**Discrete Cosine Transform (DCT)** is an orthogonal transformation method that decomposes an
image to its spatial frequency spectrum. It expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. It is used a lot in compression tasks, e..g image compression where for example high-frequency components can be discarded. It is a type of Fourier-related Transform, similar to discrete fourier transforms (DFTs), but only using real numbers.

Image Credit: [Wikipedia](https://en.wikipedia.org/wiki/Discrete_cosine_transform#/media/File:Example_dft_dct.svg)

**Graph Contrastive Coding** is a self-supervised graph neural network pre-training framework to capture the universal network topological properties across multiple networks. GCC's pre-training task is designed as subgraph instance discrimination in and across networks and leverages contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com