TIGER: Training Inductive Graph Neural Network for Large-scale Knowledge Graph Reasoning
Knowledge Graph (KG) Reasoning plays a vital role in various applications by predicting
missing facts from existing knowledge. Inductive KG reasoning approaches based on Graph …
missing facts from existing knowledge. Inductive KG reasoning approaches based on Graph …
Acceleration algorithms in gnns: A survey
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based
tasks. However, their inefficiency in training and inference presents challenges for scaling …
tasks. However, their inefficiency in training and inference presents challenges for scaling …
BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRank
In bipartite graph analysis, similarity measures play a pivotal role in various applications.
Among existing metrics, the Bidirectional Hidden Personalized PageRank (BHPP) stands …
Among existing metrics, the Bidirectional Hidden Personalized PageRank (BHPP) stands …
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs
Graph representation learning is an emerging task for effectively embedding graph-
structured data with learned features. Among them, Subgraph-based GRL (SGRL) methods …
structured data with learned features. Among them, Subgraph-based GRL (SGRL) methods …
Benchmarking Spectral Graph Neural Networks: A Comprehensive Study on Effectiveness and Efficiency
With the recent advancements in graph neural networks (GNNs), spectral GNNs have
received increasing popularity by virtue of their specialty in capturing graph signals in the …
received increasing popularity by virtue of their specialty in capturing graph signals in the …
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network
Graph Neural Networks (GNNs) have shown promising performance in various graph
learning tasks, but at the cost of resource-intensive computations. The primary overhead of …
learning tasks, but at the cost of resource-intensive computations. The primary overhead of …