在双曲空间中无监督检测社交事件，提升表达力与效率。
Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space
- 用语义锚点建模消息，结合双曲几何捕捉结构信息。
- 相较现有方法，聚类指标平均提升2%~25%，速度最快快37.41倍。
- 适合需要高效处理动态社交数据的场景，如实时舆情监控。
海量、复杂且动态的社交消息数据给社交事件检测（SED）带来挑战。尽管已有大量研究，仍存在表示能力不足（无效）和训练耗时长（低效）等问题。为此，本文提出无监督框架HyperSED（双曲社交事件检测）。该框架首先将社交消息建模为基于语义的消息锚点，再利用锚点图结构与双曲空间的表达能力，获取兼具结构与几何感知的锚点表示。最后，通过引入可微分结构信息构建锚点图的划分树，以反映检测到的事件。在公开数据集上的实验表明，HyperSED性能优异，相比当前最先进的无监督方法，在效率上提升至少12.10倍，最多达37.41倍；在增量式SED任务中，NMI、AMI和ARI指标分别平均提升2%、2%和25%。代码已开源。
原文摘要 · Abstract (English)
The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resulting in inadequately expressive message representations (ineffective) and prolonged learning durations (inefficient). In response to the challenges, this work introduces an unsupervised framework, HyperSED (Hyperbolic SED). Specifically, the proposed framework first models social messages into semantic-based message anchors, and then leverages the structure of the anchor graph and the expressiveness of the hyperbolic space to acquire structure- and geometry-aware anchor representations. Finally, HyperSED builds the partitioning tree of the anchor message graph by incorporating differentiable structural information as the reflection of the detected events. Extensive experiments on public datasets demonstrate HyperSED's competitive performance, along with a substantial improvement in efficiency compared to the current state-of-the-art unsupervised paradigm. Statistically, HyperSED boosts incremental SED by an average of 2%, 2%, and 25% in NMI, AMI, and ARI, respectively; enhancing efficiency by up to 37.41 times and at least 12.10 times, illustrating the advancement of the proposed framework. Our code is publicly available at https://github.com/XiaoyanWork/HyperSED.
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