Link Prediction in Scientists Collaboration with Author Name and Affiliation
Link Prediction in Scientists Collaboration with Author Name and Affiliation
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DOI：
10.1109/scis-isis.2016.0058
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Muhammad Ilias Amin;K. Murase
中科院分区：
文献类型：
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作者：
Muhammad Ilias Amin;K. Murase
链接预测任务是根据现有信息从网络中发现间接关系。研究了科学家/作者网络，在作者网络中加入隶属关系信息，以提高链接预测的性能。在这项研究中，排名的合作/边缘，我们使用边缘的评分算法，而不是基于节点的评分算法，并使用二分图数据结构，寻找活跃的作者。我们引入了一个名为作者多样性/相似性分数的功能，它描述了作者以相同或不同的从属关系写作的概率。与现有的系统相比，我们提出的算法的性能显着增加。
Link prediction task is to discover the indirect relationships from the network based on present information. We studied scientists/authors network and added affiliation information into the author's network in order to enhance the performance of link prediction. In this study, for ranking the collaborations/edges, we have used edge based scoring algorithms rather than node based scoring algorithms and used bipartite graph data structure for finding active authors. We have introduced a feature named Author's Diversity/Similarity score that describes the probability of an author to write with the same or different affiliation. The performance of our proposed algorithm has increased significantly comparing with the existing system.