We study the problem of detecting critical structures using a graph embedding model. Existing graph embedding models lack the ability to precisely detect critical structures that are specific to a task at the global scale. In this paper, we propose a novel graph embedding model, called the Ego-CNNs, that employs the ego-convolutions convolutions at each layer and stacks up layers using an ego-centric way to detects precise critical structures efficiently. An Ego-CNN can be jointly trained with a task model and help explain/discover knowledge for the task. We conduct extensive experiments and the results show that Ego-CNNs (1) can lead to comparable task performance as the state-of-the-art graph embedding models, (2) works nicely with CNN visualization techniques to illustrate the detected structures, and (3) is efficient and can incorporate with scale-free priors, which commonly occurs in social network datasets, to further improve the training efficiency.

本文提出了一种名为Ego-CNNs的新型图嵌入模型，利用自我卷积和各层层叠的中心方式来有效检测精确的关键结构，并可与任务模型联合训练，取得可比较的任务性能表现，且适用于CNN可视化技术，结合规模自由先验，进一步提高训练效率。

Ego-CNN: 分布式、以自我为中心的图表表示，用于检测关键结构