# Density-aware Walks for Coordinated Campaign Detection

arXiv: https://arxiv.org/abs/2506.13912 · HTML: https://mlsanity.com/paper/2506.13912/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 29 of 29)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Rethinking Graph Transformers with Spectral Attention](https://mlsanity.com/paper/2106.03893/) (2021)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [Long Range Graph Benchmark](https://mlsanity.com/paper/2206.08164/) (2022)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [Structure-Aware Transformer for Graph Representation Learning](https://mlsanity.com/paper/2202.03036/) (2022)
- [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018)
- [Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings](https://mlsanity.com/paper/2201.13410/) (2022)
- [Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes](https://mlsanity.com/paper/2310.19285/) (2023)
- [Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter](https://mlsanity.com/paper/2310.09884/) (2023)
- [Unveiling Coordinated Groups Behind White Helmets Disinformation](https://mlsanity.com/paper/2003.01313/) (2020)
- [A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links](https://mlsanity.com/paper/2103.03409/) (2021)
- [Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack](https://mlsanity.com/paper/2302.04450/) (2023)
- [Trend Alert: How a Cross-Platform Organization Manipulated Twitter Trends in the Indian General Election](https://mlsanity.com/paper/2104.13259/) (2021)
- [Characterizing Retweet Bots: The Case of Black Market Accounts](https://mlsanity.com/paper/2112.02366/) (2021)
- [Analyzing Activity and Suspension Patterns of Twitter Bots Attacking Turkish Twitter Trends by a Longitudinal Dataset](https://mlsanity.com/paper/2304.07907/) (2023)
- [Coordinated Reply Attacks in Influence Operations: Characterization and Detection](https://mlsanity.com/paper/2410.19272/) (2024)
- [Towards Understanding Trends Manipulation in Pakistan Twitter](https://mlsanity.com/paper/2109.14872/) (2021)
- [Inductive detection of Influence Operations via Graph Learning](https://mlsanity.com/paper/2305.16544/) (2023)
- [#TeamFollowBack: Detection & Analysis of Follow Back Accounts on Social Media](https://mlsanity.com/paper/2403.15856/) (2024)
- [Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends](https://mlsanity.com/paper/2503.00599/) (2025)

## Abstract (arXiv)

Coordinated campaigns frequently exploit social media platforms by artificially amplifying topics, making inauthentic trends appear organic, and misleading users into engagement. Distinguishing these coordinated efforts from genuine public discourse remains a significant challenge due to the sophisticated nature of such attacks. Our work focuses on detecting coordinated campaigns by modeling the problem as a graph classification task. We leverage the recently introduced Large Engagement Networks (LEN) dataset, which contains over 300 networks capturing engagement patterns from both fake and authentic trends on Twitter prior to the 2023 Turkish elections. The graphs in LEN were constructed by collecting interactions related to campaigns that stemmed from ephemeral astroturfing. Established graph neural networks (GNNs) struggle to accurately classify campaign graphs, highlighting the challenges posed by LEN due to the large size of its networks. To address this, we introduce a new graph classification method that leverages the density of local network structures. We propose a random weighted walk (RWW) approach in which node transitions are biased by local density measures such as degree, core number, or truss number. These RWWs are encoded using the Skip-gram model, producing density-aware structural embeddings for the nodes. Training message-passing neural networks (MPNNs) on these density-aware embeddings yields superior results compared to the simpler node features available in the dataset, with nearly a 12% and 5% improvement in accuracy for binary and multiclass classification, respectively. Our findings demonstrate that incorporating density-aware structural encoding with MPNNs provides a robust framework for identifying coordinated inauthentic behavior on social media networks such as Twitter.