7 papers
Dynamic Heterogeneous Graph Representation Learning: A Survey
Huan Liu, Pengfei Jiao, Jie Yin +2
Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest as evolving heterogeneous entit…
Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
Shuhang Lin, Chuhao Zhou, Xiao Lin +5
Knowledge Graph Question Answering (KGQA) offers grounded, interpretable reasoning, but existing methods often fail to provide reliable coverage guarantees over retrieved answers.…
MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
Han Wu, Jie Yin
Few-shot knowledge graph relational learning seeks to perform reasoning over relations given only a limited number of training examples. While existing approaches largely adopt a m…
Unbiased Online Curvature Approximation for Regularized Graph Continual Learning
Jie Yin, Ke Sun, Han Wu
Graph continual learning (GCL) aims to learn from a continuous sequence of graph-based tasks. Regularization methods are vital for preventing catastrophic forgetting in GCL, partic…
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network
Hong Jin, Kaicheng Zhou, Jie Yin +2
Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic informatio…
Meta-Semantics Augmented Few-Shot Relational Learning
Han Wu, Jie Yin
Few-shot relational learning on knowledge graph (KGs) aims to perform reasoning over relations with only a few training examples. While current methods have focused primarily on le…
Results of the Big ANN: NeurIPS'23 competition
Harsha Vardhan Simhadri, Martin Aumüller, Amir Ingber +20
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate…