1 code implementation • ACL 2022 • Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, ZongYu Wang, Rui Xie, Wei Wu, Man Lan
Entity alignment (EA) aims to discover the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs. For a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding process. In this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI). Specifically, we derive two sets of isomorphism equations: (1) Adjacency tensor isomorphism equations and (2) Gramian tensor isomorphism equations. By combining these equations, DATTI could effectively utilize the adjacency and inner correlation isomorphisms of KGs to enhance the decoding process of EA. Extensive experiments on public datasets indicate that our decoding algorithm can deliver significant performance improvements even on the most advanced EA methods, while the extra required time is less than 3 seconds.
1 code implementation • 8 Dec 2023 • Haojie Pan, Zepeng Zhai, Hao Yuan, Yaojia LV, Ruiji Fu, Ming Liu, Zhongyuan Wang, Bing Qin
Driven by curiosity, humans have continually sought to explore and understand the world around them, leading to the invention of various tools to satiate this inquisitiveness.
no code implementations • 22 Nov 2023 • Xin Ai, Qiange Wang, Chunyu Cao, Yanfeng Zhang, Chaoyi Chen, Hao Yuan, Yu Gu, Ge Yu
After extensive experiments and analysis, we find that existing task orchestrating methods fail to fully utilize the heterogeneous resources, limited by inefficient CPU processing or GPU resource contention.
no code implementations • 22 Nov 2023 • Hao Yuan, Yajiong Liu, Yanfeng Zhang, Xin Ai, Qiange Wang, Chaoyi Chen, Yu Gu, Ge Yu
Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training.
1 code implementation • COLING 2022 • Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan
However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations.
2 code implementations • 26 Jun 2022 • Shurui Gui, Hao Yuan, Jie Wang, Qicheng Lao, Kang Li, Shuiwang Ji
We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms.
1 code implementation • 14 Oct 2021 • Shen Liu, Meirong Ma, Hao Yuan, Jianchao Zhu, Yuanbin Wu, Man Lan
Pun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different meanings of the punning word.
1 code implementation • NeurIPS Workshop AI4Scien 2021 • Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji
We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.
no code implementations • 11 May 2021 • Lixin Xu, Lin Li, Kunniang Liu, Jiarui Zhang, Yuanning Chang, Yunpeng Fang, Hao Yuan, Zhiyuan Yang, Jingyuan Chen, Yiyao Wang, Yajun Fang
ransportation systems have revolutionized the form of society.
1 code implementation • 23 Mar 2021 • Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
1 code implementation • 9 Feb 2021 • Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji
To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs.
no code implementations • 22 Jan 2021 • Hao Yuan, Xiaoping Xie
Semi-discrete and fully discrete mixed finite element methods are considered for Maxwell-model-based problems of wave propagation in linear viscoelastic solid.
Numerical Analysis Numerical Analysis
no code implementations • 6 Jan 2021 • Hao Yuan, Shuiwang Ji
Several graph neural network approaches are proposed for node feature learning and they generally follow a neighboring information aggregation scheme to learn node features.
no code implementations • 31 Dec 2020 • Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji
To facilitate evaluations, we generate a set of benchmark graph datasets specifically for GNN explainability.
1 code implementation • 17 Nov 2020 • Xinyi Xu, Zhengyang Wang, Cheng Deng, Hao Yuan, Shuiwang Ji
Grouping has been commonly used in deep metric learning for computing diverse features.
no code implementations • NAACL 2021 • Mohammad Kachuee, Hao Yuan, Young-Bum Kim, Sungjin Lee
Moreover, a powerful satisfaction model can be used as an objective function that a conversational agent continuously optimizes for.
no code implementations • 18 Jul 2020 • Yi Liu, Hao Yuan, Lei Cai, Shuiwang Ji
However, these methods do not incorporate the important sequential information from amino acid chains and the high-order pairwise interactions.
Protein Interface Prediction Vocal Bursts Intensity Prediction
no code implementations • 3 Jun 2020 • Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji
Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.
no code implementations • 29 May 2020 • Dookun Park, Hao Yuan, Dongmin Kim, Yinglei Zhang, Matsoukas Spyros, Young-Bum Kim, Ruhi Sarikaya, Edward Guo, Yuan Ling, Kevin Quinn, Pham Hung, Benjamin Yao, Sungjin Lee
An widely used approach to tackle this is to collect human annotation data and use them for evaluation or modeling.
1 code implementation • ICLR 2020 • Hao Yuan, Shuiwang Ji
Learning high-level representations for graphs is of great importance for graph analysis tasks.
no code implementations • 8 Jul 2019 • Fan Yang, Shiva K. Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, Eric D. Ragan, Shuiwang Ji, Xia Hu
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.
no code implementations • 1 Jul 2019 • Yi Liu, Hao Yuan, Zhengyang Wang, Shuiwang Ji
It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results.
4 code implementations • ICLR 2018 • Hongyang Gao, Hao Yuan, Zhengyang Wang, Shuiwang Ji
When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations.
1 code implementation • 18 May 2017 • Zhengyang Wang, Hao Yuan, Shuiwang Ji
In this work, we propose spatial VAEs that use feature maps of larger size as latent variables to explicitly capture spatial information.