Toward Interpretable Graph Classification via Concept-Focused Structural Correspondence

Author:

Bui Tien-CuongORCID,Li Wen-SyanORCID

Publisher

Springer Nature Singapore

Reference26 articles.

1. Bui, T.C., Li, W.S.: Toward interpretable graph neural networks via concept matching model. In: 2023 IEEE International Conference on Data Mining (ICDM), pp. 950–955. IEEE (2023)

2. Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.K.: This looks like that: deep learning for interpretable image recognition. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

3. Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems, vol. 26 (2013)

4. Dai, E., Wang, S.: Towards self-explainable graph neural network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 302–311 (2021)

5. Davoudi, S.O., Komeili, M.: Toward faithful case-based reasoning through learning prototypes in a nearest neighbor-friendly space. In: International Conference on Learning Representations (2021)

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