# Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

arXiv: https://arxiv.org/abs/2312.11891 · HTML: https://mlsanity.com/paper/2312.11891/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 52 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Structural Entropy Guided Graph Hierarchical Pooling](https://mlsanity.com/paper/2206.13510/) (2022)
- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks](https://mlsanity.com/paper/2104.00853/) (2021)
- [Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks](https://mlsanity.com/paper/1906.04580/) (2019)
- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (2019)
- [Investigating label suggestions for opinion mining in German Covid-19 social media](https://mlsanity.com/paper/2105.12980/) (2021)
- [From Known to Unknown: Quality-aware Self-improving Graph Neural Network for Open Set Social Event Detection](https://mlsanity.com/paper/2208.06973/) (2022)
- [Unsupervised Detection of Sub-events in Large Scale Disasters](https://mlsanity.com/paper/1912.13332/) (2019)
- [Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection](https://mlsanity.com/paper/2310.19247/) (2023)
- [Transferring Knowledge Distillation for Multilingual Social Event Detection](https://mlsanity.com/paper/2108.03084/) (2021)
- [Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding](https://mlsanity.com/paper/2304.04099/) (2023)
- [Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management](https://mlsanity.com/paper/2010.01683/) (2020)

## Built on by (most cited first, 15 of 15)

- [Relational Prompt-based Pre-trained Language Models for Social Event Detection](https://mlsanity.com/paper/2404.08263/) (2024)
- [Effective Exploration Based on the Structural Information Principles](https://mlsanity.com/paper/2410.06621/) (2024)
- [Unsupervised Graph Clustering with Deep Structural Entropy](https://mlsanity.com/paper/2505.14040/) (2025)
- [KAT-Coder-V2.5 Technical Report](https://mlsanity.com/paper/2607.05471/) (2026)
- [Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning](https://mlsanity.com/paper/2512.08763/) (2025)
- [UMGAD: Unsupervised Multiplex Graph Anomaly Detection](https://mlsanity.com/paper/2411.12556/) (2024)
- [DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism](https://mlsanity.com/paper/2409.00614/) (2024)
- [Explicit and Implicit Data Augmentation for Social Event Detection](https://mlsanity.com/paper/2509.04202/) (2025)
- [Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Training](https://mlsanity.com/paper/2510.22094/) (2025)
- [Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection](https://mlsanity.com/paper/2407.18274/) (2024)
- [Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space](https://mlsanity.com/paper/2412.10712/) (2024)
- [An Incremental Clustering Baseline for Event Detection on Twitter](https://mlsanity.com/paper/2412.15257/) (2024)
- [Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection](https://mlsanity.com/paper/2502.00351/) (2025)
- [Dialogues Aspect-based Sentiment Quadruple Extraction via Structural Entropy Minimization Partitioning](https://mlsanity.com/paper/2508.05023/) (2025)
- [Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression](https://mlsanity.com/paper/2601.08187/) (2026)

## Abstract (arXiv)

As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs, is unsupervised, and does not require the number of events given a priori. Specifically, we incrementally explore the graph neighborhoods using 1-dimensional (1D) SE minimization to supplement the existing message graph with edges between semantically related messages. We then detect events from the message graph by hierarchically minimizing 2-dimensional (2D) SE. Our proposed 1D and 2D SE minimization algorithms are customized for social event detection and effectively tackle the efficiency problem of the existing SE minimization algorithms. Extensive experiments show that HISEvent consistently outperforms GNN-based methods and achieves the new SOTA for social event detection under both closed- and open-set settings while being efficient and robust.