计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 233-237.doi: 10.11896/jsjkx.190600151
马扬, 程光权, 梁星星, 李妍, 杨雨灵, 刘忠
MA Yang, CHENG Guang-quan, LIANG Xing-xing, LI Yan, YANG Yu-ling, LIU Zhong
摘要: 网络化的数据形式能够表示实体以及实体和实体之间的联系,网络结构在现实世界中普遍存在。研究网络中节点和边的关系具有重要意义。网络表示技术将网络的结构信息转换为节点向量,能够降低图表示的复杂度,同时能够有效运用到分类、网络重构和链路预测等任务中,具有很广泛的应用前景。近年提出的SDNE(Structural Deep Network Embedding)算法在图自编码领域取得了突出成果,文中针对网络表示算法SDNE在有权、有向网络中的局限性,从网络结构和衡量指标两个角度入手,提出了新的基于图自编码的网络表示模型,在原有节点向量的基础上引入了接收向量和发出向量的概念,优化了自编码器的解码部分,进而优化了神经网络的结构,减少了网络的参数以加快收敛速度;提出了基于节点度的衡量指标,将网络的加权特性反映在网络表示的结果中。在3个有向加权数据集中的实验证明,在进行网络重构和链路预测任务时,所提方法能够取得比传统方法和SDNE原始方法更好的结果。
中图分类号:
[1]LV L Y.Link Prediction on Complex Networks[J].Journal of University of Electronic Science and Technology of China,2010,39(5):651-661. [2]LV L,ZHOU T.Link prediction in complex networks:A survey[J].Physica A Statistical Mechanics & Its Applications,2010,390(6):1150-1170. [3]MA Y,LIANG X,HUANG J,et al.Intercity TransportationConstruction Based on Link Prediction[C]//2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).IEEE,2017. [4]KLIMT B.The Enron corpus:A new dataset for email classification research[C]//Proc.15th European Conf.MachineLear-ning.2004. [5]TU C C,YANG C,LIU Z Y,et al.Network representationlearning:an overview[J].Scientia Sinical Informationis,2017(8):32-48. [6]HAMILTON W L,YING R,LESKOVEC J.RepresentationLearning on Graphs:Methods and Applications[J].arxiv:1709.05584. [7]GOYAL P,FERRARA E.Graph Embedding Techniques,Applications,and Performance:A Survey[J].arXiv:1705.02801. [8]BELKIN M.Laplacian eigenmaps and spectral techniques forembedding and clustering[J].Advances in neural information processing systems,2002,14(6):585-591. [9]ADITYA GROVER J L.node2vec:Scalable Feature Learningfor Networks[J].arXiv:1607.00653. [10]GOLDBERG Y.A Primer on Neural Network Models for Natural Language Processing[J].arXiv:1510.00726,2015. [11]WANG D,PENG C,ZHU W.Structural Deep Network Embedding[C]//Acm Sigkdd International Conference on Knowledge Discovery & Data Mining.2016. [12]SAMI A,PEROZZI B,AL-RFOU R.Learning Edge Representations via Low-Rank Asymmetric Projections[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management-(CIKM 17).2017:1787-1796. [13]GOYAL P,KAMRA N,HE X,et al.DynGEM:Deep Embedding Method for Dynamic Graphs[J].arXiv:1805.11273. [14]SUTSKEVER I,VINYALS O,LE Q V.Sequence to Sequence Learning with Neural Networks[J].arXiv:1511.04868. [15]KIM J,PARK H,LEE J E,et al.SIDE:Representation Learning in Signed Directed Networks[C]//the 2018 World Wide Web Conference.2018. [16]CHEN H,PEROZZI B,AL-RFOU R,et al.A Tutorial on Network Embeddings[J].arXiv:1808.02590. [17]CUNCHAO T,XIANGKAI Z,HAO W,et al.A Unified Framework for Community Detection and Network Representation Learning[J].IEEE Transactions on Knowledge and Data Engineering,2019,31(6):1051-1065. [18]GOYAL P,HOSSEINMARDI H,FERRARA E,et al.Capturing Edge Attributes via Network Embedding[J].IEEE Tran-sactions on Computational Social Systems,2018,5(4):907-917. |
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