Papers by Xiangping Zheng
Eureka: Neural Insight Learning for Knowledge Graph Reasoning (2022.coling-1)
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| Challenge: | Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples . |
| Approach: | They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger . |
| Outcome: | The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples . |
Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training (2024.lrec-main)
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| Challenge: | Graph neural networks excel at anomaly detection, but exhibit vulnerability to attacks . novel mechanism for graph adversarial training designed to bolster anomaly detectors . |
| Approach: | They propose a mechanism for graph adversarial training to bolster anomaly detection systems against potential poisoning attacks. |
| Outcome: | The proposed method bolsters GNN-based anomaly detection systems against poisoning attacks. |
Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)
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| Challenge: | Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios. |
| Approach: | They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity. |
| Outcome: | The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods. |