Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach

Author:

Akujuobi Uchenna,Kumari Priyadarshini,Choi Jihun,Badreddine Samy,Maruyama Kana,Palaniappan Sucheendra K.,Besold Tarek R.

Publisher

Springer Science and Business Media LLC

Reference73 articles.

1. Ahmed NM, Chen L, Wang Y et al. (2016) Sampling-based algorithm for link prediction in temporal networks. Inform Sci 374:1–14

2. Akujuobi U, Chen J, Elhoseiny M et al. (2020) Temporal positive-unlabeled learning for biomedical hypothesis generation via risk estimation. Adv Neural Inform Proc Syst 33:4597–4609

3. Akujuobi U, Spranger M, Palaniappan SK et al. (2020) T-pair: Temporal node-pair embedding for automatic biomedical hypothesis generation. IEEE Trans Knowledge Data Eng 34(6):2988–3001

4. Anderson RL, Wolf WJ (1995) Compositional changes in trypsin inhibitors, phytic acid, saponins and isoflavones related to soybean processing. J Nutr 125(suppl–3):581S-588S

5. Arthur D, Vassilvitskii S (2006) $$k$$-means++: The advantages of careful seeding. Stanford University, Tech. rep

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