A problem that frequently occurs when mining complex networks is selecting algorithms with which to rank the relevance of nodes to metadata groups characterized by a small number of examples. The best algorithms are often found through experiments on labeled networks or unsupervised structural community quality measures. However, new networks could exhibit characteristics different from the labeled ones, whereas structural community quality measures favor dense congregations of nodes but not metadata groups spanning a wide breadth of the network. To avoid these shortcomings, in this work we propose using unsupervised measures that assess node rank quality across multiple metadata groups through their ability to reconstruct the local structures of network nodes; these are retrieved from the network and not assumed. Three types of local structures are explored: linked nodes, nodes up to two hops away and nodes forming triangles. We compare the resulting measures alongside unsupervised structural community quality ones to the AUC and NDCG of supervised evaluation in one synthetic and four real-world labelled networks. Our experiments suggest that our proposed local structure measures are often more accurate for unsupervised pairwise comparison of ranking algorithms, especially when few example nodes are provided. Furthermore, the ability to reconstruct the extended neighborhood, which we call HopAUC, manages to select a near-best among many ranking algorithms in most networks.

中文翻译：

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通过重构局部结构对多个节点等级进行无监督评估

当挖掘复杂的网络时，经常会出现一个问题，即选择算法对节点与以少量示例为特征的元数据组的相关性进行排名。最好的算法通常是通过在标记网络上进行的实验或无监督的结构社区质量度量来找到的。但是，新的网络可能表现出与标记的网络不同的特征，而结构性社区质量度量则倾向于密集的节点集合，而不是跨越整个网络的元数据组。为了避免这些缺点，在这项工作中，我们建议使用无监督措施，通过其重构网络节点的本地结构的能力来评估多个元数据组之间的节点等级质量。这些是从网络中检索的，而不是假定的。探讨了三种类型的局部结构：链接的节点，距离最多两跳的节点和形成三角形的节点。我们在一个合成的和四个真实的标记网络中，将所得的测量结果与无监督的结构社区质量测量结果与AUC和NDCG的有监督评估进行了比较。我们的实验表明，对于排名算法的无监督成对比较，我们提出的局部结构度量通常更准确，尤其是在提供少数示例节点的情况下。此外，重构扩展邻域的能力（我们称为HopAUC）设法在大多数网络的众多排名算法中选择了近乎最佳的。我们将结果度量与无监督的结构性社区质量度量与在一个合成的和四个真实的标记网络中的监督评估的AUC和NDCG进行了比较。我们的实验表明，对于排名算法的无监督成对比较，我们提出的局部结构度量通常更准确，尤其是在提供少数示例节点的情况下。此外，我们称为HopAUC的重建扩展邻域的能力设法在大多数网络中的许多排名算法中选择了近乎最佳的。我们将结果度量与无监督的结构性社区质量度量与在一个合成的和四个真实的标记网络中的监督评估的AUC和NDCG进行了比较。我们的实验表明，对于排名算法的无监督成对比较，我们提出的局部结构度量通常更准确，尤其是在提供少数示例节点的情况下。此外，我们称为HopAUC的重建扩展邻域的能力设法在大多数网络中的许多排名算法中选择了近乎最佳的。