HyperIDP: Customizing Temporal Hypergraph Neural Networks for Multi-Scale Information Diffusion Prediction (2025.coling-main)
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| Challenge: | Existing studies on information diffusion prediction have focused on both macroscopic and microscopic scales. |
| Approach: | They propose a hypergraph-based model that manages both macroscopic and microscopic diffusion predictions. |
| Outcome: | The proposed model outperforms baseline models on both macroscopic and microscopic tasks. |
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Hyperbolic Graph Neural Network for Temporal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing knowledge graph models are inefficient at capturing complex temporal dynamics and hierarchical relations within TKGs. |
| Approach: | They propose to use hyperbolic geometry to effectively model temporal knowledge graphs . they use the hyperbolical gated Graph Neural Network and the hyperbipolar convolutional neural network . |
| Outcome: | The proposed model achieves state-of-the-art performance on four benchmark datasets . it is compared with previous models and is expected to be useful in real-world applications . |
Holistic Prediction on a Time-Evolving Attributed Graph (2023.acl-long)
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| Challenge: | Existing methods for predicting future links, nodes, and attributes of time-evolving attributed graphs are not accurate. |
| Approach: | They propose a framework that predicts node attributes and topology changes such as appearance and disappearance of links and the emergence and loss of nodes. |
| Outcome: | The proposed framework improves on existing methods that assume that each link, node, and attribute prediction is independent and fails to predict new nodes that were not observed in the past. |
HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)
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| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
| Approach: | They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets. |
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP (D19-1)
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| Challenge: | Current approaches to social media modelling ignore the fact that an individual may be part of several communities which are not equally relevant in all communicative situations. |
| Approach: | They propose a model that captures the sociological phenomenon of homophily and combines it with linguistic information to make a prediction. |
| Outcome: | The proposed model significantly outperforms existing models on three different tasks and is compared with other models. |
Knowledge Association with Hyperbolic Knowledge Graph Embeddings (2020.emnlp-main)
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| Challenge: | Existing methods for knowledge graphs (KGs) depend on high embedding dimensions and hierarchical structures to achieve expressiveness. |
| Approach: | They propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a high-dimensional transformation. |
| Outcome: | Experiments on entity alignment and type inference show the proposed method is effective and efficient. |
From Graphs to Hypergraphs: Enhancing Aspect-Term Sentiment Analysis via Multi-Level Relational Modeling (2026.acl-srw)
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| Challenge: | Existing graph-based approaches to predict sentiment polarity for specific aspect terms rely on predefined pairwise structures to improve expressive capacity. |
| Approach: | They propose a dynamic hypergraph framework that can be used to generate a single instance-specific hypergraph from contextual token representations. |
| Outcome: | The proposed framework improves on Lap14, Rest14, and MAMS . it uses a single instance-specific hypergraph constructed directly from contextual token representations . |
HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection (2025.findings-emnlp)
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| Challenge: | Pre-trained language models (PLMs) are increasingly being applied to code-related tasks. |
| Approach: | They propose a hypergraph-based adapter to capture high-order data correlations in code tokens . they improve hypergraph neural networks and combine it with adapter tuning to propose adapter . |
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Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing models for temporal knowledge graphs model the temporal KGs in discrete state spaces, whereas static models model the KG in discretized state spaces. |
| Approach: | They propose a continuum model that extends the idea of neural ordinary differential equations to multi-relational graph convolutional networks and encodes both temporal and structural information into continuous-time dynamic embeddings. |
| Outcome: | The proposed model outperforms existing models on five benchmark datasets showing it can predict future links on temporal knowledge graphs. |
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs (2024.findings-emnlp)
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| Challenge: | Existing methods to learn informative data representations on text-attributed hypergraphs struggle to capture full extent of hypergraph structural information and rich linguistic attributes inherent in the nodes attributes. |
| Approach: | They propose to augment a pre-trained BERT model with specialized hypergraph-aware layers for the task of node classification. |
| Outcome: | The proposed model outperforms existing methods on five challenging text-attributed hypergraph node classification benchmarks. |
HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)
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| Challenge: | Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval. |
| Approach: | They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges. |
| Outcome: | Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations. |