no code implementations • Findings (ACL) 2022 • YUREN MAO, Zekai Wang, Weiwei Liu, Xuemin Lin, Pengtao Xie
Task weighting, which assigns weights on the including tasks during training, significantly matters the performance of Multi-task Learning (MTL); thus, recently, there has been an explosive interest in it.
no code implementations • ICML 2020 • YUREN MAO, Weiwei Liu, Xuemin Lin
Adversarial Multi-task Representation Learning (AMTRL) methods are able to boost the performance of Multi-task Representation Learning (MTRL) models.
1 code implementation • 4 Jan 2025 • Jianwei Wang, Kai Wang, Ying Zhang, Wenjie Zhang, Xiwei Xu, Xuemin Lin
Missing data imputation, which aims to impute the missing values in the raw datasets to achieve the completeness of datasets, is crucial for modern data-driven models like large language models (LLMs) and has attracted increasing interest over the past decades.
no code implementations • 11 Dec 2024 • Fan Li, Xiaoyang Wang, Dawei Cheng, Cong Chen, Ying Zhang, Xuemin Lin
iii) Current state-of-the-art dynamic graph generators are based on the temporal random walk, making the simulation process time-consuming.
no code implementations • 28 Oct 2024 • Ruifeng Li, Wei Liu, Xiangxin Zhou, Mingqian Li, Qiang Zhang, Hongyang Chen, Xuemin Lin
To overcome this challenge, we present a novel method named contextual representation anchor Network (CRA), where an anchor refers to a cluster center of the representations of molecules and serves as a bridge to transfer enriched contextual knowledge into molecular representations and enhance their expressiveness.
no code implementations • 9 Oct 2024 • Fan Li, Xiaoyang Wang, Dawei Cheng, Wenjie Zhang, Ying Zhang, Xuemin Lin
With growing demands for data privacy and model robustness, graph unlearning (GU), which erases the influence of specific data on trained GNN models, has gained significant attention.
1 code implementation • 21 May 2024 • Yihong Huang, Yuang Zhang, Liping Wang, Fan Zhang, Xuemin Lin
Most deep UOD models are trained exclusively on clean datasets to learn the distribution of the normal data, which requires huge manual efforts to clean the real-world data if possible.
1 code implementation • 18 Apr 2024 • Fan Li, Xiaoyang Wang, Dawei Cheng, Wenjie Zhang, Ying Zhang, Xuemin Lin
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels.
1 code implementation • 28 Jan 2024 • Hongyang Chen, Can Xu, Lingyu Zheng, Qiang Zhang, Xuemin Lin
Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks.
1 code implementation • IEEE Transactions on Knowledge and Data Engineering 2023 • PDF Han Chen, Hanchen Wang, Hongmei Chen, Ying Zhang, Wenjie Zhang, Xuemin Lin
The interactions between structured entities play important roles in a wide range of applications such as chemistry, material science, biology, and medical science.
1 code implementation • 26 May 2023 • Yihong Huang, Yuang Zhang, Liping Wang, Xuemin Lin
To our knowledge, our approach is the first to enable reliable identification of the optimal training iteration during training without requiring any labels.
1 code implementation • 24 Oct 2022 • Yihong Huang, Liping Wang, Fan Zhang, Xuemin Lin
In addition, we observe that existing algorithms have a performance drop with the mitigated data leakage issue.
no code implementations • 26 Aug 2022 • Qingqiang Sun, Xuemin Lin, Ying Zhang, Wenjie Zhang, Chaoqi Chen
Network alignment task, which aims to identify corresponding nodes in different networks, is of great significance for many subsequent applications.
no code implementations • 25 Jan 2022 • Hanchen Wang, Ying Zhang, Lu Qin, Wei Wang, Wenjie Zhang, Xuemin Lin
In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.
no code implementations • 10 Jan 2022 • Jiabao Jin, Peng Cheng, Lei Chen, Xuemin Lin, Wenjie Zhang
In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size.
no code implementations • ACL 2021 • YUREN MAO, Zekai Wang, Weiwei Liu, Xuemin Lin, Wenbin Hu
Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification.
no code implementations • 19 Jul 2021 • Peng Cheng, Jiabao Jin, Lei Chen, Xuemin Lin, Libin Zheng
In this paper, we consider an important dynamic car-hailing problem, namely \textit{maximum revenue vehicle dispatching} (MRVD), in which rider requests dynamically arrive and drivers need to serve as many riders as possible such that the entire revenue of the platform is maximized.
1 code implementation • 5 Jan 2021 • Weixin Zeng, Xiang Zhao, Jiuyang Tang, Xuemin Lin, Paul Groth
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs).
no code implementations • 21 Dec 2020 • Zhengmin Lai, You Peng, Shiyu Yang, Xuemin Lin, Wenjie Zhang
Motivated by this, in this paper, we propose the first FPGA-based algorithm PEFP to solve the problem of k-hop constrained s-t simple path enumeration efficiently.
Databases
1 code implementation • 12 May 2020 • Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xuemin Lin
We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.
no code implementations • 20 Apr 2020 • Lu Qin, Longbin Lai, Kongzhang Hao, Zhongxin Zhou, Yiwei Zhao, Yuxing Han, Xuemin Lin, Zhengping Qian, Jingren Zhou
Graph database has enjoyed a boom in the last decade, and graph queries accordingly gain a lot of attentions from both the academia and industry.
no code implementations • 19 Apr 2020 • Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xiangjian He, Yiguang Lin, Xuemin Lin
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.
1 code implementation • 18 Dec 2019 • Weixin Zeng, Xiang Zhao, Jiuyang Tang, Xuemin Lin
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration.
1 code implementation • 27 Jun 2019 • Longbin Lai, Zhu Qing, Zhengyi Yang, Xin Jin, Zhengmin Lai, Ran Wang, Kongzhang Hao, Xuemin Lin, Lu Qin, Wenjie Zhang, Ying Zhang, Zhengping Qian, Jingren Zhou
We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Databases
no code implementations • 20 Sep 2017 • Lijun Chang, Xing Feng, Xuemin Lin, Lu Qin, Wenjie Zhang
Graph edit distance (GED) is an important similarity measure adopted in a similarity-based analysis between two graphs, and computing GED is a primitive operator in graph database analysis.
Databases Data Structures and Algorithms
no code implementations • 18 Jan 2017 • Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang
Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo.
3 code implementations • 8 Oct 2016 • Wen Li, Ying Zhang, Yifang Sun, Wei Wang, Wenjie Zhang, Xuemin Lin
Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision.
Databases
no code implementations • 19 Aug 2016 • Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.