Papers with heterogeneous graph neural network
AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering (2026.acl-long)
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Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang, Tianyi Ma, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye
| Challenge: | Existing approaches to agent routing emphasize cost efficiency while overlooking the fine-grained contextual and relational structure inherent in QA tasks. |
| Approach: | They propose a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals. |
| Outcome: | The proposed framework outperforms single-agent and ensemble baselines while generalizing across benchmarks and LLM backbones. |
Cross-document Misinformation Detection based on Event Graph Reasoning (2022.naacl-main)
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| Challenge: | Existing methods for misinformation detection are limited to judging each document in isolation. |
| Approach: | They propose a task of cross-document misinformation detection that detects fake news from a cluster of topically related news documents. |
| Outcome: | The proposed method outperforms existing methods by up to 7 F1 points on this task. |
Social-aware Sparse Attention Network for Session-based Social Recommendation (2022.findings-emnlp)
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| Challenge: | Existing methods for predicting the next item for an anonymous session do not capture user preferences and noisy irrelevant interactions. |
| Approach: | They propose to use social networks and historical sessions to provide personalized recommendations for the current session. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets. |
HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Existing models for extractive document summarization are based on sequence-to-sequence (Seq2Sequency) but long-form document summaries using graph-based methods are still an open research issue. |
| Approach: | They propose a heterogeneous graph neural network model to improve the performance of extractive document summarization using graph-based methods. |
| Outcome: | The proposed model can achieve state-of-the-art performance without pre-trained language models. |