Autonomous graph mining algorithm search with best performance trade-off

The pervasiveness of graphs today has raised the demand for algorithms to answer various questions: Which products would a user like to purchase given her order list? Which users are buying fake followers? Myriads of new graph algorithms are proposed every year to answer such questions—each with a distinct problem formulation, computational time, and memory footprint. This lack of unity makes it difficult for practitioners to compare different algorithms and pick the most suitable one for their application. These challenges create a gap in which state-of-the-art techniques developed in academia fail to be optimally deployed in real-world applications. To bridge this gap, we propose AutoGM, an automated system for graph mining algorithm development. We first define a unified framework UnifiedGM for message-passing-based graph algorithms. UnifiedGM defines a search space in which five parameters are required to determine a graph algorithm. Under this search space, AutoGM explicitly optimizes for the optimal parameter set of UnifiedGM using Bayesian Optimization. AutoGM defines a novel budget-aware objective function for the optimization to find the best speed-accuracy trade-off in algorithms under a computation budget. On various real-world datasets, AutoGM generates novel graph algorithms with the best speed/accuracy trade-off compared to existing models with heuristic parameters.