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Random Walks Based Modularity: Application to Semi-Supervised Learning

机译：基于随机游走的模块化：在半监督学习中的应用

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Although criticized for some of its limitations, modularity remains a standard measure for analyzing social networks. Quantifying the statistical surprise in the arrangement of the edges of the network has led to simple and powerful algorithms. However, relying solely on the distribution of edges instead of more complex structures such as paths limits the extent of modularity. Indeed, recent studies have shown restrictions of optimizing modularity, for instance its resolution limit. We introduce here a novel, formal and well-defined modularity measure based on random walks. We show how this modularity can be computed from paths induced by the graph instead of the traditionally used edges. We argue that by computing modularity on paths instead of edges, more informative features can be extracted from the network. We verify this hypothesis on a semi-supervised classification procedure of the nodes in the network, where we show that, under the same settings, the features of the random walk modularity help to classify better than the features of the usual modularity. Additionally, the proposed approach outperforms the classical label propagation procedure on two data sets of labeled social networks.
机译：尽管因其某些局限性而受到批评，但模块化仍然是分析社交网络的标准方法。量化网络边缘排列中的统计意外值已经导致了简单而强大的算法。但是，仅依靠边缘的分布而不是更复杂的结构（例如路径）会限制模块化的程度。实际上，最近的研究显示出优化模块化的限制，例如其分辨率极限。我们在此介绍一种基于随机游走的新颖，正式且定义明确的模块化度量。我们展示了如何从图诱导的路径而不是传统使用的边缘计算出这种模块化。我们认为，通过在路径而不是边缘上计算模块化，可以从网络中提取更多的信息特征。我们在网络中节点的半监督分类过程中验证了这一假设，在此过程中，我们证明了在相同设置下，随机游走模块的功能比常规模块的功能更好地进行了分类。此外，所提出的方法在两个带有标签的社交网络数据集上的表现优于传统的标签传播程序。

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