论文标题
简化图形卷积网络：基于矩阵分解的视角
Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective
论文作者
论文摘要
近年来，在图形卷积网络（GCN）上取得了巨大进展。但是，GCN的计算通常需要较大的存储空间来保留整个图形。因此，GCN不够灵活，尤其是对于复杂的现实世界应用中的大规模图。幸运的是，基于基质分解（MF）的方法自然支持构建迷你批次，因此与GCN相比，对分布式计算更友好。因此，在本文中，我们分析了GCN和MF之间的连接，并将GCN简化为矩阵分解，并通过统一和共训练。此外，在我们的分析的指导下，我们提出了一个替代模型，该模型为nitughting和Co-Training矩阵分解（UCMF）提出了一个替代模型。在几个现实世界数据集上进行了广泛的实验。关于半监督节点分类的任务，实验结果表明，与GCN相比，UCMF的性能相似或出色。同时，分布式UCMF明显胜过分布的GCN方法，这表明UCMF可以极大地受益于大型和复杂的现实世界应用。此外，我们还对典型的图形嵌入任务（即社区检测）进行了实验，所提出的UCMF模型的表现优于几个代表性的图形嵌入模型。
In recent years, substantial progress has been made on Graph Convolutional Networks (GCNs). However, the computing of GCN usually requires a large memory space for keeping the entire graph. In consequence, GCN is not flexible enough, especially for large scale graphs in complex real-world applications. Fortunately, methods based on Matrix Factorization (MF) naturally support constructing mini-batches, and thus are more friendly to distributed computing compared with GCN. Accordingly, in this paper, we analyze the connections between GCN and MF, and simplify GCN as matrix factorization with unitization and co-training. Furthermore, under the guidance of our analysis, we propose an alternative model to GCN named Unitized and Co-training Matrix Factorization (UCMF). Extensive experiments have been conducted on several real-world datasets. On the task of semi-supervised node classification, the experimental results illustrate that UCMF achieves similar or superior performances compared with GCN. Meanwhile, distributed UCMF significantly outperforms distributed GCN methods, which shows that UCMF can greatly benefit large scale and complex real-world applications. Moreover, we have also conducted experiments on a typical task of graph embedding, i.e., community detection, and the proposed UCMF model outperforms several representative graph embedding models.
