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[1]刘 峥*,郭舒婷,周绮凤,等.网络基础设施中重要网元子图的确定[J].厦门大学学报(自然科学版),2018,57(04):558-564.[doi:10.6043/j.issn.0438-0479.201708002]
 LIU Zheng*,GUO Shuting,ZHOU Qifeng,et al.Element Subgraph Discovery in Networks Infrastructures[J].Journal of Xiamen University(Natural Science),2018,57(04):558-564.[doi:10.6043/j.issn.0438-0479.201708002]
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
57卷
期数:
2018年04期
页码:
558-564
栏目:
研究论文
出版日期:
2018-07-31

文章信息/Info

Title:
Element Subgraph Discovery in Networks Infrastructures
文章编号:
0438-0479(2018)04-0558-07
作者:
刘 峥12*郭舒婷12周绮凤3李 涛12
1.南京邮电大学计算机学院,江苏 南京 210023; 2.江苏省大数据安全与智能处理重点实验室,江苏 南京 210023; 3.厦门大学航天航空学院,福建 厦门 361005
Author(s):
LIU Zheng12*GUO Shuting12ZHOU Qifeng3LI Tao12
1.School of Computer Science and Technology,Nanjing University of Posts and Telecommunications,Nanjing 210023,China; 2.Jiangsu Key Laboratory of Big Data Security & Intelligent Processing,Nanjing 210023,China; 3.School of Aerospace Engineering,Xiamen University,Xiamen 361005,China
关键词:
网元子图 邻域影响度 故障网元 网络基础设施运维
Keywords:
element subgraphs neighborhood influence faulty elements network infrastructure management
分类号:
TP 274
DOI:
10.6043/j.issn.0438-0479.201708002
文献标志码:
A
摘要:
网元子图是指大规模网络基础设施中包含承载具体业务网元的拓扑子图,网元子图可用于网络基础设施运维中的故障排查、诊断与修复.首先定义重要网元的概念; 其次,为确定重要网元子图,提出一个统一框架来度量网元在结构和业务两方面的影响力,将其作为重要网元的衡量标准,并设计了从重要网元扩展生成重要网元子图的高效算法.基于真实的网络基础设施数据以及合成的业务承载数据进行实验,实验结果验证了该方法可以高效地找到高质量的重要网元子图,并用于网络基础设施的运维,提高运维的效率,节省运维的成本.
Abstract:
In many applications,graphs are used to model structural relationships among objects.Large scale network infrastructures can be represented as graphs,where element subgraphs are those subgraphs containing important network elements with many connections and running services.In this paper,we formularize the problem of discovering element subgraphs in network infrastructures.Element subgraphs can help network administrators lower the cost for network infrastructure operation and maintenance.A uniform framework is proposed to model the element importance by using neighborhood influence based on random walk,which considers both structural connections and running services on these network elements.We design an efficient algorithm that skillfully finds the important element subgraphs by expanding the important vertices.Our experiments are based on real data sets with synthetic service information,whose results show that our element subgraphs exhibit high quality.

参考文献/References:

[1] TANG L,LI T,SHWARTZ L,et al.An integrated framework for optimizing automatic monitoring systems in large IT infrastructures[C]∥Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Chicago:ACM,2013:1249-1257.
[2] 李涛.数据挖掘的应用与实践:大数据时代的案例分析[M].厦门:厦门大学出版社,2015:8-9.
[3] KEMPE D,KLEINBERG J,TARDOS é.Maximizing the spread of influence through a social network[C]∥Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Washington DC:ACM,2003:137-146.
[4] LESKOVEC J,KRAUSE C,GUESTRIN C,et al.Cost-effective outbreak detection in networks [C]∥Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2007:420-429.
[5] LEI S,MANIU S,MO L,et al.Online influence maximization[C]∥Proceedings of ACM SIGKDD International Conference on Konwledge Discovery and Data Mining.Sydney:ACM,2015:645-654.
[6] FARAJTABAR M,DU N,RODRIGUEZ M G,et al.Shaping social activity by incentivizing users[J].Advances in Neural Information Processing Systems,2014,27:2474-2482.
[7] JEH G,WIDOM J.SimRank:a measure of structural-context similarity [C]∥Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2002:538-543.
[8] PALMER C.R,FALOUTSOS C.Electricity based external similarity of categorical attributes[C]∥Procee-dings of Pacific-Asia Conference on Knowledge Discovery and Data Mining.New York:ACM,2003:486-500.
[9] NEWMAN M E.Spectral methods for community detection and graph partitioning[J].Physical Review E Statistical Nonlinear and Soft Matter Physics,2013,88(4):042822.
[10] NEWMAN M E J.Community detection and graph partitioning[J].EPL,2013,103(2):330-337.
[11] LIN C C,KANG J R,CHEN J Y.An integer programming approach and visual analysis for detecting hierarchical community structures in social networks[J].Information Sciences,2015,299:296-311.
[12] REN J,WANG J,LI M,et al.Identifying protein complexes based on density and modularity in protein-protein interaction network[J].BMC Systems Biology,2013,7(S4):1-15.
[13] LIU G,WONG L,CHUA H N.Complex discovery from weighted PPI networks[J].Bioinformatics,2009,25(15):1891-1897.
[14] PONS P,LATAPY M.Computing communities in large networks using random walks[J].Journal of Graph Algorithms and Applications,2006,10(2):191-218.
[15] TONG H,FALOUTSOS C.Center-piece subgraphs:problem definition and fast solutions [C]∥Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2006:404-413.

备注/Memo

备注/Memo:
收稿日期:2017-08-01 录用日期:2018-03-21
基金项目:江苏省自然科学基金(BK20171447); 江苏省高等学校自然科学研究项目(17JKB520024); 2015年度教育部-中国移动科研基金项目(5-10); 南京邮电大学引进人才科研启动基金(NY215045)
*通信作者:zliu@njupt.edu.cn
引文格式:刘峥,郭舒婷,周绮凤,等.网络基础设施中重要网元子图的确定[J].厦门大学学报(自然科学版),2018,57(4):558-564.
Citation:LIU Z,GUO S T,ZHOU Q F,et al.Element subgraph discovery in networks infrastructures[J].J Xiamen Univ Nat Sci,2018,57(4):558-564.(in Chinese)
更新日期/Last Update: 1900-01-01