# Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection

arXiv: https://arxiv.org/abs/2404.16076 · HTML: https://mlsanity.com/paper/2404.16076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 30 of 32)

- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [GraphMAE: Self-Supervised Masked Graph Autoencoders](https://mlsanity.com/paper/2205.10803/) (2022)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [GPT-GNN: Generative Pre-Training of Graph Neural Networks](https://mlsanity.com/paper/2006.15437/) (2020)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation](https://mlsanity.com/paper/2202.03104/) (2022)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive](https://mlsanity.com/paper/2105.07342/) (2021)
- [When Does Self-Supervision Help Graph Convolutional Networks?](https://mlsanity.com/paper/2006.09136/) (2020)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders](https://mlsanity.com/paper/2210.08344/) (2022)
- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [Automated Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2303.07797/) (2023)
- [Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2201.06885/) (2022)
- [Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks](https://mlsanity.com/paper/2110.04522/) (2021)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Self-supervised Training of Graph Convolutional Networks](https://mlsanity.com/paper/2006.02380/) (2020)
- [SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking](https://mlsanity.com/paper/2301.12458/) (2023)
- [Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks](https://mlsanity.com/paper/2011.07267/) (2020)
- [DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification](https://mlsanity.com/paper/2004.13455/) (2020)
- [Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media](https://mlsanity.com/paper/2202.03590/) (2022)
- [Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification](https://mlsanity.com/paper/2203.15381/) (2022)
- [Detecting False Rumors from Retweet Dynamics on Social Media](https://mlsanity.com/paper/2201.13103/) (2022)

## Built on by (most cited first, 1 of 1)

- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)

## Abstract (arXiv)

Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Recently, numerous rumor detection models which utilize textual information and the propagation structure of events have been proposed. However, these methods overlook the importance of semantic evolvement information of event in propagation process, which is often challenging to be truly learned in supervised training paradigms and traditional rumor detection methods. To address this issue, we propose a novel semantic evolvement enhanced Graph Autoencoder for Rumor Detection (GARD) model in this paper. The model learns semantic evolvement information of events by capturing local semantic changes and global semantic evolvement information through specific graph autoencoder and reconstruction strategies. By combining semantic evolvement information and propagation structure information, the model achieves a comprehensive understanding of event propagation and perform accurate and robust detection, while also detecting rumors earlier by capturing semantic evolvement information in the early stages. Moreover, in order to enhance the model's ability to learn the distinct patterns of rumors and non-rumors, we introduce a uniformity regularizer to further improve the model's performance. Experimental results on three public benchmark datasets confirm the superiority of our GARD method over the state-of-the-art approaches in both overall performance and early rumor detection.