From Louvain to Leiden: guaranteeing well-connected communities View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

V. A. Traag, L. Waltman, N. J. van Eck

ABSTRACT

Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees. More... »

PAGES

5233

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-41695-z

DOI

http://dx.doi.org/10.1038/s41598-019-41695-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112982066

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30914743


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/2008/0802", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Computation Theory and Mathematics", 
        "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": "Leiden University", 
          "id": "https://www.grid.ac/institutes/grid.5132.5", 
          "name": [
            "Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Traag", 
        "givenName": "V. A.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Leiden University", 
          "id": "https://www.grid.ac/institutes/grid.5132.5", 
          "name": [
            "Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Waltman", 
        "givenName": "L.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Leiden University", 
          "id": "https://www.grid.ac/institutes/grid.5132.5", 
          "name": [
            "Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "van Eck", 
        "givenName": "N. J.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nrn2575", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004953014", 
          "https://doi.org/10.1038/nrn2575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.72.027104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005495710"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.72.027104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005495710"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03288", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016416471", 
          "https://doi.org/10.1038/nature03288"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03288", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016416471", 
          "https://doi.org/10.1038/nature03288"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.92.032801", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016611839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.92.032801", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016611839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.78.046110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016699541"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.78.046110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016699541"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1963190.1970376", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019343192"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/asi.23734", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019790832"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physrep.2009.11.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020482279"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.74.036104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021120999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.74.036104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021120999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep30750", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023359647", 
          "https://doi.org/10.1038/srep30750"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0605965104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028061681"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.76.036106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029327122"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.76.036106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029327122"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.70.066111", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035552384"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.70.066111", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035552384"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.74.016110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036285600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.74.016110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036285600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.80.056117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036660715"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.80.056117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036660715"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/asi.22748", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036746088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1742-5468/2008/10/p10008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037912856"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1140/epjb/e2013-40829-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042305618", 
          "https://doi.org/10.1140/epjb/e2013-40829-0"
        ], 
        "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/10.1103/physreve.84.016114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052529617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.84.016114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052529617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.81.046106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060740405"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.81.046106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060740405"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tkde.2007.190689", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061661739"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.17706/ijcee.2016.8.3.207-218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068441840"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2992785", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084227892"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/208819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092408218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/208819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092408218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/208819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092408218"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-019-41695-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "From Louvain to Leiden: guaranteeing well-connected communities", 
    "pagination": "5233", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "07645a2a3638e989c981aca03662ce1a4cc7846a463b6dbd38a331c4e890e831"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30914743"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-019-41695-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112982066"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-019-41695-z", 
      "https://app.dimensions.ai/details/publication/pub.1112982066"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:54", 
    "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/v90/0000000371_0000000371/records_130808_00000006.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-019-41695-z"
  }
]
 

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.10.1038/s41598-019-41695-z'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-41695-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-41695-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-41695-z'


 

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

158 TRIPLES      21 PREDICATES      54 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-019-41695-z schema:about anzsrc-for:08
2 anzsrc-for:0802
3 schema:author N836f685aa6a64485a9e460463ed319d8
4 schema:citation sg:pub.10.1038/nature03288
5 sg:pub.10.1038/nrn2575
6 sg:pub.10.1038/srep30750
7 sg:pub.10.1140/epjb/e2013-40829-0
8 https://doi.org/10.1002/asi.22748
9 https://doi.org/10.1002/asi.23734
10 https://doi.org/10.1016/j.physrep.2009.11.002
11 https://doi.org/10.1073/pnas.0605965104
12 https://doi.org/10.1088/1742-5468/2008/10/p10008
13 https://doi.org/10.1101/208819
14 https://doi.org/10.1103/physreve.69.026113
15 https://doi.org/10.1103/physreve.70.066111
16 https://doi.org/10.1103/physreve.72.027104
17 https://doi.org/10.1103/physreve.74.016110
18 https://doi.org/10.1103/physreve.74.036104
19 https://doi.org/10.1103/physreve.76.036106
20 https://doi.org/10.1103/physreve.78.046110
21 https://doi.org/10.1103/physreve.80.056117
22 https://doi.org/10.1103/physreve.81.046106
23 https://doi.org/10.1103/physreve.84.016114
24 https://doi.org/10.1103/physreve.92.032801
25 https://doi.org/10.1109/tkde.2007.190689
26 https://doi.org/10.1145/1963190.1970376
27 https://doi.org/10.1145/2992785
28 https://doi.org/10.17706/ijcee.2016.8.3.207-218
29 schema:datePublished 2019-12
30 schema:datePublishedReg 2019-12-01
31 schema:description Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.
32 schema:genre research_article
33 schema:inLanguage en
34 schema:isAccessibleForFree true
35 schema:isPartOf N379deb6c01a647418fe2d674416a2fc5
36 N98e4211db30a47e4a822b360764434e9
37 sg:journal.1045337
38 schema:name From Louvain to Leiden: guaranteeing well-connected communities
39 schema:pagination 5233
40 schema:productId N34cdc986a83f47e7af943e2c408cc704
41 N3f3bc0f2a1484e8f9eeb4431c8ab9e0e
42 N9b1d8a692b4f4b3eac9823d34d2ebd6e
43 Na0b05590a3634ac6988f244f5dece863
44 Neca0b81d09764c469dc09ef5961e8d1e
45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112982066
46 https://doi.org/10.1038/s41598-019-41695-z
47 schema:sdDatePublished 2019-04-11T13:54
48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
49 schema:sdPublisher Nc7b13b05b5a745b196f8949419a2d6f6
50 schema:url https://www.nature.com/articles/s41598-019-41695-z
51 sgo:license sg:explorer/license/
52 sgo:sdDataset articles
53 rdf:type schema:ScholarlyArticle
54 N14c8b754fed748489230dca299a98f2d schema:affiliation https://www.grid.ac/institutes/grid.5132.5
55 schema:familyName van Eck
56 schema:givenName N. J.
57 rdf:type schema:Person
58 N19f33e6dead742b9ae09301c563069b4 schema:affiliation https://www.grid.ac/institutes/grid.5132.5
59 schema:familyName Traag
60 schema:givenName V. A.
61 rdf:type schema:Person
62 N34cdc986a83f47e7af943e2c408cc704 schema:name pubmed_id
63 schema:value 30914743
64 rdf:type schema:PropertyValue
65 N379deb6c01a647418fe2d674416a2fc5 schema:volumeNumber 9
66 rdf:type schema:PublicationVolume
67 N3f3bc0f2a1484e8f9eeb4431c8ab9e0e schema:name nlm_unique_id
68 schema:value 101563288
69 rdf:type schema:PropertyValue
70 N573d43e6084b434abbec2bc476021524 rdf:first N14c8b754fed748489230dca299a98f2d
71 rdf:rest rdf:nil
72 N6f8c00e4e7ba42ba8c3c90da5701412e schema:affiliation https://www.grid.ac/institutes/grid.5132.5
73 schema:familyName Waltman
74 schema:givenName L.
75 rdf:type schema:Person
76 N836f685aa6a64485a9e460463ed319d8 rdf:first N19f33e6dead742b9ae09301c563069b4
77 rdf:rest N954fa7f3c7a94be0a7a458fb023aa99b
78 N954fa7f3c7a94be0a7a458fb023aa99b rdf:first N6f8c00e4e7ba42ba8c3c90da5701412e
79 rdf:rest N573d43e6084b434abbec2bc476021524
80 N98e4211db30a47e4a822b360764434e9 schema:issueNumber 1
81 rdf:type schema:PublicationIssue
82 N9b1d8a692b4f4b3eac9823d34d2ebd6e schema:name dimensions_id
83 schema:value pub.1112982066
84 rdf:type schema:PropertyValue
85 Na0b05590a3634ac6988f244f5dece863 schema:name readcube_id
86 schema:value 07645a2a3638e989c981aca03662ce1a4cc7846a463b6dbd38a331c4e890e831
87 rdf:type schema:PropertyValue
88 Nc7b13b05b5a745b196f8949419a2d6f6 schema:name Springer Nature - SN SciGraph project
89 rdf:type schema:Organization
90 Neca0b81d09764c469dc09ef5961e8d1e schema:name doi
91 schema:value 10.1038/s41598-019-41695-z
92 rdf:type schema:PropertyValue
93 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
94 schema:name Information and Computing Sciences
95 rdf:type schema:DefinedTerm
96 anzsrc-for:0802 schema:inDefinedTermSet anzsrc-for:
97 schema:name Computation Theory and Mathematics
98 rdf:type schema:DefinedTerm
99 sg:journal.1045337 schema:issn 2045-2322
100 schema:name Scientific Reports
101 rdf:type schema:Periodical
102 sg:pub.10.1038/nature03288 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016416471
103 https://doi.org/10.1038/nature03288
104 rdf:type schema:CreativeWork
105 sg:pub.10.1038/nrn2575 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004953014
106 https://doi.org/10.1038/nrn2575
107 rdf:type schema:CreativeWork
108 sg:pub.10.1038/srep30750 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023359647
109 https://doi.org/10.1038/srep30750
110 rdf:type schema:CreativeWork
111 sg:pub.10.1140/epjb/e2013-40829-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042305618
112 https://doi.org/10.1140/epjb/e2013-40829-0
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1002/asi.22748 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036746088
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1002/asi.23734 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019790832
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.physrep.2009.11.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020482279
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1073/pnas.0605965104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028061681
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1088/1742-5468/2008/10/p10008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037912856
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1101/208819 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092408218
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1103/physreve.69.026113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048148225
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1103/physreve.70.066111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035552384
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1103/physreve.72.027104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005495710
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1103/physreve.74.016110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036285600
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1103/physreve.74.036104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021120999
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1103/physreve.76.036106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029327122
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1103/physreve.78.046110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016699541
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1103/physreve.80.056117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036660715
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1103/physreve.81.046106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060740405
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1103/physreve.84.016114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052529617
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1103/physreve.92.032801 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016611839
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1109/tkde.2007.190689 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061661739
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1145/1963190.1970376 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019343192
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1145/2992785 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084227892
153 rdf:type schema:CreativeWork
154 https://doi.org/10.17706/ijcee.2016.8.3.207-218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068441840
155 rdf:type schema:CreativeWork
156 https://www.grid.ac/institutes/grid.5132.5 schema:alternateName Leiden University
157 schema:name Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands
158 rdf:type schema:Organization
 




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


...