Search Results for author: Xueqi Ma

Found 9 papers, 2 papers with code

Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders

1 code implementation24 Apr 2024 Chuang Liu, Yuyao Wang, Yibing Zhan, Xueqi Ma, Dapeng Tao, Jia Wu, Wenbin Hu

To this end, we introduce a novel structure-guided masking strategy (i. e., StructMAE), designed to refine the existing GMAE models.

Transfer Learning

Gradformer: Graph Transformer with Exponential Decay

1 code implementation24 Apr 2024 Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma, Shirui Pan, Wenbin Hu

Therefore, this paper presents Gradformer, a method innovatively integrating GT with the intrinsic inductive bias by applying an exponential decay mask to the attention matrix.

Graph Classification Inductive Bias

Exploring Sparsity in Graph Transformers

no code implementations9 Dec 2023 Chuang Liu, Yibing Zhan, Xueqi Ma, Liang Ding, Dapeng Tao, Jia Wu, Wenbin Hu, Bo Du

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks.

Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes

no code implementations21 Nov 2023 Chuang Liu, Wenhang Yu, Kuang Gao, Xueqi Ma, Yibing Zhan, Jia Wu, Bo Du, Wenbin Hu

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.

Graph Representation Learning

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks

no code implementations18 Jul 2022 Chuang Liu, Xueqi Ma, Yibing Zhan, Liang Ding, Dapeng Tao, Bo Du, Wenbin Hu, Danilo Mandic

However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists.

Node Classification

FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS

no code implementations29 Sep 2021 Xueqi Ma, Pan Li, Qiong Cao, James Bailey, Yue Gao

In FAHGNN, we explore the influence of node features for the expressive power of GNNs and augment features by introducing common features and personal features to model information.

Node Classification Representation Learning

How Frequency Effect Graph Neural Networks

no code implementations29 Sep 2021 Xueqi Ma, Yubo Zhang, Weifeng Liu, Yue Gao

Based on the frequency principle on GNNs, we present a novel powerful GNNs framework, Multi-Scale Frequency Enhanced Graph Neural Networks (MSF-GNNs) which considers multi-scale representations from wavelet decomposition.

Node Classification

Hypergraph p-Laplacian Regularization for Remote Sensing Image Recognition

no code implementations21 Jun 2018 Xueqi Ma, Weifeng Liu, Shuying Li, Yicong Zhou

Graph based SSL and manifold regularization based SSL including Laplacian regularization (LapR) and Hypergraph Laplacian regularization (HLapR) are representative SSL methods and have achieved prominent performance by exploiting the relationship of sample distribution.

Ensemble p-Laplacian Regularization for Remote Sensing Image Recognition

no code implementations21 Jun 2018 Xueqi Ma, Weifeng Liu, Dapeng Tao, Yicong Zhou

Therefore, we develop an ensemble p-Laplacian regularization (EpLapR) to fully approximate the intrinsic manifold of the data distribution.

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