Network Embedding Exploration Tool (NEExT)

Author:

Dehghan Ashkan,Prałat Paweł,Théberge François

Publisher

Springer Nature Switzerland

Reference25 articles.

1. Aggarwal, M., Murty, M.N.: Machine learning in social networks: embedding nodes, edges, communities, and graphs. Springer Nature (2020)

2. Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings. In: International Conference on Learning Representations (2017)

3. Barrett, J., Kamiński, B., Pankratz, B., Prałat, P., Théberge, F.: Self-similarity of communities of the ABCD model. preprint, arXiv (2023)

4. Borgwardt, K.M., Ong, C.S., Schönauer, S., Vishwanathan, S., Smola, A.J., Kriegel, H.P.: Protein function prediction via graph kernels. Bioinformatics 21(suppl_1), i47–i56 (2005)

5. Cuturi, M.: Sinkhorn distances: Lightspeed computation of optimal transport. In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. (eds.) Advances in Neural Information Processing Systems. vol. 26. Curran Associates, Inc. (2013)

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