Graph Bayesian Optimization: Algorithms, Evaluations and Applications
This work addresses network structure optimization for complex network analysis, representing an incremental advancement by extending Bayesian optimization to graph-based inputs.
The authors tackled the problem of optimizing network structures by introducing a flexible framework called graph Bayesian optimization, which can handle arbitrary graphs and identify important features during optimization, demonstrating its efficacy on four problems.
Network structure optimization is a fundamental task in complex network analysis. However, almost all the research on Bayesian optimization is aimed at optimizing the objective functions with vectorial inputs. In this work, we first present a flexible framework, denoted graph Bayesian optimization, to handle arbitrary graphs in the Bayesian optimization community. By combining the proposed framework with graph kernels, it can take full advantage of implicit graph structural features to supplement explicit features guessed according to the experience, such as tags of nodes and any attributes of graphs. The proposed framework can identify which features are more important during the optimization process. We apply the framework to solve four problems including two evaluations and two applications to demonstrate its efficacy and potential applications.