Graph data is ubiquitous in academia and industry, from social networks to bioinformatics. The pervasiveness of graphs today has raised the demand for algorithms that can answer various questions: Which products would a user like to purchase given her order list? Which users are buying fake followers to increase their public reputation? Myriads of new graph mining 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 a practitioner to compare different algorithms and pick the most suitable one for a specific application. These challenges - even more severe for non-experts - create a gap in which state-of-the-art techniques developed in academic settings 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 that integrates various message-passing based graph algorithms, ranging from conventional algorithms like PageRank to graph neural networks. Then 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 incorporate a practical issue - finding the best speed-accuracy trade-off under a computation budget - into the graph algorithm generation problem. Experiments on real-world benchmark datasets demonstrate that AUTOGM generates novel graph mining algorithms with the best speed/accuracy trade-off compared to existing models with heuristic parameters.

中文翻译：

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具有最佳速度/精度折衷的自主图挖掘算法搜索

从社交网络到生物信息学，图形数据在学术界和工业界无处不在。如今，图形的普遍性提高了对可以回答各种问题的算法的需求：给定订单清单，用户希望购买哪些产品？哪些用户购买了假粉丝，以提高其公众声誉？每年都会提出无数新的图形挖掘算法来回答此类问题-每个问题都有独特的问题表述，计算时间和内存占用量。这种缺乏统一性的做法使从业人员很难比较不同的算法并为特定应用选择最合适的算法。这些挑战-对于非专家而言更为严峻-造成了差距，在这种差距下，学术环境中开发出的最新技术未能在实际应用中得到最佳部署。为了弥合这一差距，我们提出了AUTOGM，这是一种用于图形挖掘算法开发的自动化系统。我们首先定义一个统一的框架UNIFIEDGM，该框架集成了各种基于消息传递的图形算法，范围从传统算法（例如PageRank）到图形神经网络。然后UNIFIEDGM定义一个搜索空间，其中需要五个参数来确定图形算法。在此搜索空间下，AUTOGM使用贝叶斯优化对UNIFIEDGM的最佳参数集进行显式优化。AUTOGM为优化定义了一个新颖的预算感知目标函数，以将一个实际问题（在计算预算下找到最佳的速度准确性权衡）纳入图形算法生成问题。