nach oben

Social Network Analysis and Mining

Erschienen in:

01.12.2022 | Original Article

A core-periphery structure-based network embedding approach

verfasst von: Soumya Sarkar, Aditya Bhagwat, Animesh Mukherjee

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Recent advances in the field of network representation learning are mostly attributed to the application of the skip-gram model in the context of graphs. State-of-the-art analogs of skip-gram model in graphs define a notion of neighborhood and aim to find the vector representation for a node, which maximizes the likelihood of preserving this neighborhood. In this paper, we propose core2vec, a new algorithmic framework for learning low dimensional continuous feature mapping for a node. We utilize the well-established idea that nodes with similar core numbers play equivalent roles in the network, which is a drastic departure from existing network structure agnostic random walk based neighborhood selection approach. We compare our method against competing methods on downstream word similarity task and obtain significant improvement in performance (best 46%).

Vorheriger Artikel A local updating algorithm for personalized PageRank via Chebyshev polynomials

Nächster Artikel Social media analytics system for action inspection on social networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

In our experiments, we have set \(l=40\) and \(L=10\).

http://www.smallworldofwords.com/new/visualize/

Alvarez-Hamelin JI, Dall’Asta L, Barrat A, Vespignani A (2005) k-core decomposition: a tool for the visualization of large scale networks. arXiv preprint cs/0504107

Alvarez-Hamelin JI, Dall’Asta L, Barrat A, Vespignani A (2006) Large scale networks fingerprinting and visualization using the k-core decomposition. In: Adv Neural Inf Process Syst, 41–50

Batagelj V, Zaversnik M (2003) An o (m) algorithm for cores decomposition of networks. arXiv preprint cs/0310049

Benedek M, Kenett YN, Umdasch K, Anaki D, Faust M, Neubauer AC (2017) How semantic memory structure and intelligence contribute to creative thought: a network science approach. Think Reason 23(2):158–183CrossRef

Cancho RFI, Solé RV (2001) The small world of human language. Proc R Soc Lond B Bio Sci 268(1482):2261–2265CrossRef

Cao S, Lu W, Xu Q (2015) Grarep: learning graph representations with global structural information. In: CIKM, ACM 891–900

Choudhury M, Chatterjee D, Mukherjee A (2010) Global topology of word co-occurrence networks: Beyond the two-regime power-law. In: Coling, Association for Computational Linguistics 162–170

De Deyne S, Perfors A, Navarro DJ (2016) Predicting human similarity judgments with distributional models: the value of word associations. Coling 2016:1861–1870

Dong Y, Chawla NV, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: KDD, ACM , 135–144

Dourisboure Y, Geraci F, Pellegrini M (2007) Extraction and classification of dense communities in the web. In: WWW, ACM, 461–470

Finkelstein L, Gabrilovich E, Matias Y, Rivlin E, Solan Z, Wolfman G, Ruppin E (2001) Placing search in context: the concept revisited. In: WWW, ACM 406–414

Giatsidis C, Malliaros FD, Thilikos DM, Vazirgiannis M (2014) Corecluster: a degeneracy based graph clustering framework. AAAI 14:44–50

Gibson D, Kumar R, Tomkins A (2005) Discovering large dense subgraphs in massive graphs. In: VLDB, VLDB Endowment, 721–732

Gleiser PM, Danon L (2003) Community structure in jazz. Adv Complex Syst 6(04):565–573CrossRef

Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: KDD, ACM, 855–86s

Halawi G, Dror G, Gabrilovich E, Koren Y (2012) Large-scale learning of word relatedness with constraints. In: KDD, pp. 1406–1414. ACM

Hill F, Reichart R, Korhonen A (2015) Simlex-999: evaluating semantic models with (genuine) similarity estimation. Comp Linguist 41(4):665–695MathSciNetCrossRef

Hills T (2013) The company that words keep: comparing the statistical structure of child-versus adult-directed language. J Child Lang 40(3):586–604CrossRef

Hu J, Cheng R, Huang Z, Fang Y, Luo S (2017) On embedding uncertain graphs. In: CIKM, ACM 157–166

Jacob Y, Denoyer L, Gallinari P (2014) Learning latent representations of nodes for classifying in heterogeneous social networks. In: WSDM, ACM 373–382

Jana A, Goyal P (2018) Can network embedding of distributional thesaurus be combined with word vectors for better representation? arXiv preprint arXiv:1802.06196

Keikha MM, Rahgozar M, Asadpour M (2017) Community aware random walk for network embedding. arXiv preprint arXiv:1710.05199

Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888CrossRef

Kunegis J (2013) Konect: the koblenz network collection. In: WWW, ACM, 1343–1350

Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

Mikolov T, Sutskever I, Chen K, Corrado G.S, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Adv Neural Inf Process Syst, 3111–3119

Nelson DL, McEvoy CL, Schreiber TA (2004) The university of south florida free association, rhyme, and word fragment norms. Behav Res Methods Instrum Comput 36(3):402–407CrossRef

Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532–1543

Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: KDD, ACM, 701–710

Riedl M, Biemann C (2013) Scaling to large\(^3\) data: an efficient and effective method to compute distributional thesauri. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 884–890

Rombach P, Porter MA, Fowler JH, Mucha PJ (2017) Core-periphery structure in networks (revisited). SIAM Rev 59(3):619–646MathSciNetCrossRefMATH

Rousseau F, Vazirgiannis M (2015) Main core retention on graph-of-words for single-document keyword extraction. In: ECIR, Springer, 382–393

Sarkar S, Bhagwat A, Mukherjee A (2018) Core2vec: a core-preserving feature learning framework for networks. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 487–490

Seidman SB (1983) Network structure and minimum degree. Soc Netw 5(3):269–287MathSciNetCrossRef

Shin K, Eliassi-Rad T, Faloutsos C (2016) Corescope: graph mining using k-core analysis-patterns, anomalies and algorithms. In: ICDM, IEEE, 469–478

Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: WWW, International World Wide Web Conferences Steering Committee, 1067–1077

Tu C, Wang H, Zeng X, Liu Z, Sun M (2016) Community-enhanced network representation learning for network analysis. arXiv preprint arXiv:1611.06645

Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. In: AAAI, 203–209

Xie R, Liu Z, Jia J, Luan H, Sun M (2016) Representation learning of knowledge graphs with entity descriptions. In: AAAI, 2659–2665

Yang C, Liu Z, Zhao D, Sun M, Chang EY (2015) Network representation learning with rich text information. In: IJCAI, 2111–2117

Titel: A core-periphery structure-based network embedding approach
verfasst von: Soumya Sarkar
Aditya Bhagwat
Animesh Mukherjee
Publikationsdatum: 01.12.2022
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2022
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-021-00749-9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2022

Social data provenance framework based on zero-information loss graph database

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

Social media activity forecasting with exogenous and endogenous signals

A big data analysis of Twitter data during premier league matches: do tweets contain information valuable for in-play forecasting of goals in football?

Cross-platform information spread during the January 6th capitol riots

Event prediction in social network through Twitter messages analysis

Premium Partner