no code implementations • 9 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.
no code implementations • 21 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.
no code implementations • 18 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.
no code implementations • 29 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.
no code implementations • 29 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.
no code implementations • 21 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.
no code implementations • 21 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.