Large-scale structure of time evolving citation networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-09

AUTHORS

E. A. Leicht, G. Clarkson, K. Shedden, M. E.J. Newman

ABSTRACT

In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape. More... »

PAGES

75-83

References to SciGraph publications

  • 2004-03. Detecting community structure in networks in THE EUROPEAN PHYSICAL JOURNAL B
  • 2003-11. Compartments revealed in food-web structure in NATURE
  • 1998-07. How popular is your paper? An empirical study of the citation distribution in THE EUROPEAN PHYSICAL JOURNAL B
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1140/epjb/e2007-00271-7

    DOI

    http://dx.doi.org/10.1140/epjb/e2007-00271-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1002514863


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Michigan\u2013Ann Arbor", 
              "id": "https://www.grid.ac/institutes/grid.214458.e", 
              "name": [
                "Department of Physics, University of Michigan, 48109, Ann Arbor, MI, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Leicht", 
            "givenName": "E. A.", 
            "id": "sg:person.01142104141.95", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01142104141.95"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Michigan\u2013Ann Arbor", 
              "id": "https://www.grid.ac/institutes/grid.214458.e", 
              "name": [
                "School of Information, University of Michigan, 48109, Ann Arbor, MI, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Clarkson", 
            "givenName": "G.", 
            "id": "sg:person.01231375540.15", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01231375540.15"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Michigan\u2013Ann Arbor", 
              "id": "https://www.grid.ac/institutes/grid.214458.e", 
              "name": [
                "Department of Statistics, University of Michigan, 48109, Ann Arbor, MI, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shedden", 
            "givenName": "K.", 
            "id": "sg:person.01232746234.89", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01232746234.89"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Michigan\u2013Ann Arbor", 
              "id": "https://www.grid.ac/institutes/grid.214458.e", 
              "name": [
                "Department of Physics, University of Michigan, 48109, Ann Arbor, MI, USA", 
                "Center for the Study of Complex Systems, University of Michigan, 48109, Ann Arbor, MI, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Newman", 
            "givenName": "M. E.J.", 
            "id": "sg:person.0621643160.19", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621643160.19"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1140/epjb/e2004-00124-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007257290", 
              "https://doi.org/10.1140/epjb/e2004-00124-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/revmodphys.74.47", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008594690"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/revmodphys.74.47", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008594690"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/pan/mpm011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009554991"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0601602103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016125157"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/00018730110112519", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019965146"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s100510050359", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020100757", 
              "https://doi.org/10.1007/s100510050359"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0610537104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024193401"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.socnet.2007.05.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030735183"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.physa.2006.08.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032634805"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/asi.4630270505", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038956878"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.69.066133", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039022482"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.69.066133", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039022482"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/324133.324140", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041136418"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/aos/1176344136", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044872629"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/(sici)1097-4571(199210)43:9<628::aid-asi5>3.0.co;2-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046135794"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature02115", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047625586", 
              "https://doi.org/10.1038/nature02115"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature02115", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047625586", 
              "https://doi.org/10.1038/nature02115"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.69.026113", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048148225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.69.026113", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048148225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1086/228631", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058548129"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tac.1974.1100705", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061471419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.149.3683.510", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062485810"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/s003614450342480", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062877811"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/s003614450342480", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062877811"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2007-09", 
        "datePublishedReg": "2007-09-01", 
        "description": "In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1140/epjb/e2007-00271-7", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1129956", 
            "issn": [
              "1155-4304", 
              "1286-4862"
            ], 
            "name": "The European Physical Journal B", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "59"
          }
        ], 
        "name": "Large-scale structure of time evolving citation networks", 
        "pagination": "75-83", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "0bf937d278d74d76e8ffb1e4eb3c30c76f0ee18f0e8dc811a48967a57ce7d6d4"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1140/epjb/e2007-00271-7"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1002514863"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1140/epjb/e2007-00271-7", 
          "https://app.dimensions.ai/details/publication/pub.1002514863"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T14:06", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8660_00000498.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1140/epjb/e2007-00271-7"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1140/epjb/e2007-00271-7'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1140/epjb/e2007-00271-7'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1140/epjb/e2007-00271-7'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1140/epjb/e2007-00271-7'


     

    This table displays all metadata directly associated to this object as RDF triples.

    148 TRIPLES      21 PREDICATES      47 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1140/epjb/e2007-00271-7 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Ndc4722b1f2704164a0801343b74f61a6
    4 schema:citation sg:pub.10.1007/s100510050359
    5 sg:pub.10.1038/nature02115
    6 sg:pub.10.1140/epjb/e2004-00124-y
    7 https://doi.org/10.1002/(sici)1097-4571(199210)43:9<628::aid-asi5>3.0.co;2-0
    8 https://doi.org/10.1002/asi.4630270505
    9 https://doi.org/10.1016/j.physa.2006.08.022
    10 https://doi.org/10.1016/j.socnet.2007.05.001
    11 https://doi.org/10.1073/pnas.0601602103
    12 https://doi.org/10.1073/pnas.0610537104
    13 https://doi.org/10.1080/00018730110112519
    14 https://doi.org/10.1086/228631
    15 https://doi.org/10.1093/pan/mpm011
    16 https://doi.org/10.1103/physreve.69.026113
    17 https://doi.org/10.1103/physreve.69.066133
    18 https://doi.org/10.1103/revmodphys.74.47
    19 https://doi.org/10.1109/tac.1974.1100705
    20 https://doi.org/10.1126/science.149.3683.510
    21 https://doi.org/10.1137/s003614450342480
    22 https://doi.org/10.1145/324133.324140
    23 https://doi.org/10.1214/aos/1176344136
    24 schema:datePublished 2007-09
    25 schema:datePublishedReg 2007-09-01
    26 schema:description In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape.
    27 schema:genre research_article
    28 schema:inLanguage en
    29 schema:isAccessibleForFree true
    30 schema:isPartOf N014cc3e3c3284e5e86648c12e8500a60
    31 N90fd41a890c74e43babe70ab4d3d7454
    32 sg:journal.1129956
    33 schema:name Large-scale structure of time evolving citation networks
    34 schema:pagination 75-83
    35 schema:productId N04e3dfa5763440f287308fdafdd4037e
    36 Ne163af91b7fd4ad186615ab8bd108925
    37 Nee6a85080d1240b7abe096895fe943d3
    38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002514863
    39 https://doi.org/10.1140/epjb/e2007-00271-7
    40 schema:sdDatePublished 2019-04-10T14:06
    41 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    42 schema:sdPublisher N763462aba62540e49e8551c8149bc3c1
    43 schema:url http://link.springer.com/10.1140/epjb/e2007-00271-7
    44 sgo:license sg:explorer/license/
    45 sgo:sdDataset articles
    46 rdf:type schema:ScholarlyArticle
    47 N014cc3e3c3284e5e86648c12e8500a60 schema:issueNumber 1
    48 rdf:type schema:PublicationIssue
    49 N04e3dfa5763440f287308fdafdd4037e schema:name dimensions_id
    50 schema:value pub.1002514863
    51 rdf:type schema:PropertyValue
    52 N27873cc05611443fa67f7c23aff46afb rdf:first sg:person.01231375540.15
    53 rdf:rest N3a7823ad80c44792ab631532c375b846
    54 N2992749aa5934eb58180376215bb3fd5 rdf:first sg:person.0621643160.19
    55 rdf:rest rdf:nil
    56 N3a7823ad80c44792ab631532c375b846 rdf:first sg:person.01232746234.89
    57 rdf:rest N2992749aa5934eb58180376215bb3fd5
    58 N763462aba62540e49e8551c8149bc3c1 schema:name Springer Nature - SN SciGraph project
    59 rdf:type schema:Organization
    60 N90fd41a890c74e43babe70ab4d3d7454 schema:volumeNumber 59
    61 rdf:type schema:PublicationVolume
    62 Ndc4722b1f2704164a0801343b74f61a6 rdf:first sg:person.01142104141.95
    63 rdf:rest N27873cc05611443fa67f7c23aff46afb
    64 Ne163af91b7fd4ad186615ab8bd108925 schema:name doi
    65 schema:value 10.1140/epjb/e2007-00271-7
    66 rdf:type schema:PropertyValue
    67 Nee6a85080d1240b7abe096895fe943d3 schema:name readcube_id
    68 schema:value 0bf937d278d74d76e8ffb1e4eb3c30c76f0ee18f0e8dc811a48967a57ce7d6d4
    69 rdf:type schema:PropertyValue
    70 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    71 schema:name Information and Computing Sciences
    72 rdf:type schema:DefinedTerm
    73 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    74 schema:name Artificial Intelligence and Image Processing
    75 rdf:type schema:DefinedTerm
    76 sg:journal.1129956 schema:issn 1155-4304
    77 1286-4862
    78 schema:name The European Physical Journal B
    79 rdf:type schema:Periodical
    80 sg:person.01142104141.95 schema:affiliation https://www.grid.ac/institutes/grid.214458.e
    81 schema:familyName Leicht
    82 schema:givenName E. A.
    83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01142104141.95
    84 rdf:type schema:Person
    85 sg:person.01231375540.15 schema:affiliation https://www.grid.ac/institutes/grid.214458.e
    86 schema:familyName Clarkson
    87 schema:givenName G.
    88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01231375540.15
    89 rdf:type schema:Person
    90 sg:person.01232746234.89 schema:affiliation https://www.grid.ac/institutes/grid.214458.e
    91 schema:familyName Shedden
    92 schema:givenName K.
    93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01232746234.89
    94 rdf:type schema:Person
    95 sg:person.0621643160.19 schema:affiliation https://www.grid.ac/institutes/grid.214458.e
    96 schema:familyName Newman
    97 schema:givenName M. E.J.
    98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621643160.19
    99 rdf:type schema:Person
    100 sg:pub.10.1007/s100510050359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020100757
    101 https://doi.org/10.1007/s100510050359
    102 rdf:type schema:CreativeWork
    103 sg:pub.10.1038/nature02115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047625586
    104 https://doi.org/10.1038/nature02115
    105 rdf:type schema:CreativeWork
    106 sg:pub.10.1140/epjb/e2004-00124-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1007257290
    107 https://doi.org/10.1140/epjb/e2004-00124-y
    108 rdf:type schema:CreativeWork
    109 https://doi.org/10.1002/(sici)1097-4571(199210)43:9<628::aid-asi5>3.0.co;2-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046135794
    110 rdf:type schema:CreativeWork
    111 https://doi.org/10.1002/asi.4630270505 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038956878
    112 rdf:type schema:CreativeWork
    113 https://doi.org/10.1016/j.physa.2006.08.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032634805
    114 rdf:type schema:CreativeWork
    115 https://doi.org/10.1016/j.socnet.2007.05.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030735183
    116 rdf:type schema:CreativeWork
    117 https://doi.org/10.1073/pnas.0601602103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016125157
    118 rdf:type schema:CreativeWork
    119 https://doi.org/10.1073/pnas.0610537104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024193401
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1080/00018730110112519 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019965146
    122 rdf:type schema:CreativeWork
    123 https://doi.org/10.1086/228631 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058548129
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1093/pan/mpm011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009554991
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1103/physreve.69.026113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048148225
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1103/physreve.69.066133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039022482
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1103/revmodphys.74.47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008594690
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1109/tac.1974.1100705 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061471419
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1126/science.149.3683.510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062485810
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1137/s003614450342480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062877811
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1145/324133.324140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041136418
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1214/aos/1176344136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044872629
    142 rdf:type schema:CreativeWork
    143 https://www.grid.ac/institutes/grid.214458.e schema:alternateName University of Michigan–Ann Arbor
    144 schema:name Center for the Study of Complex Systems, University of Michigan, 48109, Ann Arbor, MI, USA
    145 Department of Physics, University of Michigan, 48109, Ann Arbor, MI, USA
    146 Department of Statistics, University of Michigan, 48109, Ann Arbor, MI, USA
    147 School of Information, University of Michigan, 48109, Ann Arbor, MI, USA
    148 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...