# Inductive detection of Influence Operations via Graph Learning

arXiv: https://arxiv.org/abs/2305.16544 · HTML: https://mlsanity.com/paper/2305.16544/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 16 of 16)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Graph Neural Networks with Learnable Structural and Positional Representations](https://mlsanity.com/paper/2110.07875/) (2021)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [On the Detection of Disinformation Campaign Activity with Network Analysis](https://mlsanity.com/paper/2005.13466/) (2020)
- [Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls](https://mlsanity.com/paper/1811.03130/) (2018)
- [Automatic Detection of Influential Actors in Disinformation Networks](https://mlsanity.com/paper/2005.10879/) (2020)
- [Bots, Disinformation, and the First Trump Impeachment](https://mlsanity.com/paper/2204.08915/) (2022)
- [The Half-Life of a Tweet](https://mlsanity.com/paper/2302.09654/) (2023)

## Built on by (most cited first, 2 of 2)

- [Connecting the geometry and dynamics of many-body complex systems with message passing neural operators](https://mlsanity.com/paper/2502.15913/) (2025)
- [Density-aware Walks for Coordinated Campaign Detection](https://mlsanity.com/paper/2506.13912/) (2025)

## Abstract (arXiv)

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations which evade current detection methods and influence public discourse on social media with greater scale, reach, and specificity. New methods with inductive learning capacity will be needed to identify these novel operations before they indelibly alter public opinion and events. We develop an inductive learning framework which: 1) determines content- and graph-based indicators that are not specific to any operation; 2) uses graph learning to encode abstract signatures of coordinated manipulation; and 3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicatorsunicodex2013illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.