Distributed, Egocentric Representations of Graphs for Detecting Critical Structures

Ruo-Chun Tzeng, Shan-Hung Wu
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6354-6362, 2019.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-tzeng19a, title = {Distributed, Egocentric Representations of Graphs for Detecting Critical Structures}, author = {Tzeng, Ruo-Chun and Wu, Shan-Hung}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {6354--6362}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/tzeng19a/tzeng19a.pdf}, url = {https://proceedings.mlr.press/v97/tzeng19a.html}, 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.} }
Endnote
%0 Conference Paper %T Distributed, Egocentric Representations of Graphs for Detecting Critical Structures %A Ruo-Chun Tzeng %A Shan-Hung Wu %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-tzeng19a %I PMLR %P 6354--6362 %U https://proceedings.mlr.press/v97/tzeng19a.html %V 97 %X 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.
APA
Tzeng, R. & Wu, S.. (2019). Distributed, Egocentric Representations of Graphs for Detecting Critical Structures. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:6354-6362 Available from https://proceedings.mlr.press/v97/tzeng19a.html.

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