Conformal Inductive Graph Neural Networks
Soroush H. Zargarbashi, Aleksandar Bojchevski
TL;DR
This paper introduces conformal prediction methods tailored for inductive graph neural networks, ensuring valid, distribution-free coverage guarantees even with message passing shifts, applicable to both node and edge-exchangeable graphs.
Contribution
It develops a novel conformal prediction framework for inductive GNNs that maintains coverage guarantees despite message passing shifts, extending conformal methods to more realistic graph learning scenarios.
Findings
Guarantees valid coverage in inductive GNN settings
Applicable to both node and edge-exchangeable graphs
Coverage holds at any prediction time
Abstract
Conformal prediction (CP) transforms any model's output into prediction sets guaranteed to include (cover) the true label. CP requires exchangeability, a relaxation of the i.i.d. assumption, to obtain a valid distribution-free coverage guarantee. This makes it directly applicable to transductive node-classification. However, conventional CP cannot be applied in inductive settings due to the implicit shift in the (calibration) scores caused by message passing with the new nodes. We fix this issue for both cases of node and edge-exchangeable graphs, recovering the standard coverage guarantee without sacrificing statistical efficiency. We further prove that the guarantee holds independently of the prediction time, e.g. upon arrival of a new node/edge or at any subsequent moment.
Peer Reviews
Decision·ICLR 2024 poster
The key insight of this paper is to demonstrate that the impact of new data points on the calibration and evaluation sets is symmetric, ensuring that the shift embedding still maintains its guarantee, conditional to the graph at a specific time step.
(1) Notations: The presentation of this paper leaves room for improvement, as some notations are either omitted or unclear, making it misleading for readers. For instance, in Equation (3), the graph is not explicitly noted, and on page 5, in the linear message passing example, the meanings of A, X, and W are not immediately clear. Although these aspects become clearer upon reading the entire paper, the initial lack of clarity can impede readers from following the logic in their first encounter.
This is an interesting conformal prediction problem. The paper seems solid with a large amount of experiments. The code of experiments is available in the supplementary material.
1\ I found the paper difficult to follow. For instance, the setting of transductive node-classification of Section 2.1 or the inductive one of Section 3 are not properly defined. Another example is Fig.1 which is, in my opinion, not very clear and not explained properly. The definition of a graph is never given etc. Globally, the paper lacks explanation. 2\ There is a misuse of several words/expressions to discuss known concepts, which ultimately confuses the paper. What is the added value of a
1. Significance of contribution. Conformal prediction on graph data has just recently become a popular topic. This topic is important for deploying GNNs in critical domains. While existing discussion mainly focuses on transductive setting, or only provides approximate and not-actionable guarantees for other settings, this paper stands out by providing solid theory, method, and experiments on node-exchangeable and edge-exchangeable settings. By thoroughly exploring the exchangeability structure
My complaints are not detrimental to this paper. I'm happy with the main ideas of this paper, so they are just comments that should be addressed to make the paper stronger. Conformal prediction relies crucially rigorous mathematical statements about how data is generated / how test data appears in the set, how score functions are defined, how the score is trained and applied. These are all important factors for the validity of CP. While I believe the technical discussion in this paper is corre
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Taxonomy
TopicsNeural Networks and Applications