基于梯度的重叠式层次社区检测

社区检测(community detection)任务一直是数据挖掘领域的一个研究热点,近年来,深度学习和图链接数据呈现出多样化和复杂化的发展趋势,层次(Hierarchical)社区检测逐渐成为研究的焦点.层次社区检测任务的目标是,在将同质图中相似的节点聚集到社区中的同时,学习社区之间的层次结构关系,以更好的理解图数据结构.社区间层次关系的引入给社区检测算法带来了更复杂的建模挑战.针对该任务,已经有一些有效的启发式的方法被提出,但是受限于社区分布形态的简单假设和离散的优化学习方式,它们无法描述更复杂的图链路数据,也无法和其它有效的连续优化算法组合获得更好的结果.为了解决这个问题,本文首次尝试建模复杂的重叠式(overlapping)层次社区结构,提出简洁的节点嵌入和社区检测双任务优化模型,通过梯度更新的方式来灵活地探索节点和重叠式层次社区的隶属关系.在学习过程中,我们可以分别获得节点和社区的嵌入表示,以应用于丰富的下游任务.

Gradient-Based Overlapping Hierarchical Community Detection

Community detection task is a hotspot in data mining. In recent years, deep learning and graph data have been increasingly diverse and complex, and the task of hierarchical community detection has gradually become a focus of research. The goal of this task is to learn the hierarchical relationship between communities while gathering similar nodes in homogeneous graphs to better understand the graph data structure. The introduction of this relationship poses a higher modeling challenge to community detection algorithms. For this task, some effective heuristic methods have been proposed. However, limited by the simple assumptions of community distribution and discrete optimization learning methods, these methods cannot describe more complex graph data, nor can they be combined with other effective continuous optimization algorithms. To solve this issue, we first attempt to model a complex overlapping hierarchical community structure and propose a simple dual-task optimization model of node embedding and community detection. The relationship of nodes and overlapping hierarchical communities can be flexibly explored through gradient updates. In the learning process, we can also obtain the embedding representations of nodes and communities to apply to rich downstream tasks.