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Detecting which nodes in graphs are outliers is a relatively new machinelearning task with numerous applications. Despite the proliferation ofalgorithms developed in recent years for this task, there has been no standardcomprehensive setting for performance evaluation. Consequently, it has beendifficult to understand which methods work well and when under a broad range ofsettings. To bridge this gap, we present--to the best of our knowledge--thefirst comprehensive benchmark for unsupervised outlier node detection on staticattributed graphs called BOND, with the following highlights. (1) We benchmarkthe outlier detection performance of 14 methods ranging from classical matrixfactorization to the latest graph neural networks. (2) Using nine realdatasets, our benchmark assesses how the different detection methods respond totwo major types of synthetic outliers and separately to "organic" (realnon-synthetic) outliers. (3) Using an existing random graph generationtechnique, we produce a family of synthetically generated datasets of differentgraph sizes that enable us to compare the running time and memory usage of thedifferent outlier detection algorithms. Based on our experimental results, wediscuss the pros and cons of existing graph outlier detection algorithms, andwe highlight opportunities for future research. Importantly, our code is freelyavailable and meant to be easily extendable:https://github.com/pygod-team/pygod/tree/main/benchmark

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs