no code implementations • ICML 2020 • Siqiang Luo
PageRank is a widely used approach for measuring the importance of a node in a graph.
no code implementations • 20 Mar 2024 • Ningyi Liao, Zihao Yu, Siqiang Luo
Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations.
no code implementations • 19 Feb 2024 • Kai Wang, Yuwei Xu, Zhiyong Wu, Siqiang Luo
Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications.
1 code implementation • 18 Jan 2024 • Chenghua Gong, Yao Cheng, Xiang Li, Caihua Shan, Siqiang Luo
Graphs are structured data that models complex relations between real-world entities.
no code implementations • 25 Oct 2023 • Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li
In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes.
no code implementations • 14 Aug 2023 • Dingheng Mo, Fanchao Chen, Siqiang Luo, Caihua Shan
LSM-trees are widely adopted as the storage backend of key-value stores.
1 code implementation • 17 May 2023 • 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 local and uniform aggregation.
no code implementations • 17 May 2023 • Kai Wang, Siqiang Luo, Dan Lin
We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning.
1 code implementation • 28 Mar 2023 • Peng Fang, Arijit Khan, Siqiang Luo, Fang Wang, Dan Feng, Zhenli Li, Wei Yin, Yuchao Cao
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks.
1 code implementation • 19 Jul 2022 • Ningyi Liao, Dingheng Mo, Siqiang Luo, Xiang Li, Pengcheng Yin
Recent advances in data processing have stimulated the demand for learning graphs of very large scales.
2 code implementations • 30 Jun 2022 • Eric R. Knorr, Baptiste Lemaire, Andrew Lim, Siqiang Luo, Huanchen Zhang, Stratos Idreos, Michael Mitzenmacher
We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement.
1 code implementation • 15 May 2022 • Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian
Further, for other homophilous nodes excluded in the neighborhood, they are ignored for information aggregation.
Ranked #2 on Node Classification on pokec
1 code implementation • 5 May 2022 • Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, Siqiang Luo
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information.
1 code implementation • 3 Apr 2022 • Kai Siong Yow, Ningyi Liao, Siqiang Luo, Reynold Cheng, Chenhao Ma, Xiaolin Han
Many algorithms are proposed in studying subgraph problems, where one common approach is by extracting the patterns and structures of a given graph.