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Drug-Target Interactions Prediction Based on Signed Heterogeneous graph neural networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods graph neural networks

Accurate and efficient floor localization with scalable spiking graph neural networks

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Satellite Navigation 2024年第1期5卷 191-206页

作者： Fuqiang Gu Fangming Guo Fangwen Yu Xianlei Long Chao Chen Kai Liu Xuke Hu Jianga Shang Songtao Guo College of Computer Science Chongqing UniversityChongqingChina Department of Precision Instrument Tsinghua UniversityBeijingChina Institute of Data Science German Aerospace Center(DLR)JenaGermany School of Computer Science China University of GeosciencesWuhanChina

Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational *** this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a ***,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural *** approach offers high accuracy,easy scalability to new buildings,and computational *** results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art ***,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.

关键词： Indoor positioning Deep learning Floor localization Spiking neural networks graph neural networks

Re-quantization based binary graph neural networks

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Science China(Information Sciences) 2024年第7期67卷 160-171页

作者： Kai-Lang YAO Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing University

Binary neural networks have become a promising research topic due to their advantages of fast inference speed and low energy consumption. However, most existing studies focus on binary convolutional neural networks, while less attention has been paid to binary graph neural networks. A common drawback of existing studies on binary graph neural networks is that they still include lots of inefficient full-precision operations in multiplying three matrices and are therefore not efficient enough. In this paper, we propose a novel method, called re-quantization-based binary graph neural networks(RQBGN), for binarizing graph neural networks. Specifically, re-quantization, a necessary procedure contributing to the further reduction of superfluous inefficient full-precision operations, quantizes the results of multiplication between any two matrices during the process of multiplying three matrices. To address the challenges introduced by requantization, in RQBGN we first study the impact of different computation orders to find an effective one and then introduce a mixture of experts to increase the model capacity. Experiments on five benchmark datasets show that performing re-quantization in different computation orders significantly impacts the performance of binary graph neural network models, and RQBGN can outperform other baselines to achieve state-of-the-art performance.

关键词： graph neural networks binary neural networks mixture of experts computation-efficient algorithms

Federated Collaborative graph neural networks for Few-shot graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： graph neural networks federated learning collaborative learning few-shot learning graph classification

Robust Regularization Design of graph neural networks Against Adversarial Attacks Based on Lyapunov Theory

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Chinese Journal of Electronics 2024年第3期33卷 732-741页

作者： Wenjie YAN Ziqi LI Yongjun QI School of Artificial Intelligence Hebei University of Technology School of Computer Science and Engineering of North China Institute of Aerospace Engineering

The robustness of graph neural networks（GNNs） is a critical research topic in deep *** researchers have designed regularization methods to enhance the robustness of neural networks,but there is a lack of theoretical analysis on the principle of *** order to tackle the weakness of current robustness designing methods,this paper gives new insights into how to guarantee the robustness of GNNs.A novel regularization strategy named Lya-Reg is designed to guarantee the robustness of GNNs by Lyapunov *** results give new insights into how regularization can mitigate the various adversarial effects on different graph *** experiments on various public datasets demonstrate that the proposed regularization method is more robust than the state-of-theart methods such as L1-norm,L2-norm,L2-norm,Pro-GNN,PA-GNN and GARNET against various types of graph adversarial attacks.

关键词： Training Deep learning Garnets Robustness graph neural networks

Investigating the Logical Capability of graph neural networks via the Connection to C_(2)

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Data Intelligence 2024年第3期6卷 720-748页

作者： Zhangquan Zhou Shijiao Tang School of Command and Control Engineering Army Engineering University of PLA Nanjing 210001China School of Information Science and Engineering Nanjing Audit University Jinshen College Nanjing 210023China

graph neural networks(GNNs)have garnered substantial application across a spectrum of real-world scenarios due to their remarkable ability to handle data organized in the form of ***,the full extent of GNNs'computational properties and logical capability remains a subject of ongoing *** study undertakes an exploration of the logical capabilities intrinsic to GNNs,approaching the matter from a theoretical *** this pursuit,a pivotal connection is established between GNNs and a specific fragment of first-order logic known as C_(2),which serves as a logical framework for modeling graph *** research further amplifies this discourse,introducing a subcategory of GNNs named ACR-GNN,illustrating that GNNs are capable of emulating the evaluation process of unary C,*** on these insights,we introduce an innovative version of GNN architectures capable of dealing with general C,*** attain this,we employ a mechanism known as message passing for GNN *** proposed GNN adaptations allow for simultaneous updating of node and node pair features,thereby enabling the management of both unary and binary C,*** prove that the proposed models exhibit the equivalent expressiveness to C_(2).This underpins the profound alignment between the logical capability of GNNs and the inherent nature of the logical language C,.We conduct several experiments on both of synthetic and real-world datasets to support our *** the experiments,we verify that our suggested models outperform both ACR-GNN and a commonly used model,GIN,when it comes to evaluating C,formulas.

关键词： graph neural networks Logical capabilities C_(2)logical language

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Towards adaptive graph neural networks via solving prior-data conflicts

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 369-383页

作者： Xugang WU Huijun WU Ruibo WANG Xu ZHOU Kai LU College of Computer National University of Defense TechnologyChangsha 410073China

graph neural networks(GNNs)have achieved remarkable performance in a variety of graph-related *** evidence in the GNN community shows that such good performance can be attributed to the homophily prior;i.e.,connected nodes tend to have similar features and ***,in heterophilic settings where the features of connected nodes may vary significantly,GNN models exhibit notable performance *** this work,we formulate this problem as prior-data confict and propose a model called the mixture-prior graph neural network(MPGNN).First,to address the mismatch of homophily prior on heterophilic graphs,we introduce the non-informative prior,which makes no assumptions about the relationship between connected nodes and learns such relationship from the ***,to avoid performance degradation on homophilic graphs,we implement a soft switch to balance the effects of homophily prior and non-informative prior by learnable *** evaluate the performance of MPGNN on both synthetic and real-world *** show that MPGNN can effectively capture the relationship between connected nodes,while the soft switch helps select a suitable prior according to the graph *** these two designs,MPGNN outperforms state-of-the-art methods on heterophilic graphs without sacrificing performance on homophilic graphs.

关键词： graph neural networks Heterophily Prior-data conflict

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Multi-Head Attention Spatial-Temporal graph neural networks for Traffic Forecasting

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Journal of Computer and Communications 2024年第3期12卷 52-67页

作者： Xiuwei Hu Enlong Yu Xiaoyu Zhao School of Computer Science and Technology Shandong University of Technology Zibo China Shandong Deyun Land Real Estate Appraisal Consulting Co. Zibo China College of Computer and Control Engineering Qiqihar University Qiqihar China

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.

关键词： Traffic Prediction Intelligent Traffic System Multi-Head Attention graph neural networks

A review on the applications of graph neural networks in materials science at the atomic scale

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Materials Genome Engineering Advances 2024年第2期2卷 1-19页

作者： Xingyue Shi Linming Zhou Yuhui Huang Yongjun Wu Zijian Hong School of Materials Science and Engineering Zhejiang UniversityHangzhouZhejiangChina Nanhu Brain-Computer Interface Institute HangzhouZhejiangChina Research Institute of Zhejiang University-Taizhou TaizhouZhejiangChina State Key Laboratory of Silicon and Advanced Semiconductor Materials Zhejiang UniversityHangzhouZhejiangChina

In recent years,interdisciplinary research has become increasingly popular within the scientific *** fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer *** neural networks(GNNs)are new machine learning models with powerful feature extraction,relationship inference,and compositional generalization *** advantages drive researchers to design computational models to accelerate material property prediction and new materials design,dramatically reducing the cost of traditional experimental *** review focuses on the principles and applications of the *** basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural ***,the principles and highlights of seven classic GNN models,namely crystal graph convolutional neural networks,iCGCNN,Orbital graph Convolutional neural Network,MatErials graph Network,Global Attention mechanism with graph neural Network,Atomistic Line graph neural Network,and BonDNet are *** connections and differences are also ***,insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.

关键词： CGCNN graph neural networks MACHINE learning materials design property prediction