1 code implementation • 26 Apr 2024 • Renqiang Luo, Huafei Huang, Shuo Yu, Xiuzhen Zhang, Feng Xia
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups.
no code implementations • 6 Feb 2024 • Huiling Tu, Shuo Yu, Vidya Saikrishna, Feng Xia, Karin Verspoor
Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains.
no code implementations • 25 Jan 2024 • Lei Liu, Shuo Yu, Runze Wang, Zhenxun Ma, Yanming Shen
We tackle the data mismatch by proposing: 1) STG-Tokenizer: This spatial-temporal graph tokenizer transforms intricate graph data into concise tokens capturing both spatial and temporal relationships; 2) STG-Adapter: This minimalistic adapter, consisting of linear encoding and decoding layers, bridges the gap between tokenized data and LLM comprehension.
no code implementations • 12 Jun 2023 • Feng Xia, Xin Chen, Shuo Yu, Mingliang Hou, Mujie Liu, Linlin You
To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships.
1 code implementation • 25 May 2023 • Shuo Yu, Hongyan Xue, Xiang Ao, Feiyang Pan, Jia He, Dandan Tu, Qing He
In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together.
no code implementations • 2 Feb 2023 • Shuo Yu, Ciyuan Peng, Yingbo Wang, Ahsan Shehzad, Feng Xia, Edwin R. Hancock
However, facilitating quantum theory to enhance graph learning is in its infancy.
no code implementations • 6 Apr 2022 • Feng Xia, Shuo Yu, Chengfei Liu, Ivan Lee
In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs.
1 code implementation • 17 Mar 2022 • Shuo Yu, Huafei Huang, Minh N. Dao, Feng Xia
To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks.
no code implementations • 3 May 2021 • Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, Huan Liu
In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.
no code implementations • 7 Mar 2021 • Ke Sun, Jiaying Liu, Shuo Yu, Bo Xu, Feng Xia
Features representation leverages the great power in network analysis tasks.
no code implementations • 27 Aug 2020 • Shuo Yu, Feng Xia, Jin Xu, Zhikui Chen, Ivan Lee
In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined.
no code implementations • 9 Aug 2020 • Jin Xu, Shuo Yu, Ke Sun, Jing Ren, Ivan Lee, Shirui Pan, Feng Xia
Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.
no code implementations • 9 Aug 2020 • Hayat Dino Bedru, Shuo Yu, Xinru Xiao, Da Zhang, Liangtian Wan, He guo, Feng Xia
This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network.