标题： 基于集成的深度多层社区搜索 显示英文标题

标题： Ensemble-based Deep Multilayer Community Search

摘要： 多层图由多个相互连接的层组成，被广泛用于建模现实世界中的各种关系。 社区是一个凝聚的子图，为分析（多层）图提供了有价值的见解。 最近，出现了一种新兴趋势，即在多层图中搜索查询驱动的社区。 然而，现有的多层社区搜索方法要么是基于规则的，存在结构灵活性不足的问题；要么是基于学习的，依赖于标记数据或无法捕捉层特定的特征。 为了解决这些问题，我们提出了 EnMCS，一种基于集成的无监督（即无标签）多层社区 搜索框架。 EnMCS包含两个关键组件，即HoloSearch，它在每个层中识别潜在社区的同时整合层共享和层特定信息，以及EMerge，这是一种基于期望最大化（EM）的方法，将每个层的潜在社区综合成一个共识社区。 具体来说， HoloSearch首先采用一种基于图扩散的模型，该模型整合了三个无标签损失函数，以学习每个节点的层特定和层共享表示。 然后根据在层共享表示中表现出高相似性而在层特定表示中相对于查询节点表现出低相似性的节点来识别每层中的社区。 为了在合并社区时考虑每层不同的层特定特征，EMerge将各层的错误率和真实社区作为潜在变量进行建模。 然后它使用EM算法同时最小化各层的错误率，并通过迭代最大似然估计预测最终的共识社区。 在10个真实数据集上的实验突显了EnMCS在效率和有效性方面的优越性。 显示英文摘要

摘要： Multilayer graphs, consisting of multiple interconnected layers, are widely used to model diverse relationships in the real world. A community is a cohesive subgraph that offers valuable insights for analyzing (multilayer) graphs. Recently, there has been an emerging trend focused on searching query-driven communities within the multilayer graphs. However, existing methods for multilayer community search are either 1) rule-based, which suffer from structure inflexibility; or 2) learning-based, which rely on labeled data or fail to capture layer-specific characteristics. To address these, we propose EnMCS, an Ensemble-based unsupervised (i.e., label-free) Multilayer Community Search framework. EnMCS contains two key components, i.e., HoloSearch which identifies potential communities in each layer while integrating both layer-shared and layer-specific information, and EMerge which is an Expectation-Maximization (EM)-based method that synthesizes the potential communities from each layer into a consensus community. Specifically, HoloSearch first employs a graph-diffusion-based model that integrates three label-free loss functions to learn layer-specific and layer-shared representations for each node. Communities in each layer are then identified based on nodes that exhibit high similarity in layer-shared representations while demonstrating low similarity in layer-specific representations w.r.t. the query nodes. To account for the varying layer-specific characteristics of each layer when merging communities, EMerge models the error rates of layers and true community as latent variables. It then employs the EM algorithm to simultaneously minimize the error rates of layers and predict the final consensus community through iterative maximum likelihood estimation. Experiments over 10 real-world datasets highlight the superiority of EnMCS in terms of both efficiency and effectiveness.