Compressive Network Analysis

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on automatic control Vol. 59; no. 11; pp. 2946 - 2961
Main Authors:	Xiaoye Jiang, Yuan Yao, Han Liu, Guibas, Leonidas
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.11.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algebra Algorithms Atmospheric modeling Automatic control Communities Compressed sensing Computer networks Data models Dictionaries Disengaging Joints Network analysis Networks Noise Vectors Clique detection network data analysis restricted isometry property Radon basis pursuit compressive sensing
ISSN:	0018-9286, 1558-2523
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets.
AbstractList	Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets. Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: and . From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely . Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets. Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets.Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets.
Author	Han Liu Yuan Yao Xiaoye Jiang Guibas, Leonidas
Author_xml	– sequence: 1 surname: Xiaoye Jiang fullname: Xiaoye Jiang organization: Stanford Univ., Stanford, CA, USA – sequence: 2 surname: Yuan Yao fullname: Yuan Yao email: yuany@math.pku.edu.cn organization: Peking Univ., Beijing, China – sequence: 3 surname: Han Liu fullname: Han Liu organization: Princeton Univ., Princeton, NJ, USA – sequence: 4 givenname: Leonidas surname: Guibas fullname: Guibas, Leonidas organization: Stanford Univ., Stanford, CA, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25620806$$D View this record in MEDLINE/PubMed
BookMark	eNqFkc1LIzEYxsOirFX3LihS8OJlat4kk0kuQinrB8h60XNIM-9odDqpyVTxv9-Udot6cC8JIb_n_XieXbLVhQ4JOQA6AqD67G48GTEKYsR4CRWwH2QAZakKVjK-RQaUgio0U3KH7Kb0lJ9SCPhJdlgpGVVUDsjRJMzmEVPyrzj8g_1biM_DcWfb9-TTPtlubJvw1_reI_cXv-8mV8XN7eX1ZHxTOFFVfVHa6VQ2zllXU9o0jlZaorVVU-dD17ZmsnLawlQ6ja4UVa2aSkl0gjsJCvgeOV_VnS-mM6wddn20rZlHP7Px3QTrzeefzj-ah_BqBM8rMZoLnK4LxPCywNSbmU8O29Z2GBbJgGJSQp5L_B-VgmVvhOYZPfmCPoVFzN4sKZBaAdMqU8cfh99M_c_jDNAV4GJIKWKzQYCaZYwmx2iWMZp1jFkiv0ic723vw3J9334nPFwJPSJu-kilOHDO_wI3Hqib
CODEN	IETAA9
CitedBy_id	crossref_primary_10_1109_TCNS_2021_3089141
Cites_doi	10.1080/10556780310001607956 10.1145/1117454.1117459 10.1109/TIT.2005.864420 10.1038/30918 10.1007/BF02248731 10.1145/1772690.1772755 10.1111/j.2517-6161.1996.tb02080.x 10.1145/362342.362367 10.1016/0378-8733(87)90015-3 10.1126/science.286.5439.509 10.1109/SFCS.2000.892065 10.1007/BF02294547 10.1090/S0025-5718-08-02189-3 10.1007/3-540-45995-2_51 10.1214/009053606000001523 10.1287/opre.8.1.101 10.1111/j.1467-9868.2007.00581.x 10.5486/PMD.1959.6.3-4.12 10.1103/PhysRevE.71.065103 10.1002/(SICI)1098-2418(199810/12)13:3/4<457::AID-RSA14>3.3.CO;2-K 10.1046/j.0039-0402.2003.00258.x 10.1137/1.9781611973013.8 10.1073/pnas.0601602103 10.1073/pnas.0907096106 10.1017/CBO9780511811395.011 10.1080/0022250X.1971.9989788 10.1214/lnms/1215467407 10.1080/01621459.1981.10477598 10.1038/nature03607 10.1109/TIT.2004.828141 10.1023/A:1011419012209 10.1007/s10618-010-0186-6 10.1109/TIT.2006.871582 10.1002/9781118032701 10.1371/journal.pone.0012528 10.1198/016214502388618906 10.1103/PhysRevE.80.016118 10.1137/S1064827596304010 10.1016/j.crma.2008.03.014 10.1109/TIT.2005.858979 10.1073/pnas.122653799
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2014 2014 IEEE. 2014
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2014 – notice: 2014 IEEE. 2014
DBID	97E RIA RIE AAYXX CITATION NPM 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D F28 7X8 5PM
DOI	10.1109/TAC.2014.2351712
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic
DatabaseTitleList	Technology Research Database PubMed Technology Research Database MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEL url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-2523
EndPage	2961
ExternalDocumentID	PMC4301620 3472705221 25620806 10_1109_TAC_2014_2351712 6883133
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: ARO grantid: W911NF-10-1-0037; W911NF-07-2-0027 – fundername: Google Corporation – fundername: FDA grantid: HHSF223201000072C – fundername: NSF grantid: CCF 1011228 – fundername: NSF grantid: III-1116730 – fundername: Hundred Talents Program at Peking University – fundername: NSFC grantid: 61071157; 61370004; 11326038 – fundername: National Basic Research Program of China (973 Program 2011CB825501) – fundername: Microsoft Research Asia – fundername: NSF grantid: III-1332109; NIH R01MH102339; NIH R01GM083084; R01HG06841 – fundername: NIMH NIH HHS grantid: R01 MH102339 – fundername: NIGMS NIH HHS grantid: R01 GM083084 – fundername: NHGRI NIH HHS grantid: R01 HG006841
GroupedDBID	-~X .DC 0R~ 29I 3EH 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 VJK ~02 AAYXX CITATION AAYOK NPM PKN RIG Z5M 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D F28 7X8 5PM
ID	FETCH-LOGICAL-c477t-5abb6fccacd00ffc0796eaa7fdaa79dad267c9a1b6c9ec547d8f786ec43c61813
IEDL.DBID	RIE
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000344482500008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0018-9286
IngestDate	Tue Sep 30 16:51:42 EDT 2025 Sun Nov 09 12:30:49 EST 2025 Sun Sep 28 00:43:53 EDT 2025 Mon Jun 30 10:14:25 EDT 2025 Wed Feb 19 01:53:34 EST 2025 Sat Nov 29 05:40:05 EST 2025 Tue Nov 18 22:35:33 EST 2025 Wed Aug 27 02:48:54 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	Clique detection network data analysis restricted isometry property Radon basis pursuit compressive sensing
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c477t-5abb6fccacd00ffc0796eaa7fdaa79dad267c9a1b6c9ec547d8f786ec43c61813
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	http://doi.org/10.1109/TAC.2014.2351712
PMID	25620806
PQID	1616981298
PQPubID	85475
PageCount	16
ParticipantIDs	proquest_journals_1616981298 proquest_miscellaneous_1826610794 crossref_citationtrail_10_1109_TAC_2014_2351712 ieee_primary_6883133 proquest_miscellaneous_1642208493 pubmed_primary_25620806 crossref_primary_10_1109_TAC_2014_2351712 pubmedcentral_primary_oai_pubmedcentral_nih_gov_4301620
PublicationCentury	2000
PublicationDate	2014-Nov. 2014-11-00 2014-Nov 20141101
PublicationDateYYYYMMDD	2014-11-01
PublicationDate_xml	– month: 11 year: 2014 text: 2014-Nov.
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on automatic control
PublicationTitleAbbrev	TAC
PublicationTitleAlternate	IEEE Trans Automat Contr
PublicationYear	2014
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref14 ref11 ref10 ref17 goldenberg (ref24) 2010; 2 lueker (ref36) 0 kleinberg (ref29) 0 ref51 ref50 ref45 ref48 ref47 ref41 ref43 ref49 ref8 ref7 ref9 ref3 ref6 ref5 zhao (ref52) 2006; 7 ref40 diaconis (ref16) 1988 ref35 ref34 ref37 ref31 ref33 ref32 tsaig (ref46) 2006; 52 grimmett (ref25) 1990 ref39 ref38 airoldi (ref2) 2008; 9 abello (ref1) 2002; 2286 ref23 erdös (ref19) 1960; 5 ref26 ref20 ref22 ref21 knuth (ref30) 1993 ref27 jagabathula (ref28) 2008 erdös (ref18) 1959; 6 tibshirani (ref44) 1996; 58 barabasi (ref4) 1999; 286 shi (ref42) 2000; 22 deshpande (ref15) 2013 19658785 - Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jul;80(1 Pt 2):016118 20824084 - PLoS One. 2010 Sep 02;5(9):null 16723398 - Proc Natl Acad Sci U S A. 2006 Jun 6;103(23):8577-82 15944704 - Nature. 2005 Jun 9;435(7043):814-8 16089800 - Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Jun;71(6 Pt 2):065103 12060727 - Proc Natl Acad Sci U S A. 2002 Jun 11;99(12):7821-6 19934050 - Proc Natl Acad Sci U S A. 2009 Dec 15;106(50):21068-73 21701698 - J Mach Learn Res. 2008 Sep;9:1981-2014 9623998 - Nature. 1998 Jun 4;393(6684):440-2 10521342 - Science. 1999 Oct 15;286(5439):509-12
References_xml	– ident: ref37 doi: 10.1080/10556780310001607956 – volume: 7 start-page: 2541 year: 2006 ident: ref52 article-title: On model selection consistency of lasso publication-title: J Mach Learn Res – year: 2013 ident: ref15 article-title: Finding hidden cliques of size $\sqrt{N/e}$ in nearly linear time publication-title: arXiv 1304 7047 – ident: ref41 doi: 10.1145/1117454.1117459 – ident: ref45 doi: 10.1109/TIT.2005.864420 – ident: ref49 doi: 10.1038/30918 – volume: 9 start-page: 1981 year: 2008 ident: ref2 article-title: Mixed membership stochastic blockmodels publication-title: J Mach Learn Res – ident: ref21 doi: 10.1007/BF02248731 – ident: ref34 doi: 10.1145/1772690.1772755 – volume: 58 start-page: 267 year: 1996 ident: ref44 article-title: Regression shrinkage and selection via the lasso publication-title: J Roy Statist Soc B doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: ref6 doi: 10.1145/362342.362367 – volume: 5 start-page: 17 year: 1960 ident: ref19 article-title: On the evolution of random graphs publication-title: Pub Mathemat Inst Hungrian Acad of Sci – ident: ref47 doi: 10.1016/0378-8733(87)90015-3 – volume: 286 start-page: 509 year: 1999 ident: ref4 article-title: Emergence of scaling in random networks publication-title: Science doi: 10.1126/science.286.5439.509 – ident: ref32 doi: 10.1109/SFCS.2000.892065 – ident: ref48 doi: 10.1007/BF02294547 – ident: ref7 doi: 10.1090/S0025-5718-08-02189-3 – volume: 2286 start-page: 598 year: 2002 ident: ref1 article-title: Massive quasi-clique detection publication-title: Lecture Notes in Computer Science doi: 10.1007/3-540-45995-2_51 – ident: ref10 doi: 10.1214/009053606000001523 – ident: ref13 doi: 10.1287/opre.8.1.101 – volume: 22 year: 2000 ident: ref42 article-title: Normalized cuts and image segmentation publication-title: IEEE Trans Pattern Anal Mach Intell – ident: ref51 doi: 10.1111/j.1467-9868.2007.00581.x – volume: 6 start-page: 290 year: 1959 ident: ref18 article-title: On random graphs, i publication-title: Publicationes Mathematicae doi: 10.5486/PMD.1959.6.3-4.12 – ident: ref39 doi: 10.1103/PhysRevE.71.065103 – ident: ref3 doi: 10.1002/(SICI)1098-2418(199810/12)13:3/4<457::AID-RSA14>3.3.CO;2-K – year: 0 ident: ref29 article-title: The web as a graph: Measurements, models, methods publication-title: Proc Int Computing and Combinatorics Conf – ident: ref17 doi: 10.1046/j.0039-0402.2003.00258.x – year: 1990 ident: ref25 publication-title: Disorder in Physical Systems a Volume in Honour of John M Hammersley – ident: ref14 doi: 10.1137/1.9781611973013.8 – ident: ref38 doi: 10.1073/pnas.0601602103 – volume: 2 year: 2010 ident: ref24 article-title: A survey of statistical network models publication-title: Found and Trends in Mach Learn – ident: ref5 doi: 10.1073/pnas.0907096106 – ident: ref43 doi: 10.1017/CBO9780511811395.011 – ident: ref35 doi: 10.1080/0022250X.1971.9989788 – year: 1988 ident: ref16 publication-title: Group Representations in Probability and Statistics doi: 10.1214/lnms/1215467407 – ident: ref27 doi: 10.1080/01621459.1981.10477598 – ident: ref40 doi: 10.1038/nature03607 – ident: ref20 doi: 10.1109/TIT.2004.828141 – ident: ref23 doi: 10.1023/A:1011419012209 – ident: ref12 doi: 10.1007/s10618-010-0186-6 – volume: 52 start-page: 1289 year: 2006 ident: ref46 article-title: Compressed sensing publication-title: IEEE Trans Inform Theory doi: 10.1109/TIT.2006.871582 – ident: ref50 doi: 10.1002/9781118032701 – year: 2008 ident: ref28 article-title: Inferring rankings under constrained sensing publication-title: Neural Inform Process Syst (NIPS) – ident: ref31 doi: 10.1371/journal.pone.0012528 – ident: ref26 doi: 10.1198/016214502388618906 – ident: ref33 doi: 10.1103/PhysRevE.80.016118 – ident: ref11 doi: 10.1137/S1064827596304010 – ident: ref8 doi: 10.1016/j.crma.2008.03.014 – ident: ref9 doi: 10.1109/TIT.2005.858979 – year: 1993 ident: ref30 publication-title: The Stanford GraphBase A Platform for Combinatorial Computing – year: 0 ident: ref36 article-title: Maximization problems on graphs with edge weights chosen from a normal distribution publication-title: ACM Symp Theory of Comput – ident: ref22 doi: 10.1073/pnas.122653799 – reference: 19934050 - Proc Natl Acad Sci U S A. 2009 Dec 15;106(50):21068-73 – reference: 15944704 - Nature. 2005 Jun 9;435(7043):814-8 – reference: 10521342 - Science. 1999 Oct 15;286(5439):509-12 – reference: 20824084 - PLoS One. 2010 Sep 02;5(9):null – reference: 16089800 - Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Jun;71(6 Pt 2):065103 – reference: 21701698 - J Mach Learn Res. 2008 Sep;9:1981-2014 – reference: 19658785 - Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jul;80(1 Pt 2):016118 – reference: 12060727 - Proc Natl Acad Sci U S A. 2002 Jun 11;99(12):7821-6 – reference: 16723398 - Proc Natl Acad Sci U S A. 2006 Jun 6;103(23):8577-82 – reference: 9623998 - Nature. 1998 Jun 4;393(6684):440-2
SSID	ssj0016441
Score	2.1436832
Snippet	Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the...
SourceID	pubmedcentral proquest pubmed crossref ieee
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	2946
SubjectTerms	Algebra Algorithms Atmospheric modeling Automatic control Communities Compressed sensing Computer networks Data models Dictionaries Disengaging Joints Network analysis Networks Noise Vectors
Title	Compressive Network Analysis
URI	https://ieeexplore.ieee.org/document/6883133 https://www.ncbi.nlm.nih.gov/pubmed/25620806 https://www.proquest.com/docview/1616981298 https://www.proquest.com/docview/1642208493 https://www.proquest.com/docview/1826610794 https://pubmed.ncbi.nlm.nih.gov/PMC4301620
Volume	59
WOSCitedRecordID	wos000344482500008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEL customDbUrl: eissn: 1558-2523 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016441 issn: 0018-9286 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS-QwEB9UfPAezq87r7rKHvgiXN20STPJo4ji0-KDB_tW2mTCCUdXdNe_30nbLe4hwr2UQia0-WWSmWS-AM5VUEjoRVqTDqmqHaZ1YWXKsrsIlFW6JtUWm8Dp1Mxm9n4Dfg2xMETUOp_RZXxtbfl-7pbxqmyijZF8ptqETUTdxWoNFoMo17tdlxdwbgaTpLCTh6vr6MOlLnNZZJjlayKoranykXr5r5fkO7Fzu_t_P7wHX3v1cnzV8cM-bFBzAF_eJR08hFHcAlrv11caTzsv8PEqN8k3-H1783B9l_Y1ElKnEBdpUdUxXMdVzgsRghNoNVUVBs8P6yufa3S2ymrtLLlCoTcBjSanpNMs3eV32GrmDf2AcS6D9sYH5FOOyvOsFiSdqW3Gk6YDygQmK9hK1ycQj3Us_pbtQULYkoEuI9BlD3QCF0OPpy55xie0hxG_ga6HLoHRambKfnW9lKylasuaiTUJ_ByaeV1EY0fV0HwZaXgMwigrP6ExUT1hyFQCR91kD99nVZC7C50ArrHBQBDzcq-3NI9_2vzcijdN7nz88YhOYCeOu4tnHMHW4nlJp7DtXhePL89nzNozc9ay9ht1aPXn
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS-QwEB_UE9QHz2_r7Xl74Itgd9MmzcejyImHutzDHvhW2mSCwtE9dNe_30nbLa6I4EspZEKbXz5mJvMFcCK8UKgci0uUPhalVXGZGR4T7848JoUsUdTFJtRopO_uzJ8lOOtiYRCxdj7DQXitbfluYmfhqmwoteakUy3Dl0yIlDXRWp3NIHD25tylLZzqzijJzHB8fhG8uMQg5VmiknSBCdVVVd4TMN_6Sb5iPJdfP_fLW7DZCpj982ZFbMMSVjuw8Srt4C70wiFQ-78-Y3_U-IH359lJ9uDv5a_xxVXcVkmIrVBqGmdFGQJ2bGEdY95bpozEolDe0cO4wqVSWVMkpbQGbSaU015piVZwK4m_831YqSYVHkI_5V467bwiPUekaVIy5FaXJqFpk17xCIZz2HLbphAPlSz-5bUqwUxOQOcB6LwFOoLTrsf_Jn3GB7S7Ab-OroUugt58ZvJ2fz3lJKdKQ7KJ0RH87JppZwRzR1HhZBZoaAxMC8M_oNFBQCHIRAQHzWR33ydhkLozGYFaWAYdQcjMvdhSPdzXGboFHZvU-ej9Ef2Atavx7U1-83t0_Q3WAwZNdGMPVqaPM_wOq_Z5-vD0eFwv8BeUpfhG
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Compressive+Network+Analysis&rft.jtitle=IEEE+transactions+on+automatic+control&rft.au=Jiang%2C+Xiaoye&rft.au=Yao%2C+Yuan&rft.au=Liu%2C+Han&rft.au=Guibas%2C+Leonidas&rft.date=2014-11-01&rft.issn=0018-9286&rft.eissn=1558-2523&rft.volume=59&rft.issue=11&rft.spage=2946&rft.epage=2961&rft_id=info:doi/10.1109%2FTAC.2014.2351712&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9286&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9286&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9286&client=summon