38 citations · 163 across the 25 of their papers we have counts for
7 papers · 1 filter
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
Yao Cheng, Caihua Shan, Yifei Shen +3
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels…
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
Dingheng Mo, Fanchao Chen, Siqiang Luo +1
LSM-trees are widely adopted as the storage backend of key-value stores. However, optimizing the system performance under dynamic workloads has not been sufficiently studied or eva…
River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning
Kai Wang, Siqiang Luo, Dan Lin
We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the-art K…
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation
Haoyu Liu, Ningyi Liao, Siqiang Luo
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their…
Massively Parallel Single-Source SimRanks in Rounds
Siqiang Luo, Zulun Zhu
SimRank is one of the most fundamental measures that evaluate the structural similarity between two nodes in a graph and has been applied in a plethora of data management tasks. Th…
Less is More: Towards Lightweight Cost Estimator for Database Systems
Weiping Yu, Siqiang Luo
We present FasCo, a simple yet effective learning-based estimator for the cost of executing a database query plan. FasCo uses significantly shorter training time and a lower infere…
Distributed Graph Embedding with Information-Oriented Random Walks
Peng Fang, Arijit Khan, Siqiang Luo +5
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the imp…