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A Method for Community Detection in Networks with Mixed Scale Features at Its Nodes

机译:在其节点处具有混合尺度特征的网络中的社区检测方法

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The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is to find relatively dense groups of interconnected entities sharing some features in common. Algorithms based on probabilistic community models require the node features to be categorical. We use a data-driven model by combining the least-squares data recovery criteria for both, the graph structure and node features. This allows us to take into account both quantitative and categorical features. After deriving an equivalent complementary criterion to optimize, we apply a greedy-wise algorithm for detecting communities in sequence. We experimentally show that our proposed method is effective on both real-world data and synthetic data. In the cases at which attributes are categorical, we compare our approach with state-of-the-art algorithms. Our algorithm appears competitive against them.
机译:在其节点中具有特征的网络中社区检测的问题考虑了图形结构和节点特征。 目标是找到相对密集的互联实体,共享一些共同的特征。 基于概率社区模型的算法要求节点功能是分类的。 我们通过组合既有规范数据恢复标准,使用数据驱动模型,图形结构和节点特征。 这使我们能够考虑定量和分类特征。 在获得优化的等效互补标准之后,我们应用一种贪婪的算法来依次检测社区。 我们通过实验表明我们的提出方法对真实世界和合成数据有效。 在属性是分类的情况下,我们将我们的方法与最先进的算法进行比较。 我们的算法似乎对它们具有竞争力。

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