no code implementations • 7 Jul 2024 • Fangqiang Xu, Wenxuan Tu, Fan Feng, Malitha Gunawardhana, Jiayuan Yang, Yun Gu, Jichao Zhao
Specifically, we design a shuffle-then-reorder attention module to adjust the position of disrupted objects in the latent space using dynamic generation ratios, such that the vital dependencies among these random cropping targets could be well captured and preserved.
1 code implementation • 21 Sep 2023 • Meng Liu, Ke Liang, Dayu Hu, Hao Yu, Yue Liu, Lingyuan Meng, Wenxuan Tu, Sihang Zhou, Xinwang Liu
We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.
no code implementations • 23 May 2023 • Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang, Wenxuan Tu, Yue Liu, Meng Liu, Xinwang Liu
However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.
2 code implementations • 18 May 2023 • Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu
To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs.
Ranked #1 on
Node Clustering
on arXivCS
no code implementations • 4 Apr 2023 • Wenxuan Tu, Qing Liao, Sihang Zhou, Xin Peng, Chuan Ma, Zhe Liu, Xinwang Liu, Zhiping Cai
To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space.
no code implementations • 14 Mar 2023 • Linxuan Song, Wenxuan Tu, Sihang Zhou, Xinwang Liu, En Zhu
Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning.
no code implementations • 15 Feb 2023 • Wenxuan Tu, Bin Xiao, Xinwang Liu, Sihang Zhou, Zhiping Cai, Jieren Cheng
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world.
no code implementations • 15 Feb 2023 • Meng Liu, Ke Liang, Yawei Zhao, Wenxuan Tu, Sihang Zhou, Xinbiao Gan, Xinwang Liu, Kunlun He
To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations.
1 code implementation • 3 Jan 2023 • Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, En Zhu
Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views.
2 code implementations • 16 Dec 2022 • Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, Cancan Chen
Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.
1 code implementation • 12 Dec 2022 • Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun
According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.
no code implementations • 19 Nov 2022 • Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, Xinwang Liu
To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models.
1 code implementation • 30 May 2022 • Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu
Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions.
no code implementations • 25 Feb 2022 • Yue Liu, Sihang Zhou, Xinwang Liu, Wenxuan Tu, Xihong Yang
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task.
1 code implementation • CVPR 2022 • Siwei Wang, Xinwang Liu, Li Liu, Wenxuan Tu, Xinzhong Zhu, Jiyuan Liu, Sihang Zhou, En Zhu
Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations.
2 code implementations • 29 Dec 2021 • Yue Liu, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, En Zhu
To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner.
no code implementations • 9 Dec 2021 • Wenxuan Tu, Sihang Zhou, Yue Liu, Xinwang Liu
First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit from more abundant and diverse information.
no code implementations • 14 Sep 2021 • Jieren Cheng, Le Liu, Xiangyan Tang, Wenxuan Tu, Boyi Liu, Ke Zhou, Qiaobo Da, Yue Yang
In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance.
no code implementations • 1 May 2021 • Chen Zhang, Siwei Wang, Wenxuan Tu, Pei Zhang, Xinwang Liu, Changwang Zhang, Bo Yuan
Multi-view clustering is an important yet challenging task in machine learning and data mining community.
no code implementations • 21 Mar 2021 • Mingjie Luo, Siwei Wang, Xinwang Liu, Wenxuan Tu, Yi Zhang, Xifeng Guo, Sihang Zhou, En Zhu
Clustering is a fundamental task in the computer vision and machine learning community.
1 code implementation • 15 Dec 2020 • Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, Jieren Cheng
Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning.
no code implementations • 26 Jul 2019 • Bin Jiang, Wenxuan Tu, Chao Yang, Junsong Yuan
The core components of CIFReNet are the Long-skip Refinement Module (LRM) and the Multi-scale Context Integration Module (MCIM).