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Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, …
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Shrinking Embeddings for Hyper-Relational Knowledge Graphs
Link prediction on knowledge graphs (KGs) has been extensively studied on binary relational KGs, wherein each fact is represented by a …
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Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with …
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Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, session-based …
Robust Graph Representation Learning for Local Corruption Recovery
The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a …
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MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of …
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TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems
The scalability problem has been one of the most significant barriers limiting blockchain adoption. Blockchain sharding is a promising …
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Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several …
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Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs
Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks …
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Neighbor Contrastive Learning on Learnable Graph Augmentation
Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. …
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