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Abstract The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs always yields optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity results of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics. The source code to reproduce experimental results is available athttps://github.com/Kingsford-Group/gtednewilp/.
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This content will become publicly available on December 31, 2025
Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims
This article presents the latest developments to ClaimBuster’s claim-spotting model, which tackles the critical task of identifying check-worthy claims from large streams of information. We introduce the first adversarially regularized, transformer-based claim-spotting model, which achieves state-of-the-art results on several benchmark datasets. In addition to analyzing model performance metrics, we also quantitatively and qualitatively analyze the impact of ClaimBuster’s real-world deployment. Moreover, to help facilitate reproducibility and community engagement, we publicly release our codebase, dataset, data curation platform, API, Google Colab notebooks, and various ClaimBuster-based demo systems, atclaimbuster.org.
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- Award ID(s):
- 2346261
- PAR ID:
- 10644849
- Publisher / Repository:
- Association for Computing Machinery
- Date Published:
- Journal Name:
- ACM Transactions on Intelligent Systems and Technology
- Volume:
- 15
- Issue:
- 6
- ISSN:
- 2157-6904
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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