基于结构深度网络嵌入模型的节点标签分类算法

doi:10.11896/jsjkx.201000177

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 105-112.doi: 10.11896/jsjkx.201000177

• Database & Big Data & Data Science • Previous Articles Next Articles

Node Label Classification Algorithm Based on Structural Depth Network Embedding Model

CHEN Shi-cong¹, YUAN De-yu^1,2, HUANG Shu-hua^1,2, YANG Ming^1,2

1 School of Information and Cyber Security,People’s Public Security University of China,Beijing 100038,China
2 Key Laboratory of Safety Precautions and Risk Assessment,Ministry of Public Security,Beijing 100038,China

Received:2020-10-29 Revised:2021-05-23 Online:2022-03-15 Published:2022-03-15
About author:CHEN Shi-cong,born in 1997,master.His main research interests include cyberspace security and law enforcement technology.
YUAN De-yu,born in 1986,Ph.D,lecturer,Ph.D supervisor.His main research interests include cyber security and complex networks.
Supported by:
National Social Science Foundation of China(20AZD114),Fundamental Research Funds for the Central Universities(2021JKF215),Open Research Fund of the Public Security Behavioral Science Laboratory,People’s Public Security University of China(2020SYS03) and Open Research Fund of Key Laboratory of the Police Internet of Things Application Technology.

Abstract

Abstract: In the era of Internet,where massive data is growing explosively,traditional algorithms have been unable to meet the needs of processing large-scale and multi type data.In recent years,the latest graph embedding algorithm has achieved excellent results in link prediction,network reconstruction and node classification by learning graph network characteristics.Based on the traditional automatic encoder model,a new algorithm combining Sdne algorithm and link prediction similarity matrix is proposed.By introducing a high-order loss function in the process of back-propagation,the performance is adjusted according to the new characteristics of the auto-encoder.The disadvantages of traditional algorithm in determining node similarity in a single way are improved.A simple model is established to analyze and prove the rationality of the optimization.Compared with the most effective Sdne algorithm in the latest research,the improvement effect of this algorithm on Micro-F1 and Macro-F1 two evaluation indicators is close to 1%,and the visual classification effect is good.At the same time,it is found that the optimal value of the hyperparameter of the higher-order loss function is approximately in the range of 1~10,and the change of the numerical value can basically maintain the robustness of the whole network.

Key words: Auto-encoder, Complex network, Deep learning, Network embedding, Node classification

CLC Number:

TP311

CHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming. Node Label Classification Algorithm Based on Structural Depth Network Embedding Model[J].Computer Science, 2022, 49(3): 105-112.

References

[1]LIBENNOWELL D,KLEINBERY J.The link-prediction pro-blem for social networks[J].Journal of the Association for Information Science and Technology,2007,58(7):1019-1031.
[2]LV L Y,JIN C H,ZHOU T.Similarity index based on local paths for link prediction of complex network[J].Phys Reve,2009,80(4):211-223.
[3]SHEIKH N,KEFATO Z T,MONTRESOR A.Semi-Supervised Heterogeneous Information Network Embedding for Node Classification Using 1D-CNN[C]//2018 Fifth International Confe-rence on Social Networks Analysis,Management and Security (SNAMS).IEEE,2018:177-181.
[4]ZHANG D,YIN J,ZHU X,et al.Network representation lear-ning:A survey[J/OL].IEEE Transactions on Big Data.https://ieeexplore.ieee.org/document/8395024.
[5]CAI L J,XU Y B,HE T Q,et al.A New Algorithm of DeepWalk Based On Probability[J].Journal of Physics:Conference Series,2019,1069(1):130-135.
[6]PEROZZI B,AlRFOU R,SKIENA S.Deepwalk:Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2014:701-710.
[7]TANG M,ZHU L,ZOU X C.Document Vector Representation Based on Word2Vec[J].Computer Science,2016,43(6):214-217.
[8]WANG H W,WANG J L,XIE X,et al.GraphGAN:Graph representation learning with generative adversarial nets[J].arXiv,2017,30(22):11-19.
[9]ABU-EL-HAIJA S,PEROZZI B,AL-RFOU R.Learning EdgeRepresentations via Low-Rank Asymmetric Projections[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management(CIKM 17).2017:1787-1796.
[10]GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.ACM,2016:855-864.
[11]TANG J,QU M,WANG M,et al.Line:Large- scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web.2015:1067-1077.
[12]WANG D,CUI P,ZHU W.Structural deep network embedding[C]//Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining.ACM,2016:1225-1234.
[13]LI W Z,YIN D C,YUAN D Y,et al.Particle Propagation Modelfor Dynamic Node Classification[J].IEEE Access,2020(8):140205-140215.
[14]SANJAY K,SANIDHYA C,SAKSHAM K,et al.Node Classification in Complex Networks Using Network Embedding Techniques[C]//Proceedings of the Fifth International Conference on Communication and Electronics Systems.2020:369-374.
[15]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative Adversarial Networks[J].Advances in Neural Information Processing Systems,2014,3:2672-2680.
[16]BENGIO Y,LAMBLIN P,POPOVICI D,et al.Greedy layerwise training of deep networks[C]//International Conference on Neural Information Processing Systems.MIT Press,2006:153-160.
[17]RANZATO M,BOUREAU Y L,LECUN Y.Sparse featurelearning for deep belief networks[C]//Advances in Neural Information Processing Systems.2007:1185-1192.
[18]TOMAS N K,MAX W.Variational Graph Auto-Encoders[J].Springer,2016,28(3):61-63.
[19]BELKIN M,NIYOGI P.Laplacian eigenmaps and spectral techniques for embedding and clustering[J].Advances in Neural Information Processing Systems,2002,46(2),585-591.
[20]LEO K.A new status index derived from sociometric analysis[J].Psychometrika,1953,50(18):39-43.
[21]SALTON G,MCGILL M.Introduction to Modern InformationRetrieval[J].McGraw-Hill,1983,32(6):528-536.
[22]NEWMAN M E J.Clustering and preferential attachment in growing networks[J].Phys Rev E,2001,64(10):251-264.

Related Articles 15

[1]	RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2]	TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[3]	ZHENG Wen-ping, LIU Mei-lin, YANG Gui. Community Detection Algorithm Based on Node Stability and Neighbor Similarity [J]. Computer Science, 2022, 49(9): 83-91.
[4]	XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[5]	WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[6]	HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[7]	JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8]	SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[9]	HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[10]	ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[11]	SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[12]	HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[13]	CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[14]	LIU Wei-ye, LU Hui-min, LI Yu-peng, MA Ning. Survey on Finger Vein Recognition Research [J]. Computer Science, 2022, 49(6A): 1-11.
[15]	SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features [J]. Computer Science, 2022, 49(6A): 12-16.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!