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- W2945572065 abstract "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." @default.
- W2945572065 created "2019-05-29" @default.
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- W2945572065 date "2019-06-23" @default.
- W2945572065 modified "2023-10-16" @default.
- W2945572065 title "Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" @default.
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