Search Results for author: Wenxuan Tu

Found 20 papers, 8 papers with code

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

1 code implementation21 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.

Graph Learning

Message Intercommunication for Inductive Relation Reasoning

no code implementations23 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.

Knowledge Graphs

RARE: Robust Masked Graph Autoencoder

no code implementations4 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.

GANN: Graph Alignment Neural Network for Semi-Supervised Learning

no code implementations14 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.

Node Classification

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network

no code implementations15 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.

Imputation Knowledge Graphs

Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

no code implementations15 Feb 2023 Meng Liu, Ke Liang, Yawei Zhao, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Kunlun He

To solve this issue, by extracting both temporal and structural information to learn more informative node representations, we propose a self-supervised method termed S2T for temporal graph learning.

Graph Learning

Cluster-guided Contrastive Graph Clustering Network

1 code implementation3 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.

Clustering Contrastive Learning +1

Hard Sample Aware Network for Contrastive Deep Graph Clustering

2 code implementations16 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.

Clustering Graph Clustering

A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal

1 code implementation12 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.

General Knowledge Knowledge Graph Embedding +3

Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure

no code implementations19 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.

Contrastive Learning Graph Learning +4

Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

1 code implementation30 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.

Clustering Graph Clustering

Improved Dual Correlation Reduction Network

no code implementations25 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.

Clustering Graph Clustering

Deep Graph Clustering via Dual Correlation Reduction

2 code implementations29 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.

Clustering Graph Clustering

Siamese Attribute-missing Graph Auto-encoder

no code implementations9 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.

Graph Representation Learning

Foreground Object Structure Transfer for Unsupervised Domain Adaptation

no code implementations14 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.

Clustering Unsupervised Domain Adaptation

Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

no code implementations1 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.


Deep Fusion Clustering Network

1 code implementation15 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.

Clustering Deep Clustering +2

Context-Integrated and Feature-Refined Network for Lightweight Object Parsing

no code implementations26 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).

Scene Parsing Semantic Segmentation

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