# Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection

arXiv: https://arxiv.org/abs/2502.00351 · HTML: https://mlsanity.com/paper/2502.00351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Hyperbolic Neural Networks](https://mlsanity.com/paper/1805.09112/) (2018)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [Hyperbolic Graph Neural Networks](https://mlsanity.com/paper/1910.12892/) (2019)
- [Constant Curvature Graph Convolutional Networks](https://mlsanity.com/paper/1911.05076/) (2019)
- [Fully Hyperbolic Neural Networks](https://mlsanity.com/paper/2105.14686/) (2021)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks](https://mlsanity.com/paper/1906.04580/) (2019)
- [Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks](https://mlsanity.com/paper/2010.14123/) (2020)
- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection](https://mlsanity.com/paper/2312.11891/) (2023)
- [Mix Dimension in Poincar'e Geometry for 3D Skeleton-based Action Recognition](https://mlsanity.com/paper/2007.15678/) (2020)
- [Event Nugget Detection with Forward-Backward Recurrent Neural Networks](https://mlsanity.com/paper/1802.05672/) (2018)
- [GTN-ED: Event Detection Using Graph Transformer Networks](https://mlsanity.com/paper/2104.15104/) (2021)
- [Heterogeneous Social Event Detection via Hyperbolic Graph Representations](https://mlsanity.com/paper/2302.10362/) (2023)
- [Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering](https://mlsanity.com/paper/2312.08374/) (2023)

## Abstract (arXiv)

Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provides valuable insights for businesses to understand consumer preferences and supports public services in handling emergencies and disaster management. Due to the hierarchical structure of event detection data, traditional approaches in Euclidean space often fall short in capturing the complexity of such relationships. While existing methods in both Euclidean and hyperbolic spaces have shown promising results, they tend to overlook multi-order relationships between events. To address these limitations, this paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED. Experimental results demonstrate significant improvements under both supervised and unsupervised settings. To further validate the effectiveness and robustness of the proposed framework, we conducted extensive evaluations across multiple datasets, confirming its superiority in tackling common challenges in social event detection.