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

• URL: http://arxiv.org/abs/2502.00351v2
• Date: Mon, 10 Feb 2025 10:08:51 GMT
• Title: Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection
• Authors: Yao Liu, Zhilan Liu, Tien Ping Tan, Yuxin Li,
• Abstract summary: Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. This paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED.
• Score: 4.183900122103969
• License:
• Abstract: 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.

Related papers

• Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space [54.936897625837474]
  This work introduces an unsupervised framework, HyperSED (Hyperbolic SED). Specifically, the framework first models social messages into semantic-based message anchors, and then leverages the structure of the anchor graph. Experiments on public datasets demonstrate HyperSED's competitive performance, along with a substantial improvement in efficiency.
  arXiv  Detail & Related papers  (2024-12-14T06:55:27Z)
• DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism [55.45581907514175]
  This paper proposes a personalized federated learning framework with a dual aggregation mechanism for social event detection, namely DAMe. We introduce a global aggregation strategy to provide clients with maximum external knowledge of their preferences. In addition, we incorporate a global-local event-centric constraint to prevent local overfitting and client-drift''
  arXiv  Detail & Related papers  (2024-09-01T04:56:41Z)
• Hypergraph-based Multi-View Action Recognition using Event Cameras [20.965606424362726]
  We introduce HyperMV, a multi-view event-based action recognition framework. We present the largest multi-view event-based action dataset $textTHUtextMV-EACTtext-50$, comprising 50 actions from 6 viewpoints. Experimental results show that HyperMV significantly outperforms baselines in both cross-subject and cross-view scenarios.
  arXiv  Detail & Related papers  (2024-03-28T11:17:00Z)
• EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction [4.905346662577355]
  We propose a novel framework, namely EnrichEvent, that leverages the linguistic and contextual representations of streaming social data. Our proposed framework produces cluster chains for each event to show the evolving variation of the event through time.
  arXiv  Detail & Related papers  (2023-07-29T21:37:55Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• Abnormal Event Detection via Hypergraph Contrastive Learning [54.80429341415227]
  Abnormal event detection plays an important role in many real applications. In this paper, we study the unsupervised abnormal event detection problem in Attributed Heterogeneous Information Network. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns.
  arXiv  Detail & Related papers  (2023-04-02T08:23:20Z)
• Learning Constraints and Descriptive Segmentation for Subevent Detection [74.48201657623218]
  We propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction. We adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.
  arXiv  Detail & Related papers  (2021-09-13T20:50:37Z)
• JRDB-Act: A Large-scale Multi-modal Dataset for Spatio-temporal Action, Social Group and Activity Detection [54.696819174421584]
  We introduce JRDB-Act, a multi-modal dataset that reflects a real distribution of human daily life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels. JRDB-Act comes with social group identification annotations conducive to the task of grouping individuals based on their interactions in the scene.
  arXiv  Detail & Related papers  (2021-06-16T14:43:46Z)
• Event Prediction in the Big Data Era: A Systematic Survey [7.3810864598379755]
  Event prediction is becoming a viable option in the big data era. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction.
  arXiv  Detail & Related papers  (2020-07-19T23:24:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.