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 • 16 Oct 2024 • Kai Wang, Siqiang Luo

Our focus is on utilizing KGs as a unified topological structure to tackle diverse tasks, while addressing semantic isolation challenges in KG reasoning to effectively integrate diverse semantic and structural features.

no code implementations • 23 Sep 2024 • Weiping Yu, Siqiang Luo, Zihao Yu, Gao Cong

We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations.

1 code implementation • 14 Jun 2024 • Ningyi Liao, Haoyu Liu, Zulun Zhu, Siqiang Luo, Laks V. S. Lakshmanan

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 frequency domain, demonstrating promising capability in specific tasks.

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.

1 code implementation • 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.

2 code implementations • 18 Jan 2024 • Chenghua Gong, Yao Cheng, Jianxiang Yu, Can Xu, Caihua Shan, Siqiang Luo, Xiang Li

In this survey, we comprehensively review existing works on learning from graphs with heterophily.

1 code implementation • 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.

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.

2 code implementations • 17 May 2023 • Haoyu Liu, Ningyi Liao, Siqiang Luo

Existing attempts of heterophilous GNNs incorporate long-range or global aggregations to distinguish nodes in the graph.

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.

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.

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