Unveiling the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works have underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Different from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.

中文翻译：

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一致性：节点属性网络的路径感知同质性度量


揭示表征复杂网络接线模式的同质/异质行为是社交网络分析中的一项重要任务，通常用于研究节点属性的分类混合。最近的研究强调，量化节点同质性的全局测量必然提供部分的、常常是欺骗性的现实情况。从这些文献出发，在这项工作中，我们提出了一种新颖的衡量标准，即一致性，旨在通过提供以节点为中心的分类混合模式量化来克服这种限制。与迄今为止提出的措施不同，Conformity被设计为路径感知的，从而可以更详细地评估不同分离程度的节点对目标同质嵌入的影响。对合成数据和真实数据的实验分析使我们能够观察到一致性可以从节点属性图中揭示有价值的见解。