paper-embedding

논문 구현을 통한 알고리즘 공부 [CSE URP 23기]

Papers

1. B. Perozzi, R. Al-Rfou, and S. Skiena, “DeepWalk: Online learning of social representations,” in Proc. KDD’14, New York, NY USA.
2. J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “LINE: Large-scale information network embedding,” in Proc. WWW’15, Florence, Italy.
3. A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proc. KDD’16, San Francisco, CA USA.

목차

• 1. fully-connected neural network (loss)
• 2. fully-connected neural network (loss&validation loss)
• 3. network embedding (matrix factorization)
• 4. network embedding (word2vec : softmax)
• 5. network embedding (word2vec : hierarchical softmax)
• 6.
• . . .