的存在。在本文中，我们提出了一个视觉分析系统Missbin，将分析师参与循环，以了解链接预测结果。 Missbin通过利用网络中的BI-CLIQUES信息来为在两部分网络中链接预测的新方法。它还提供了一个交互式可视化，以了解算法输出。 Missbin的设计基于三个高级分析问题（什么，原因和方式），这些问题是从文献和专家访谈中提取的。我们进行了定量实验，以评估拟议的链接预测算法的性能，并采访了来自不同领域的两名专家，以证明Missbin整体的有效性。我们还提供了全面的用法方案，以说明该工具在智能分析应用中的有用性。
The analysis of bipartite networks is critical in a variety of application domains, such as exploring entity co-occurrences in intelligence analysis and investigating gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of unseen links based on currently observed ones. In this article, we propose a visual analysis system, MissBiN, to involve analysts in the loop for making sense of link prediction results. MissBiN equips a novel method for link prediction in a bipartite network by leveraging the information of bi-cliques in the network. It also provides an interactive visualization for understanding the algorithm outputs. The design of MissBiN is based on three high-level analysis questions (what, why, and how) regarding missing links, which are distilled from the literature and expert interviews. We conducted quantitative experiments to assess the performance of the proposed link prediction algorithm, and interviewed two experts from different domains to demonstrate the effectiveness of MissBiN as a whole. We also provide a comprehensive usage scenario to illustrate the usefulness of the tool in an application of intelligence analysis.