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The Infinity Mirror Test for Graph Models
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DOI：10.1109/TKDE.2022.3140252.png)
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Abstract
En 中文
Graph models, like other machine learning models, have implicit and explicit biases built-in, which often impact performance in nontrivial ways. The model's faithfulness is often measured by comparing the newly generated graph against the source graph using any number of graph properties. Therefore, differences in the size or topology of the generated graph indicate a loss in the model. Yet, in many systems, errors encoded in loss functions are subtle and not well understood. In the present work, we introduce the Infinity Mirror test for analyzing the robustness of graph models. This straightforward stress test works by repeatedly fitting a model to its outputs. A hypothetically perfect graph model would have no deviation from the source graph; however, a model's implicit biases and assumptions are exaggerated by the Infinity Mirror test, exposing potential previously obscured issues. Through an analysis of thousands of experiments on synthetic and real-world graphs, we show that several conventional graph models degenerate in exciting and informative ways. We believe that the observed degenerative patterns are clues to the future development of better graph models.
Keywords:
Mirrors
Measurement
Predictive models
Computational modeling
Data models
Analytical models
Feature extraction
Graph models
methodology
biases
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