Large-Scale Nodes Classification With Deep Aggregation Network

Abstract

The most fundamental task of network representation learning (NRL) is nodes classification which requires an algorithm to map nodes to vectors and use machine learning models to predict nodes' labels. Recently, many methods based on neighborhood aggregation have achieved brilliant results in this task. However, the recursive expansion of neighborhood aggregation poses scalability and efficiency problems for deep models. Existing methods are limited to shallow architectures and cannot capture the high order proximity in networks. In this article, we propose the deep aggregation network (DAN). DAN uses a layer-wise greedy optimization strategy which stacks several sequential trained base models to form the final deep model. The high order neighborhood aggregation is performed in a dynamic programming manner, which allows the recursion nature of neighborhood aggregation to be eliminated. The reverse random walk is also proposed, and combined with the classic random walk in formulating a novel sampling strategy that allows DAN to flexibly adapt to different tasks related to communities or structural roles. DAN is more efficient and effective than previous neighborhood aggregation based methods, especially when it is intended to handle large-scale networks with dense connections. Extensive experiments are conducted on both synthetic and real-world networks to empirically demonstrate the effectiveness and efficiency of the proposed method.