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Deepwalk-aware graph convolutional networks

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Science China(Information Sciences) 2022年第5期65卷 81-95页

作者： Taisong JIN Huaqiang DAI Liujuan CAO Baochang ZHANG Feiyue HUANG Yue GAO Rongrong JI Media Analytics and Computing Lab Department of Computer Science and Technology School of InformaticsXiamen University Media Analytics and Computing Lab Department of Artificial Intelligence School of InformaticsXiamen University School of Automation Science and Electrical Engineering Beihang University Tencent Youtu Lab School of Software Tsinghua University

graph convolutional networks(GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph data.The fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer,and Pubmed citation networks.

关键词： graph convolutional networks global information fusion node classification

Environment Information-Based Channel Prediction Method Assisted by graph Neural Network

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China Communications 2022年第11期19卷 1-15页

作者： Yutong Sun Jianhua Zhang Yuxiang Zhang Li Yu Qixing Wang Guangyi Liu State Key Lab of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Future Research Laboratory China Mobile Research InstituteBeijing 100053China

Recently,whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel.This paper presents an environment information-based channel prediction(EICP)method for connecting the environment with the channel assisted by the graph neural networks(GNN).Firstly,the effective scatterers(ESs)producing paths and the primary scatterers(PSs)generating single propagation paths are detected by building the scatterercentered communication environment graphs(SCCEGs),which can simultaneously preserve the structure information and highlight the pending scatterer.The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers.Secondly,large-scale parameters(LSP)and small-scale parameters(SSP)are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs.Simulation results show that the average normalized mean squared error(NMSE)of LSP and SSP predictions are 0.12 and 0.008,which outperforms the methods of linear data learning.

关键词： channel prediction propagation environment graph scatterer detection GNN

On Conjugacy Class graph of Normal Subgroup

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Algebra Colloquium 2022年第3期29卷 437-442页

作者： Ruifang Chen Xianhe Zhao College of Mathematics and Information Science Henan Normal University XinxiangHenan 453007China

Let G be a finite group and N a normal subgroup of G.Denote by Γ_(G)(N)the graph whose vertices are all distinct G-conjugacy class sizes of non-central elements in N,and two vertices of Γ_(G)(N)are adjacent if and only if they are not coprime numbers.We prove that if the center Z(N)=Z(G)∩N and Γ_(G)(N)is k-regular for k≥1,then either a section of Nis a quasi-Frobenius group or Γ_(G)(N)is a complete graph with k+1 vertices.

关键词： normal subgroup conjugacy class size quasi-Frobenius group graph

A Note on the Aα-Spectral Radius of a graph

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Journal of Mathematical Research with Applications 2023年第1期43卷 1-8页

作者： Hongzhang CHEN Jianxi LI School of Mathematics and Statistics Minnan Normal University

Let G be a simple graph of order n and ρα(G) be the Aα(G)-spectral radius of G.In this note,for any vertex viof G,we establish the relationship between ρα(G) and ρα(G—vi).

关键词： graph Aα-spectral radius Principal eigenvector

graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 584-595页

作者： Chengcai Leng Hai Zhang Guorong Cai Irene Cheng Anup Basu the School of Mathematics Northwest University School of Mathematics and Information Sciences Nanchang Hangkong University Nanchang 330063 China the Department of Computing Science University of Alberta the Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines Macao University of Science and Technology the College of Computer Engineering Jimei University the Department of Computing Science University of Alberta

This paper proposes a graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.

关键词： Data clustering dimensionality reduction graph regularization Lp smooth non-negative matrix factorization(SNMF)

Independent Component Analysis and graph Theoretical Analysis in Patients with Narcolepsy

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Neuroscience Bulletin 2019年第4期35卷 743-755页

作者： Fulong Xiao Chao Lu Dianjiang Zhao Qihong Zou Liyue Xu Jing Li Jun Zhang Fang Han Sleep Medicine Center Department of Respiratory and Critical Care Medicine Peking University People's Hospital Beijing 100044 China Department of Radiology Peking University International Hospital Beijing 102206 China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 China PKU-UPenn Sleep Center Peking University International Hospital Beijing 102206 China Department of Neurology Peking University People's Hospital Beijing 100044 China

The present study was aimed to evaluate restingstate functional connectivity and topological properties of brain networks in narcolepsy patients compared with healthy controls.Resting-state fMRI was performed in 26 adult narcolepsy patients and 30 matched healthy controls.MRI data were first analyzed by group independent component analysis,then a graph theoretical method was applied to evaluate the topological properties in the whole brain.Small-world network parameters and nodal topological properties were measured.Altered topological properties in brain areas between groups were selected as regionof-interest seeds,then the functional connectivity among these seeds was compared between groups.Partial correlation analysis was performed to evaluate the relationship between the severity of sleepiness and functional connectivity or topological properties in the narcolepsy patients.Twenty-one independent components out of 48 were obtained.Compared with healthy controls,the narcolepsy patients exhibited significantly decreased functional connectivity within the executive and salience networks,along with increased functional connectivity in the bilateral frontal lobes within the executive network.There were no differences in small-world network properties between patients and controls.The altered brain areas in nodal topological properties between groups were mainly in the inferior frontal cortex,basal ganglia,anterior cingulate,sensory cortex,supplementary motor cortex,and visual cortex.In the partial correlation analysis,nodal topological properties in the putamen,anterior cingulate,and sensory cortex as well as functional connectivity between these regions were correlated with the severity of sleepiness(sleep latency,REM sleep latency,and Epworth sleepiness score)among narcolepsy patients.Altered connectivity within the executive and salience networks was found in narcolepsy patients.Functional connection changes between the left frontal cortex and left caudate nucleus may be one of the parameters describing the sev

关键词： Narcolepsy Functional connectivity Independent component analysis graph theory

graph-ranking collective Chinese entity linking algorithm

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Frontiers of Computer Science 2020年第2期14卷 291-303页

作者： Tao XIE Bin WU Bingjing JIA Bai WANG Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China

Entity linking(EL)systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base.Existing EL approaches usually ignore the semantic correlation between the mentions in the text,and are limited to the scale of the local knowledge base.In this paper,we propose a novel graphranking collective Chinese entity linking(GRCCEL)algorithm,which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources.By improved weighted word2vec textual similarity and improved PageRank algorithm,more semantic information and structural information can be captured in the document.With an incremental evidence mining process,more powerful discrimination capability for similar entities can be obtained.We evaluate the performance of our algorithm on some open domain corpus.Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.

关键词： collective entity linking knowledge mapping word embedding entity correlation graph PageRank

graph Embedding Based API graph Search and Recommendation

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Journal of Computer Science & Technology 2019年第5期34卷 993-1006页

作者： Chun-Yang Ling Yan-Zhen Zou Ze-Qi Lin Bing Xie Key Laboratory of High Confidence Software Technologies Ministry of EducationBeijing 100871China School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China

Searching application programming interfaces (APIs) is very important for developers to reuse software projects. Existing natural language based API search mainly faces the following challenges. 1) More accurate results are required as software projects evolve to be more heterogeneous and complex. 2) The semantic relationships between APIs (e.g., inheritances between classes, and invocations between methods) need to be illustrated so that developers can better understand their usage scenarios. To deal with these issues, we propose GeAPI, a novel graph embedding based approach for API graph search and recommendation in this paper. First, we build a software project’s API graph automatically from its source code and represent each API using graph embedding methods. Second, we search the API graph with a question in natural language, and return the corresponding subgraph that is composed of relevant code elements and their associated relationships, as the best answer of the question. In experiments, we select three well-known open source projects, JodaTime, Apache Lucene and POI, as examples to perform API search tasks. The experimental results show that our approach GeAPI improves F1-score by 10% compared with the existing shortest path based API search approach, while reduces the average response time about 60 times.

关键词： application programming interface (API) recommendation API graph graph embedding software reuse

graph-theoretic algorithms for cyber-physical vulnerability analysis of power grid with incomplete information

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Journal of Modern Power Systems and Clean Energy 2018年第5期6卷 887-899页

作者： Anurag K.SRIVASTAVA Timothy A.ERNSTER Ren LIU Vignesh G.KRISHNAN Department of Electrical Engineering and Computer Science Washington State University U.S. Army Corps of Engineers Dominion Power

A key focus recently has been on assessing therisk of a coordinated cyber-physical attack and minimizing the impact of a successful attack. Most of the cyberattackers will have limited system information and conventional power grid N-1 security analysis cannot be extended to assess the risk. Centrality measures are widely used in the network science and an attacker with incomplete information can use it to identify power system vulnerabilities by defining the system as a complex network but without real-time system measurements. This paper presents a graph theory based centrality indices for vulnerability assessment of the power system due to various bus and branch contingencies using limited system information and provides a preliminary defense mechanism to prevent such an attack. Proposed work answers the fundamental question of possible attack scenarios by balancing risk(limited information with low risk to get caught orhigh risk attack to access more system information) and impact(identifying contingencies with maximal impact on system operation). Statistical comparisons are made between the graph theory measures compared to the corresponding DC power flow based N-X linear sensitivity measures. A unified N-X centrality based performance index is proposed and validated against the AC power flow based performance index by doing the real-time simulations of an N-3 attack scenario. Defensive mechanisms using topology-based performance indices are also presented.

关键词： Cyber-physical security graph theory Transmission system Contingency Defense mechanism Incomplete information