Proximity Preserving Nonnegative Matrix Factorization
Proximity Preserving Nonnegative Matrix Factorization
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DOI：
10.2197/ipsjjip.28.445
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yuya Ogawa;Koh Takeuchi;Yuya Sasaki;Makoto Onizuka
中科院分区：
文献类型：
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作者：
Yuya Ogawa;Koh Takeuchi;Yuya Sasaki;Makoto Onizuka
我们考虑社区发现的问题。尽管最近的网络嵌入和表示学习方法越来越流行，但我们声称它们属于社区检测的次优解决方案，因为它们是基于间接方法，该方法需要将K-Means（例如K-Means）（例如K-均值）应用于嵌入/表示向量。我们介绍了PPNMF，即保持非负矩阵分解以进行社区检测。 PPNMF的想法是三个含量。 1）PPNMF基于直接方法：它直接最大程度地减少其损失函数以供社区检测。 2）用户可以控制观察到的边缘在未观察到的边缘上的重要性。 3）PPNMF可以精确捕获顶点与社区的一阶和二阶接近的影响。另外，PPNMF采用Adamic ADAR指数作为二阶接近度。该实验验证了PPNMF的性能更好或与各种实际数据集中的现有方法相媲美，以完成社区检测任务。
We consider the problem of community detection. Although network embedding and representation learning methods are recently getting popular, we claim that they fall into suboptimal solutions for community detection, because they are based on indirect approach, which requires to apply clustering methods such as k-means to the embedding/representation vectors. We present PPNMF, proximity preserving nonnegative matrix factorization for community detection. The idea of PPNMF is three-hold. 1) PPNMF is based on direct approach: it directly minimizes its loss function for community detection. 2) Users can control the importance of observed edges over unobserved edges. 3) PPNMF can precisely capture the effects of the first-order and second-order proximities of vertexes to communities. Also, PPNMF employs the Adamic Adar index as the second-order proximity. The experiments validate that PPNMF performs better or comparable to existing methods in various real datasets for the tasks of community detection.