This content originally appeared on HackerNoon and was authored by Computational Technology for All
:::info Authors:
(1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk;
(2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk.
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Table of Links
5 A Case Study on Shortest-Path Distance
6 Conclusion and Discussion, and References
9 Procedure for Solving Eq. (6)
10 Additional Experiments Details and Results
11 Other Potential Applications
8 Proof of Theorem 2
Before diving into the detailed proof, we present an outline of the structure of the proof and prove a lemma which we use in the proof of the theorem. Outline of the proof for the thereom
\ 1. Suppose we are given Vi and Vj , two test groups which satisfy the premise of the theorem;
\ 2. Then, we can approximate and bound the loss of each vertex in these groups based on the nearest vertex in the training set by extending the result from Theorem 1;
\ 3. If we can show that there exists a constant independent of the property of each test group, then we obtain the results of Theorem 2.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Computational Technology for All
Computational Technology for All | Sciencx (2024-10-22T07:00:13+00:00) Unfair Generalization in Graph Neural Networks (GNNs). Retrieved from https://www.scien.cx/2024/10/22/unfair-generalization-in-graph-neural-networks-gnns/
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