**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.

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)

DCNN

Diffusion-Convolutional Neural Networks

G-NIA

Single Node Injection Attack against Graph Neural Networks

DiffPool is a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer.

Description and image from: [Hierarchical Graph Representation Learning with Differentiable Pooling](https://arxiv.org/pdf/1806.08804.pdf)

Source	Single Node Injection Attack against Graph Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com