THINK: Temporal Hypergraph Hyperbolic Network

Agarwal, Shivam; Sawhney, Ravinder Singh; Thakkar, Megh; Nakov, Preslav; Derr, Tyler

doi:10.1109/icdm54844.2022.00096

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…They explored the evolution of agents' opinions and the role of internet bots in spreading fake news and they found that even with a relatively small percentage of agents believing fake news, significant misinformation and polarization can occur, emphasizing the importance of network effects. (Agarwal et al 2022) introduced THINK, a novel framework for network-based time series forecasting that leveraged hypergraph learning and hyperbolic properties. By capturing higher-order relations and scale-free characteristics, THINK outperformed state-of-the-art methods across various tasks.…”

Section: Related Work ( ‡)mentioning

confidence: 99%

Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation

Ghosh,

Mitra,

Nakov

2024

ICWSM

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As social networks become the primary sources of information, the rise of misinformation poses a significant threat to the information ecosystem. Here, we address this challenge by proposing a dynamic system for real-time evaluation and assignment of misinformation scores to tweets, which can support the ongoing efforts to counteract the impact of misinformation public health, public opinion, and society. We use a unique combination of Temporal Graph Network (TGN) and Recurrent Neural Networks (RNNs) to capture both structural and temporal characteristics of misinformation propagation. We further use active learning to refine the understanding of misinformation, and a dual model system to ensure the accurate grading of tweets. Our system also incorporates a temporal embargo strategy based on belief scores, allowing for comprehensive assessment of information over time. We further outline a retraining strategy to keep the model current and robust in the dynamic misinformation landscape. The evaluation results across five social media misinformation datasets show promising accuracy in identifying false information and reducing propagation by a significant margin.

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Section: Related Work ( ‡)mentioning

confidence: 99%

Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation

Ghosh,

Mitra,

Nakov

2024

ICWSM

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Hypergraph Computation

Gao,

Ji,

Han

et al. 2024

Engineering

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Dynamic Graph Representation Learning With Neural Networks: A Survey

Yang,

Chatelain,

Adam

2024

IEEE Access

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In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynamic graphs efficiently handle applications such as social network prediction, recommender systems, traffic forecasting, or electroencephalography analysis, which cannot be addressed using standard numerical representations. As a direct consequence, dynamic graph learning has emerged as a new machine learning problem, combining challenges from both sequential/temporal data processing and static graph learning. In this research area, the Dynamic Graph Neural Network (DGNN) has become the state-of-the-art approach and a plethora of models have been proposed in the very recent years. This paper aims to provide a review of the problems and models related to dynamic graph learning. The various dynamic graph supervised learning settings are analyzed and discussed. We identify the similarities and differences between existing models concerning the way time information is modeled. Finally, we provide guidelines for DGNN design and optimization, and review public datasets for evaluating model performance on various tasks, along with the corresponding publications.

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THINK: Temporal Hypergraph Hyperbolic Network

Cited by 5 publications

References 26 publications

Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation

Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation

Hypergraph Computation

Dynamic Graph Representation Learning With Neural Networks: A Survey

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