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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 8: AAAI-21 Technical Tracks 8

Cost-aware Graph Generation: A Deep Bayesian Optimization Approach

February 1, 2023

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Authors

Jiaxu Cui

Jilin University


Bo Yang

Jilin University


Bingyi Sun

Jilin University National University of Defense Technology


Jiming Liu

Hong Kong Baptist University


DOI:

10.1609/aaai.v35i8.16878


Abstract:

Graph-structured data is ubiquitous throughout the natural and social sciences, ranging from complex drug molecules to artificial neural networks. Evaluating their functional properties, e.g., drug effectiveness and prediction accuracy, is usually costly in terms of time, money, energy, or environment, becoming a bottleneck for the graph generation task. In this work, from the perspective of saving cost, we propose a novel Cost-Aware Graph Generation (CAGG) framework to generate graphs with optimal properties at as low cost as possible. By introducing a robust Bayesian graph neural network as the surrogate model and a goal-oriented training scheme for the generation model, the CAGG can approach the real expensive evaluation function and generate search space close to the optimal property, to avoid unnecessary evaluations. Intensive experiments conducted on two challenging real-world applications, including molecular discovery and neural architecture search, demonstrate its effectiveness and applicability. The results show that it can generate the optimal graphs and reduce the evaluation costs significantly compared to the state-of-the-art.

Topics: AAAI

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HOW TO CITE:

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu Cost-aware Graph Generation: A Deep Bayesian Optimization Approach Proceedings of the AAAI Conference on Artificial Intelligence (2021) 7142-7150.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu Cost-aware Graph Generation: A Deep Bayesian Optimization Approach AAAI 2021, 7142-7150.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu (2021). Cost-aware Graph Generation: A Deep Bayesian Optimization Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 7142-7150.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu. Cost-aware Graph Generation: A Deep Bayesian Optimization Approach. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.7142-7150.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu. 2021. Cost-aware Graph Generation: A Deep Bayesian Optimization Approach. "Proceedings of the AAAI Conference on Artificial Intelligence". 7142-7150.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu. (2021) "Cost-aware Graph Generation: A Deep Bayesian Optimization Approach", Proceedings of the AAAI Conference on Artificial Intelligence, p.7142-7150

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu, "Cost-aware Graph Generation: A Deep Bayesian Optimization Approach", AAAI, p.7142-7150, 2021.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu. "Cost-aware Graph Generation: A Deep Bayesian Optimization Approach". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.7142-7150.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu. "Cost-aware Graph Generation: A Deep Bayesian Optimization Approach". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 7142-7150.

Jiaxu Cui||Bo Yang||Bingyi Sun||Jiming Liu. Cost-aware Graph Generation: A Deep Bayesian Optimization Approach. AAAI[Internet]. 2021[cited 2023]; 7142-7150.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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