Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited. In particular, most state-of-the-art methods generating node embeddings of signed networks focus on link sign prediction, and those that pertain to node clustering are usually not graph neural network (GNN) methods. Here, we introduce a novel probabilistic balanced normalized cut loss for training nodes in a GNN framework for semi-supervised signed network clustering, called SSSNET. The method is end-to-end in combining embedding generation and clustering without an intermediate step; it has node clustering as main focus, with an emphasis on polarization effects arising in networks. The main novelty of our approach is a new take on the role of social balance theory for signed network embeddings. The standard heuristic for justifying the criteria for the embeddings hinges on the assumption that "an enemy's enemy is a friend". Here, instead, a neutral stance is assumed on whether or not the enemy of an enemy is a friend. Experimental results on various data sets, including a synthetic signed stochastic block model, a polarized version of it, and real-world data at different scales, demonstrate that SSSNET can achieve comparable or better results than state-of-the-art spectral clustering methods, for a wide range of noise and sparsity levels. SSSNET complements existing methods through the possibility of including exogenous information, in the form of node-level features or labels.
翻译:节点嵌入是分析网络的有力工具; 然而,它们对于节点分组重要任务的全部潜力尚未充分挖掘。 特别是, 多数最先进的生成节点嵌入网络的最新方法, 重点是链接符号预测, 而那些与节点组合相关的方法通常不是图形神经网络( GNN) 的方法。 在这里, 我们为在半受监督的已签名网络集群 GNN 框架中培训节点, 称为 SSSNET, 引入一种新的概率平衡的常规切换损失。 这种方法是将嵌入生成和分组同时不采取中间步骤的端对端组合; 它以节点组合为主要焦点, 重点是网络中产生的两极分效应。 我们的方法的主要新颖之处是, 社会平衡理论对于签名网络嵌入网络( GNNNN) 的方法的作用。 用于解释嵌入标准的标准超常量取决于“ 敌人是朋友” 的假设。 这里, 一种中立立场是假设敌人的敌人是否是朋友的模型和组合; 它以节点组合组合为主要焦点, 重点是网络中的极点效果, 包括Sloveal roal roal roal roal 和 Sal roal roal 。 在Sal roal 的模型中, 在Sal 的模型中, roalalalalalalal 的模型上的结果可以显示, 在Sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- d- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- s- sal- sal- sal- sal- sal- sal- sal- sal- s