# Jointly embedding the local and global relations of heterogeneous graph for rumor detection

arXiv: https://arxiv.org/abs/1909.04465 · HTML: https://mlsanity.com/paper/1909.04465/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 30 of 31)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2107.11934/) (2021)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks](https://mlsanity.com/paper/2110.04522/) (2021)
- [GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking](https://mlsanity.com/paper/2312.05739/) (2023)
- [Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks](https://mlsanity.com/paper/2209.08799/) (2022)
- [Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detection](https://mlsanity.com/paper/2503.09153/) (2025)
- [WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom](https://mlsanity.com/paper/2310.16579/) (2023)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media](https://mlsanity.com/paper/2105.04098/) (2021)
- [Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis](https://mlsanity.com/paper/2111.08001/) (2021)
- [Fake News Detection with Heterogeneous Transformer](https://mlsanity.com/paper/2205.03100/) (2022)
- [Rumor Detection with Diverse Counterfactual Evidence](https://mlsanity.com/paper/2307.09296/) (2023)
- [Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks](https://mlsanity.com/paper/2212.10778/) (2022)
- [Fake News Detection Through Graph-based Neural Networks: A Survey](https://mlsanity.com/paper/2307.12639/) (2023)
- [Evaluating BERT-based Pre-training Language Models for Detecting Misinformation](https://mlsanity.com/paper/2203.07731/) (2022)
- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)
- [Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model](https://mlsanity.com/paper/2508.07209/) (2025)
- [AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach](https://mlsanity.com/paper/2208.08200/) (2022)
- [Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning](https://mlsanity.com/paper/2508.07205/) (2025)
- [Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users](https://mlsanity.com/paper/2605.12511/) (2026)
- [Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs](https://mlsanity.com/paper/2606.08605/) (2026)
- [Region-enhanced Deep Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2206.07665/) (2022)
- [A Meta Path-based Approach for Rumor Detection on Social Media](https://mlsanity.com/paper/2301.04341/) (2023)
- [Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election](https://mlsanity.com/paper/2503.01394/) (2025)
- [Weighted Graph Structure Learning with Attention Denoising for Node Classification](https://mlsanity.com/paper/2503.12157/) (2025)
- [LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection](https://mlsanity.com/paper/2508.18819/) (2025)
- [Verifying Rumors via Stance-Aware Structural Modeling](https://mlsanity.com/paper/2512.13559/) (2025)
- [Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection](https://mlsanity.com/paper/2604.23974/) (2026)

## Abstract (arXiv)

The development of social media has revolutionized the way people communicate, share information and make decisions, but it also provides an ideal platform for publishing and spreading rumors. Existing rumor detection methods focus on finding clues from text content, user profiles, and propagation patterns. However, the local semantic relation and global structural information in the message propagation graph have not been well utilized by previous works. In this paper, we present a novel global-local attention network (GLAN) for rumor detection, which jointly encodes the local semantic and global structural information. We first generate a better integrated representation for each source tweet by fusing the semantic information of related retweets with the attention mechanism. Then, we model the global relationships among all source tweets, retweets, and users as a heterogeneous graph to capture the rich structural information for rumor detection. We conduct experiments on three real-world datasets, and the results demonstrate that GLAN significantly outperforms the state-of-the-art models in both rumor detection and early detection scenarios.