# Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection

arXiv: https://arxiv.org/abs/1910.01180 · HTML: https://mlsanity.com/paper/1910.01180/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency](https://mlsanity.com/paper/2208.08320/) (2022)
- [Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/2208.06651/) (2022)
- [MulBot: Unsupervised Bot Detection Based on Multivariate Time Series](https://mlsanity.com/paper/2209.10361/) (2022)
- [BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection](https://mlsanity.com/paper/2506.13795/) (2025)
- [BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline](https://mlsanity.com/paper/2306.00037/) (2023)
- [BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference](https://mlsanity.com/paper/2503.03775/) (2025)
- [RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection](https://mlsanity.com/paper/2510.23648/) (2025)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)

## Abstract (arXiv)

Neural networks are increasingly used for graph classification in a variety of contexts. Social media is a critical application area in this space, however the characteristics of social media graphs differ from those seen in most popular benchmark datasets. Social networks tend to be large and sparse, while benchmarks are small and dense. Classically, large and sparse networks are analyzed by studying the distribution of local properties. Inspired by this, we introduce Graph-Hist: an end-to-end architecture that extracts a graph's latent local features, bins nodes together along 1-D cross sections of the feature space, and classifies the graph based on this multi-channel histogram. We show that Graph-Hist improves state of the art performance on true social media benchmark datasets, while still performing well on other benchmarks. Finally, we demonstrate Graph-Hist's performance by conducting bot detection in social media. While sophisticated bot and cyborg accounts increasingly evade traditional detection methods, they leave artificial artifacts in their conversational graph that are detected through graph classification. We apply Graph-Hist to classify these conversational graphs. In the process, we confirm that social media graphs are different than most baselines and that Graph-Hist outperforms existing bot-detection models.