Summary of Online Gnn Evaluation Under Test-time Graph Distribution Shifts, by Xin Zheng et al.
Online GNN Evaluation Under Test-time Graph Distribution Shifts
by Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, Shirui Pan
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach for evaluating the performance of Graph Neural Networks (GNNs) on real-world graphs, particularly in scenarios where the training graph is unknown during test time. The authors develop an effective learning behavior discrepancy score called LeBeD to estimate the generalization errors of well-trained GNN models. This metric integrates learning behavior discrepancies from node prediction and structure reconstruction perspectives, enabling the evaluation of a GNN’s ability to capture test node semantics and structural representations. The paper demonstrates the effectiveness of LeBeD through extensive experiments on real-world test graphs under diverse graph distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about figuring out how well trained GNNs work in real-life situations where we don’t know what kind of data they were trained on beforehand. This is important because it affects how reliable those GNNs are when used online. The researchers created a new way to measure how good GNNs are at doing this, called LeBeD. It looks at two things: how well the GNN can predict what nodes are like and how well it can understand the structure of the graph. This helps us understand if the GNN is good at working with new data that’s different from what it was trained on. |
Keywords
* Artificial intelligence * Generalization * Gnn * Semantics
