Search Results for author: Guoji Fu

Found 11 papers, 6 papers with code

Implicit Graph Neural Diffusion Based on Constrained Dirichlet Energy Minimization

no code implementations7 Aug 2023 Guoji Fu, Mohammed Haroon Dupty, Yanfei Dong, Lee Wee Sun

To overcome the over-smoothing problem, we design our implicit graph diffusion layer as the solution of a Dirichlet energy minimization problem with constraints on vertex features, enabling it to trade off smoothing with the preservation of node feature information.

Graph Classification Graph Learning

SyNDock: N Rigid Protein Docking via Learnable Group Synchronization

no code implementations23 May 2023 Yuanfeng Ji, Yatao Bian, Guoji Fu, Peilin Zhao, Ping Luo

Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution.

Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

no code implementations15 Feb 2022 Jintang Li, Bingzhe Wu, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang, Zibin Zheng

Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.

Adversarial Attack Graph Learning

What Has Been Enhanced in my Knowledge-Enhanced Language Model?

1 code implementation2 Feb 2022 Yifan Hou, Guoji Fu, Mrinmaya Sachan

We conduct experiments to verify that our GCS can indeed be used to correctly interpret the KI process, and we use it to analyze two well-known knowledge-enhanced LMs: ERNIE and K-Adapter, and find that only a small amount of factual knowledge is integrated in them.

Graph Attention Language Modelling

$p$-Laplacian Based Graph Neural Networks

1 code implementation14 Nov 2021 Guoji Fu, Peilin Zhao, Yatao Bian

Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and topological information simultaneously.

Node Classification

Robust Dynamic Network Embedding via Ensembles

3 code implementations30 May 2021 Chengbin Hou, Guoji Fu, Peng Yang, Zheng Hu, Shan He, Ke Tang

It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.

Network Embedding

Understanding Graph Neural Networks from Graph Signal Denoising Perspectives

1 code implementation8 Jun 2020 Guoji Fu, Yifan Hou, Jian Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng

This paper aims to provide a theoretical framework to understand GNNs, specifically, spectral graph convolutional networks and graph attention networks, from graph signal denoising perspectives.

Denoising Graph Attention +2

Learning Topological Representation for Networks via Hierarchical Sampling

1 code implementation15 Feb 2019 Guoji Fu, Chengbin Hou, Xin Yao

To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network.

Link Prediction Representation Learning

Representation Learning for Heterogeneous Information Networks via Embedding Events

1 code implementation29 Jan 2019 Guoji Fu, Bo Yuan, Qiqi Duan, Xin Yao

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.

Link Prediction Node Classification +1

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