Diffusion-convolutional neural networks (DCNN) is a model for graph-structured data. Through the introduction of a diffusion-convolution operation, diffusion-based representations can be learned from graph structured data and used as an effective basis for node classification.

Description and image from: [Diffusion-Convolutional Neural Networks](https://arxiv.org/pdf/1511.02136.pdf)

**Scaled Exponential Linear Units**, or **SELUs**, are activation functions that induce self-normalizing properties.

The SELU activation function is given by 

$$f\left(x\right) = \lambda{x} \text{ if } x \geq{0}$$
$$f\left(x\right) = \lambda{\alpha\left(\exp\left(x\right) -1 \right)} \text{ if } x < 0 $$

with $\alpha \approx 1.6733$ and $\lambda \approx 1.0507$.

SELU

Self-Normalizing Neural Networks

DCNN

Diffusion-Convolutional Neural Networks

**Generalizable Node Injection Attack**, or **G-NIA**, is an attack scenario for graph neural networks where the attacker injects malicious nodes rather than modifying original nodes or edges to affect the performance of GNNs. G-NIA generates the discrete edges also by Gumbel-Top-𝑘 following OPTI and captures the coupling effect between network structure and node features by a sophisticated designed model. 

 G-NIA explicitly models the most critical feature propagation via jointly modeling. Specifically, the malicious attributes are adopted to guide the generation of edges, modeling the influence of attributes and edges. G-NIA also adopts a model-based framework, utilizing useful information of attacking during model training, as well as saving computational cost during inference without re-optimization.

Source	Diffusion-Convolutional Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com