Ego-network probabilistic graphical model for discovering on-line communities
Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ding, Fei [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 48(2018), 9 vom: 06. Feb., Seite 3038-3052 |
|---|---|
| Übergeordnetes Werk: |
volume:48 ; year:2018 ; number:9 ; day:06 ; month:02 ; pages:3038-3052 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10489-018-1137-y |
|---|
| Katalog-ID: |
OLC2066105325 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2066105325 | ||
| 003 | DE-627 | ||
| 005 | 20230502205026.0 | ||
| 007 | tu | ||
| 008 | 200820s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10489-018-1137-y |2 doi | |
| 035 | |a (DE-627)OLC2066105325 | ||
| 035 | |a (DE-He213)s10489-018-1137-y-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 Ding, Fei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Ego-network probabilistic graphical model for discovering on-line communities |
| 264 | 1 | |c 2018 | |
| 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 © Springer Science+Business Media, LLC, part of Springer Nature 2018 | ||
| 520 | |a Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. | ||
| 650 | 4 | |a Social network analysis | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Community discovery | |
| 650 | 4 | |a Bayesian network | |
| 650 | 4 | |a Ego network | |
| 700 | 1 | |a Zhuang, Yi |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied intelligence |d Springer US, 1991 |g 48(2018), 9 vom: 06. Feb., Seite 3038-3052 |w (DE-627)130990515 |w (DE-600)1080229-0 |w (DE-576)029154286 |x 0924-669X |7 nnns |
| 773 | 1 | 8 | |g volume:48 |g year:2018 |g number:9 |g day:06 |g month:02 |g pages:3038-3052 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10489-018-1137-y |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 48 |j 2018 |e 9 |b 06 |c 02 |h 3038-3052 | ||
| author_variant |
f d fd y z yz |
|---|---|
| matchkey_str |
article:0924669X:2018----::gntokrbblsigahcloefricvrn |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s10489-018-1137-y doi (DE-627)OLC2066105325 (DE-He213)s10489-018-1137-y-p DE-627 ger DE-627 rakwb eng 004 VZ Ding, Fei verfasserin aut Ego-network probabilistic graphical model for discovering on-line communities 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. Social network analysis Machine learning Community discovery Bayesian network Ego network Zhuang, Yi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 06. Feb., Seite 3038-3052 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:06 month:02 pages:3038-3052 https://doi.org/10.1007/s10489-018-1137-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 06 02 3038-3052 |
| spelling |
10.1007/s10489-018-1137-y doi (DE-627)OLC2066105325 (DE-He213)s10489-018-1137-y-p DE-627 ger DE-627 rakwb eng 004 VZ Ding, Fei verfasserin aut Ego-network probabilistic graphical model for discovering on-line communities 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. Social network analysis Machine learning Community discovery Bayesian network Ego network Zhuang, Yi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 06. Feb., Seite 3038-3052 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:06 month:02 pages:3038-3052 https://doi.org/10.1007/s10489-018-1137-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 06 02 3038-3052 |
| allfields_unstemmed |
10.1007/s10489-018-1137-y doi (DE-627)OLC2066105325 (DE-He213)s10489-018-1137-y-p DE-627 ger DE-627 rakwb eng 004 VZ Ding, Fei verfasserin aut Ego-network probabilistic graphical model for discovering on-line communities 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. Social network analysis Machine learning Community discovery Bayesian network Ego network Zhuang, Yi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 06. Feb., Seite 3038-3052 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:06 month:02 pages:3038-3052 https://doi.org/10.1007/s10489-018-1137-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 06 02 3038-3052 |
| allfieldsGer |
10.1007/s10489-018-1137-y doi (DE-627)OLC2066105325 (DE-He213)s10489-018-1137-y-p DE-627 ger DE-627 rakwb eng 004 VZ Ding, Fei verfasserin aut Ego-network probabilistic graphical model for discovering on-line communities 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. Social network analysis Machine learning Community discovery Bayesian network Ego network Zhuang, Yi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 06. Feb., Seite 3038-3052 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:06 month:02 pages:3038-3052 https://doi.org/10.1007/s10489-018-1137-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 06 02 3038-3052 |
| allfieldsSound |
10.1007/s10489-018-1137-y doi (DE-627)OLC2066105325 (DE-He213)s10489-018-1137-y-p DE-627 ger DE-627 rakwb eng 004 VZ Ding, Fei verfasserin aut Ego-network probabilistic graphical model for discovering on-line communities 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. Social network analysis Machine learning Community discovery Bayesian network Ego network Zhuang, Yi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 06. Feb., Seite 3038-3052 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:06 month:02 pages:3038-3052 https://doi.org/10.1007/s10489-018-1137-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 06 02 3038-3052 |
| language |
English |
| source |
Enthalten in Applied intelligence 48(2018), 9 vom: 06. Feb., Seite 3038-3052 volume:48 year:2018 number:9 day:06 month:02 pages:3038-3052 |
| sourceStr |
Enthalten in Applied intelligence 48(2018), 9 vom: 06. Feb., Seite 3038-3052 volume:48 year:2018 number:9 day:06 month:02 pages:3038-3052 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Social network analysis Machine learning Community discovery Bayesian network Ego network |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Applied intelligence |
| authorswithroles_txt_mv |
Ding, Fei @@aut@@ Zhuang, Yi @@aut@@ |
| publishDateDaySort_date |
2018-02-06T00:00:00Z |
| hierarchy_top_id |
130990515 |
| dewey-sort |
14 |
| id |
OLC2066105325 |
| 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">OLC2066105325</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205026.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-018-1137-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066105325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-018-1137-y-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">Ding, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Ego-network probabilistic graphical model for discovering on-line communities</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social network analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Community discovery</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ego network</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhuang, Yi</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">48(2018), 9 vom: 06. Feb., Seite 3038-3052</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:48</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:9</subfield><subfield code="g">day:06</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:3038-3052</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-018-1137-y</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="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">48</subfield><subfield code="j">2018</subfield><subfield code="e">9</subfield><subfield code="b">06</subfield><subfield code="c">02</subfield><subfield code="h">3038-3052</subfield></datafield></record></collection>
|
| author |
Ding, Fei |
| spellingShingle |
Ding, Fei ddc 004 misc Social network analysis misc Machine learning misc Community discovery misc Bayesian network misc Ego network Ego-network probabilistic graphical model for discovering on-line communities |
| authorStr |
Ding, Fei |
| 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 |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0924-669X |
| topic_title |
004 VZ Ego-network probabilistic graphical model for discovering on-line communities Social network analysis Machine learning Community discovery Bayesian network Ego network |
| topic |
ddc 004 misc Social network analysis misc Machine learning misc Community discovery misc Bayesian network misc Ego network |
| topic_unstemmed |
ddc 004 misc Social network analysis misc Machine learning misc Community discovery misc Bayesian network misc Ego network |
| topic_browse |
ddc 004 misc Social network analysis misc Machine learning misc Community discovery misc Bayesian network misc Ego network |
| 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 |
Ego-network probabilistic graphical model for discovering on-line communities |
| ctrlnum |
(DE-627)OLC2066105325 (DE-He213)s10489-018-1137-y-p |
| title_full |
Ego-network probabilistic graphical model for discovering on-line communities |
| author_sort |
Ding, Fei |
| journal |
Applied intelligence |
| journalStr |
Applied intelligence |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
3038 |
| author_browse |
Ding, Fei Zhuang, Yi |
| container_volume |
48 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Ding, Fei |
| doi_str_mv |
10.1007/s10489-018-1137-y |
| dewey-full |
004 |
| title_sort |
ego-network probabilistic graphical model for discovering on-line communities |
| title_auth |
Ego-network probabilistic graphical model for discovering on-line communities |
| abstract |
Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
9 |
| title_short |
Ego-network probabilistic graphical model for discovering on-line communities |
| url |
https://doi.org/10.1007/s10489-018-1137-y |
| remote_bool |
false |
| author2 |
Zhuang, Yi |
| author2Str |
Zhuang, Yi |
| ppnlink |
130990515 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10489-018-1137-y |
| up_date |
2024-07-04T03:46:39.125Z |
| _version_ |
1803618666816733184 |
| 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">OLC2066105325</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205026.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-018-1137-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066105325</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-018-1137-y-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">Ding, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Ego-network probabilistic graphical model for discovering on-line communities</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social network analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Community discovery</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ego network</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhuang, Yi</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">48(2018), 9 vom: 06. Feb., Seite 3038-3052</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:48</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:9</subfield><subfield code="g">day:06</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:3038-3052</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-018-1137-y</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="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">48</subfield><subfield code="j">2018</subfield><subfield code="e">9</subfield><subfield code="b">06</subfield><subfield code="c">02</subfield><subfield code="h">3038-3052</subfield></datafield></record></collection>
|
| score |
7.39983 |