# CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks

arXiv: https://arxiv.org/abs/2402.17363 · HTML: https://mlsanity.com/paper/2402.17363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders](https://mlsanity.com/paper/1802.03480/) (2018)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning](https://mlsanity.com/paper/2008.09202/) (2020)
- [GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks](https://mlsanity.com/paper/1901.09165/) (2019)
- [Flow-based Network Traffic Generation using Generative Adversarial Networks](https://mlsanity.com/paper/1810.07795/) (2018)
- [Semi-supervised Learning on Graphs with Generative Adversarial Nets](https://mlsanity.com/paper/1809.00130/) (2018)
- [Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation](https://mlsanity.com/paper/2104.13316/) (2021)
- [Deep Graph Translation](https://mlsanity.com/paper/1805.09980/) (2018)
- [Deep Multi-attributed Graph Translation with Node-Edge Co-evolution](https://mlsanity.com/paper/2003.09945/) (2020)
- [XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics](https://mlsanity.com/paper/2207.09088/) (2022)
- [Few-shot Weakly-supervised Cybersecurity Anomaly Detection](https://mlsanity.com/paper/2304.07470/) (2023)
- [Sparse Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1710.09813/) (2017)
- [DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection](https://mlsanity.com/paper/2301.03368/) (2023)

## Abstract (arXiv)

Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT). Graph generative models are often used to address the issue of imbalanced node categories in dynamic graphs. Nevertheless, the constraints it faces include the monotonicity of adjacency relationships, the difficulty in constructing multi-dimensional features for nodes, and the lack of a method for end-to-end generation of multiple categories of nodes. In this paper, we propose a novel graph generation model, called CGGM, specifically for generating samples belonging to the minority class. The framework consists two core module: a conditional graph generation module and a graph-based anomaly detection module. The generative module adapts to the sparsity of the matrix by downsampling a noise adjacency matrix, and incorporates a multi-dimensional feature encoder based on multi-head self-attention to capture latent dependencies among features. Additionally, a latent space constraint is combined with the distribution distance to approximate the latent distribution of real data. The graph-based anomaly detection module utilizes the generated balanced dataset to predict the node behaviors. Extensive experiments have shown that CGGM outperforms the state-of-the-art methods in terms of accuracy and divergence. The results also demonstrate CGGM can generated diverse data categories, that enhancing the performance of multi-category classification task.