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 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.
Ruo-Chun Tzeng (Microsoft Inc.)
Shan-Hung (Brandon) Wu (National Tsing Hua University)
Related Events (a corresponding poster, oral, or spotlight)
2019 Poster: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures »
Thu Jun 13th 06:30 -- 09:00 PM Room Pacific Ballroom