有向图嵌入的协同双聚集算法
Collaborative Bi-Aggregation for Directed Graph Embedding
摘要：
有向图是节点间非对称关系的模型，有向图嵌入的研究对下游的图分析和推理具有重要意义。分别学习源节点和目标节点的嵌入以保持边缘不对称性已成为主流方法，但也对稀疏图中普遍存在的低甚至零输入/输出度节点的学习表示提出了挑战。本文通过引入基于空间的图卷积，提出了一种协同双向聚合有向图嵌入方法（COBA）。首先，通过聚合源邻居和目标邻居的对应节点来学习中心节点的源嵌入和目标嵌入;其次，通过聚合方向相反的邻居的对应节点来增强零入/出度中心节点的源/目标嵌入（即目标/源邻居）;最后，将同一节点的源嵌入和目标嵌入相关联以实现协作聚合。在真实数据集上的大量实验表明，COBA在多个任务上的性能全面优于最先进的方法，同时验证了所提出的聚合策略的有效性。
英文摘要：
Directed graphs model asymmetric relationships between nodes and research on directed graph embedding is of great significance in downstream graph analysis and inference. Learning source and target embedding of nodes separately to preserve edge asymmetry has become the dominant approach, but also poses challenge for learning representations of low or even zero in/out degree nodes that are ubiquitous in sparse graphs. In this paper, a collaborative bi-directional aggregation method (COBA) for directed graphs embedding is proposed by introducing spatial-based graph convolution. Firstly, the source and target embeddings of the central node are learned by aggregating from the counterparts of the source and target neighbors, respectively; Secondly, the source/target embeddings of the zero in/out degree central nodes are enhanced by aggregating the counterparts of opposite-directional neighbors (i.e. target/source neighbors); Finally, source and target embeddings of the same node are correlated to achieve collaborative aggregation. Extensive experiments on real-world datasets demonstrate that the COBA comprehensively outperforms state-of-the-art methods on multiple tasks and meanwhile validates the effectiveness of proposed aggregation strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
