Network representation learning based on community-aware and adaptive random walk for overlapping community detection
Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Guo, Kun [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 52(2022), 9 vom: 10. Jan., Seite 9919-9937 |
|---|---|
| Übergeordnetes Werk: |
volume:52 ; year:2022 ; number:9 ; day:10 ; month:01 ; pages:9919-9937 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10489-021-02999-8 |
|---|
| Katalog-ID: |
OLC2078962589 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2078962589 | ||
| 003 | DE-627 | ||
| 005 | 20230506032052.0 | ||
| 007 | tu | ||
| 008 | 221220s2022 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10489-021-02999-8 |2 doi | |
| 035 | |a (DE-627)OLC2078962589 | ||
| 035 | |a (DE-He213)s10489-021-02999-8-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Guo, Kun |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Network representation learning based on community-aware and adaptive random walk for overlapping community detection |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 | ||
| 520 | |a Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms | ||
| 650 | 4 | |a Community detection | |
| 650 | 4 | |a Network representation learning | |
| 650 | 4 | |a Community aware | |
| 650 | 4 | |a Random walk | |
| 700 | 1 | |a Wang, Qinze |4 aut | |
| 700 | 1 | |a Lin, Jiaqi |4 aut | |
| 700 | 1 | |a Wu, Ling |0 (orcid)0000-0001-5293-8701 |4 aut | |
| 700 | 1 | |a Guo, Wenzhong |4 aut | |
| 700 | 1 | |a Chao, Kuo-Ming |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied intelligence |d Springer US, 1991 |g 52(2022), 9 vom: 10. Jan., Seite 9919-9937 |w (DE-627)130990515 |w (DE-600)1080229-0 |w (DE-576)029154286 |x 0924-669X |7 nnns |
| 773 | 1 | 8 | |g volume:52 |g year:2022 |g number:9 |g day:10 |g month:01 |g pages:9919-9937 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10489-021-02999-8 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 951 | |a AR | ||
| 952 | |d 52 |j 2022 |e 9 |b 10 |c 01 |h 9919-9937 | ||
| author_variant |
k g kg q w qw j l jl l w lw w g wg k m c kmc |
|---|---|
| matchkey_str |
article:0924669X:2022----::ewrrpeettolannbsdnomntaaenaatvrnowlfr |
| hierarchy_sort_str |
2022 |
| publishDate |
2022 |
| allfields |
10.1007/s10489-021-02999-8 doi (DE-627)OLC2078962589 (DE-He213)s10489-021-02999-8-p DE-627 ger DE-627 rakwb eng 004 VZ Guo, Kun verfasserin aut Network representation learning based on community-aware and adaptive random walk for overlapping community detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms Community detection Network representation learning Community aware Random walk Wang, Qinze aut Lin, Jiaqi aut Wu, Ling (orcid)0000-0001-5293-8701 aut Guo, Wenzhong aut Chao, Kuo-Ming aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 9 vom: 10. Jan., Seite 9919-9937 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:9 day:10 month:01 pages:9919-9937 https://doi.org/10.1007/s10489-021-02999-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 9 10 01 9919-9937 |
| spelling |
10.1007/s10489-021-02999-8 doi (DE-627)OLC2078962589 (DE-He213)s10489-021-02999-8-p DE-627 ger DE-627 rakwb eng 004 VZ Guo, Kun verfasserin aut Network representation learning based on community-aware and adaptive random walk for overlapping community detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms Community detection Network representation learning Community aware Random walk Wang, Qinze aut Lin, Jiaqi aut Wu, Ling (orcid)0000-0001-5293-8701 aut Guo, Wenzhong aut Chao, Kuo-Ming aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 9 vom: 10. Jan., Seite 9919-9937 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:9 day:10 month:01 pages:9919-9937 https://doi.org/10.1007/s10489-021-02999-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 9 10 01 9919-9937 |
| allfields_unstemmed |
10.1007/s10489-021-02999-8 doi (DE-627)OLC2078962589 (DE-He213)s10489-021-02999-8-p DE-627 ger DE-627 rakwb eng 004 VZ Guo, Kun verfasserin aut Network representation learning based on community-aware and adaptive random walk for overlapping community detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms Community detection Network representation learning Community aware Random walk Wang, Qinze aut Lin, Jiaqi aut Wu, Ling (orcid)0000-0001-5293-8701 aut Guo, Wenzhong aut Chao, Kuo-Ming aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 9 vom: 10. Jan., Seite 9919-9937 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:9 day:10 month:01 pages:9919-9937 https://doi.org/10.1007/s10489-021-02999-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 9 10 01 9919-9937 |
| allfieldsGer |
10.1007/s10489-021-02999-8 doi (DE-627)OLC2078962589 (DE-He213)s10489-021-02999-8-p DE-627 ger DE-627 rakwb eng 004 VZ Guo, Kun verfasserin aut Network representation learning based on community-aware and adaptive random walk for overlapping community detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms Community detection Network representation learning Community aware Random walk Wang, Qinze aut Lin, Jiaqi aut Wu, Ling (orcid)0000-0001-5293-8701 aut Guo, Wenzhong aut Chao, Kuo-Ming aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 9 vom: 10. Jan., Seite 9919-9937 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:9 day:10 month:01 pages:9919-9937 https://doi.org/10.1007/s10489-021-02999-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 9 10 01 9919-9937 |
| allfieldsSound |
10.1007/s10489-021-02999-8 doi (DE-627)OLC2078962589 (DE-He213)s10489-021-02999-8-p DE-627 ger DE-627 rakwb eng 004 VZ Guo, Kun verfasserin aut Network representation learning based on community-aware and adaptive random walk for overlapping community detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms Community detection Network representation learning Community aware Random walk Wang, Qinze aut Lin, Jiaqi aut Wu, Ling (orcid)0000-0001-5293-8701 aut Guo, Wenzhong aut Chao, Kuo-Ming aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 9 vom: 10. Jan., Seite 9919-9937 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:9 day:10 month:01 pages:9919-9937 https://doi.org/10.1007/s10489-021-02999-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 9 10 01 9919-9937 |
| language |
English |
| source |
Enthalten in Applied intelligence 52(2022), 9 vom: 10. Jan., Seite 9919-9937 volume:52 year:2022 number:9 day:10 month:01 pages:9919-9937 |
| sourceStr |
Enthalten in Applied intelligence 52(2022), 9 vom: 10. Jan., Seite 9919-9937 volume:52 year:2022 number:9 day:10 month:01 pages:9919-9937 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Community detection Network representation learning Community aware Random walk |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Applied intelligence |
| authorswithroles_txt_mv |
Guo, Kun @@aut@@ Wang, Qinze @@aut@@ Lin, Jiaqi @@aut@@ Wu, Ling @@aut@@ Guo, Wenzhong @@aut@@ Chao, Kuo-Ming @@aut@@ |
| publishDateDaySort_date |
2022-01-10T00:00:00Z |
| hierarchy_top_id |
130990515 |
| dewey-sort |
14 |
| id |
OLC2078962589 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2078962589</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506032052.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-021-02999-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2078962589</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-021-02999-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Guo, Kun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Network representation learning based on community-aware and adaptive random walk for overlapping community detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Community detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Network representation learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Community aware</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random walk</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Qinze</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Jiaqi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Ling</subfield><subfield code="0">(orcid)0000-0001-5293-8701</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Wenzhong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chao, Kuo-Ming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">52(2022), 9 vom: 10. Jan., Seite 9919-9937</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:52</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:9</subfield><subfield code="g">day:10</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:9919-9937</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-021-02999-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">52</subfield><subfield code="j">2022</subfield><subfield code="e">9</subfield><subfield code="b">10</subfield><subfield code="c">01</subfield><subfield code="h">9919-9937</subfield></datafield></record></collection>
|
| author |
Guo, Kun |
| spellingShingle |
Guo, Kun ddc 004 misc Community detection misc Network representation learning misc Community aware misc Random walk Network representation learning based on community-aware and adaptive random walk for overlapping community detection |
| authorStr |
Guo, Kun |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130990515 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0924-669X |
| topic_title |
004 VZ Network representation learning based on community-aware and adaptive random walk for overlapping community detection Community detection Network representation learning Community aware Random walk |
| topic |
ddc 004 misc Community detection misc Network representation learning misc Community aware misc Random walk |
| topic_unstemmed |
ddc 004 misc Community detection misc Network representation learning misc Community aware misc Random walk |
| topic_browse |
ddc 004 misc Community detection misc Network representation learning misc Community aware misc Random walk |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Applied intelligence |
| hierarchy_parent_id |
130990515 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Applied intelligence |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 |
| title |
Network representation learning based on community-aware and adaptive random walk for overlapping community detection |
| ctrlnum |
(DE-627)OLC2078962589 (DE-He213)s10489-021-02999-8-p |
| title_full |
Network representation learning based on community-aware and adaptive random walk for overlapping community detection |
| author_sort |
Guo, Kun |
| journal |
Applied intelligence |
| journalStr |
Applied intelligence |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
txt |
| container_start_page |
9919 |
| author_browse |
Guo, Kun Wang, Qinze Lin, Jiaqi Wu, Ling Guo, Wenzhong Chao, Kuo-Ming |
| container_volume |
52 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Guo, Kun |
| doi_str_mv |
10.1007/s10489-021-02999-8 |
| normlink |
(ORCID)0000-0001-5293-8701 |
| normlink_prefix_str_mv |
(orcid)0000-0001-5293-8701 |
| dewey-full |
004 |
| title_sort |
network representation learning based on community-aware and adaptive random walk for overlapping community detection |
| title_auth |
Network representation learning based on community-aware and adaptive random walk for overlapping community detection |
| abstract |
Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT |
| container_issue |
9 |
| title_short |
Network representation learning based on community-aware and adaptive random walk for overlapping community detection |
| url |
https://doi.org/10.1007/s10489-021-02999-8 |
| remote_bool |
false |
| author2 |
Wang, Qinze Lin, Jiaqi Wu, Ling Guo, Wenzhong Chao, Kuo-Ming |
| author2Str |
Wang, Qinze Lin, Jiaqi Wu, Ling Guo, Wenzhong Chao, Kuo-Ming |
| ppnlink |
130990515 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10489-021-02999-8 |
| up_date |
2024-07-03T22:55:25.333Z |
| _version_ |
1803600344209424384 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2078962589</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506032052.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-021-02999-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2078962589</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-021-02999-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Guo, Kun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Network representation learning based on community-aware and adaptive random walk for overlapping community detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Community detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Network representation learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Community aware</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random walk</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Qinze</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Jiaqi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Ling</subfield><subfield code="0">(orcid)0000-0001-5293-8701</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Wenzhong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chao, Kuo-Ming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">52(2022), 9 vom: 10. Jan., Seite 9919-9937</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:52</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:9</subfield><subfield code="g">day:10</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:9919-9937</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-021-02999-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">52</subfield><subfield code="j">2022</subfield><subfield code="e">9</subfield><subfield code="b">10</subfield><subfield code="c">01</subfield><subfield code="h">9919-9937</subfield></datafield></record></collection>
|
| score |
7.399379 |