1 code implementation • 6 Feb 2024 • Lingxiao Zhao, Xueying Ding, Lijun Yu, Leman Akoglu
Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs.
no code implementations • 6 Feb 2024 • Lingxiao Zhao, Xueying Ding, Leman Akoglu
Current graph diffusion models generate graphs in a one-shot fashion, but they require extra features and thousands of denoising steps to achieve optimal performance.
1 code implementation • 21 Jul 2023 • Zhiyuan Zhao, Xueying Ding, B. Aditya Prakash
Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning framework for approximating numerical solutions to partial differential equations (PDEs).
no code implementations • 20 Jul 2023 • Xueying Ding, Yue Zhao, Leman Akoglu
Outlier detection (OD) finds many applications with a rich literature of numerous techniques.
no code implementations • 6 Apr 2023 • Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few.
2 code implementations • 21 Jun 2022 • Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
1 code implementation • 15 Jun 2022 • Xueying Ding, Lingxiao Zhao, Leman Akoglu
Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains.
1 code implementation • 26 Apr 2022 • Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu
PyGOD is an open-source Python library for detecting outliers in graph data.
no code implementations • 25 Nov 2020 • Ye Yuan, Xueying Ding, Ziv Bar-Joseph
Causal inference from observation data is a core problem in many scientific fields.
2 code implementations • 8 Feb 2020 • Yue Zhao, Xueying Ding, Jianing Yang, Haoping Bai
In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models.
1 code implementation • 21 Sep 2019 • Yue Zhao, Xuejian Wang, Cheng Cheng, Xueying Ding
Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications.