no code implementations • 18 Apr 2024 • Qian Li, Cheng Ji, Shu Guo, Yong Zhao, Qianren Mao, Shangguang Wang, Yuntao Wei, JianXin Li
Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task.
no code implementations • 7 Mar 2024 • Qian Li, Shu Guo, Yinjia Chen, Cheng Ji, Jiawei Sheng, JianXin Li
Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution.
1 code implementation • 9 Feb 2024 • Haonan Yuan, Qingyun Sun, Xingcheng Fu, Cheng Ji, JianXin Li
Leveraged by the Information Bottleneck (IB) principle, we first propose the expected optimal representations should satisfy the Minimal-Sufficient-Consensual (MSC) Condition.
no code implementations • 4 Jan 2024 • Jing Wu, Suiyao Chen, Qi Zhao, Renat Sergazinov, Chen Li, ShengJie Liu, Chongchao Zhao, Tianpei Xie, Hanqing Guo, Cheng Ji, Daniel Cociorva, Hakan Brunzel
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies.
1 code implementation • NeurIPS 2023 • Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, JianXin Li
To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
2 code implementations • NeurIPS 2023 • Beining Yang, Kai Wang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Hao Tang, Yang You, JianXin Li
We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98. 6% test accuracy of training on the original graph dataset with 1, 000 times saving on the scale of the graph.
1 code implementation • 10 Oct 2023 • Qian Li, Cheng Ji, Shu Guo, Zhaoji Liang, Lihong Wang, JianXin Li
To address these challenges, we propose a novel MMEA transformer, called MoAlign, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task.
no code implementations • 19 Jun 2023 • Qian Li, Shu Guo, Cheng Ji, Xutan Peng, Shiyao Cui, JianXin Li
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues.
no code implementations • 4 Apr 2023 • Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, JianXin Li
In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge.
no code implementations • 18 Feb 2023 • Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
1 code implementation • 28 Jan 2023 • Cheng Ji, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Qingyun Sun, Phillip S. Yu
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning.
no code implementations • 15 Nov 2022 • Qian Li, JianXin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie
To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.
1 code implementation • 17 Aug 2022 • Qingyun Sun, JianXin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.
1 code implementation • 3 Mar 2022 • JianXin Li, Xingcheng Fu, Qingyun Sun, Cheng Ji, Jiajun Tan, Jia Wu, Hao Peng
In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.
1 code implementation • 16 Dec 2021 • Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
no code implementations • 16 Nov 2021 • Jing Cao, Zirui Lian, Weihong Liu, Zongwei Zhu, Cheng Ji
Federated learning (FL) supports training models on geographically distributed devices.
1 code implementation • 15 Oct 2021 • Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.
1 code implementation • 18 Nov 2019 • JianXin Li, Cheng Ji, Hao Peng, Yu He, Yangqiu Song, Xinmiao Zhang, Fanzhang Peng
However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved.
no code implementations • 7 Sep 2019 • Yu He, Yangqiu Song, Jian-Xin Li, Cheng Ji, Jian Peng, Hao Peng
Heterogeneous information network (HIN) embedding has gained increasing interests recently.