4 research outputs found
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
One prevalent property we find empirically in real-world graph anomaly
detection (GAD) datasets is a one-class homophily, i.e., normal nodes tend to
have strong connection/affinity with each other, while the homophily in
abnormal nodes is significantly weaker than normal nodes. However, this
anomaly-discriminative property is ignored by existing GAD methods that are
typically built using a conventional anomaly detection objective, such as data
reconstruction. In this work, we explore this property to introduce a novel
unsupervised anomaly scoring measure for GAD -- local node affinity -- that
assigns a larger anomaly score to nodes that are less affiliated with their
neighbors, with the affinity defined as similarity on node
attributes/representations. We further propose Truncated Affinity Maximization
(TAM) that learns tailored node representations for our anomaly measure by
maximizing the local affinity of nodes to their neighbors. Optimizing on the
original graph structure can be biased by non-homophily edges (i.e., edges
connecting normal and abnormal nodes). Thus, TAM is instead optimized on
truncated graphs where non-homophily edges are removed iteratively to mitigate
this bias. The learned representations result in significantly stronger local
affinity for normal nodes than abnormal nodes. Extensive empirical results on
six real-world GAD datasets show that TAM substantially outperforms seven
competing models, achieving over 10% increase in AUROC/AUPRC compared to the
best contenders on challenging datasets. Our code will be made available at
https: //github.com/mala-lab/TAM-master/.Comment: 19 pages, 9 figure
Truncated Affinity Maximization: One-class homophily modeling for graph anomaly detection
Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.
OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI