Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Tensor-Based Analysis for Urban Networks

  • Konstantinos Pelechrinis
  • Yu-Ru Lin
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_110174




Factorization, Latent patterns

Urban networks

Composite/Heterogeneous networks


Definition 1

A composite network\( {\mathcal{N}}_c \)

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  1. Batty M (2008) The size, scale, and shape of cities. Science 319(5864):769–771CrossRefGoogle Scholar
  2. Bettencourt LMA, Lobo J, Helbing D, Khnert C, West GB (2007) Growth, innovation, scaling, and the pace of life in cities. Proc Natl Acad Sci 104(17):7301–7306CrossRefGoogle Scholar
  3. Bro R, Kiers HA (2003) A new efficient method for determining the number of components in parafac models. J Chemom 17(5):274–286CrossRefGoogle Scholar
  4. Cheng Z, Caverlee J, Lee K, Sui DZ (2011) Exploring millions of footprints in location sharing services. ICWSM 2011:81–88Google Scholar
  5. Chowell G, Hyman JM, Eubank S, Castillo-Chavez C (2003) Scaling laws for the movement of people between locations in a large city. Phys Rev E 68(6):066102CrossRefGoogle Scholar
  6. Coulton C (2005) The place of community in social work practice research: conceptual and methodological developments. Soc Work Res 29(2):73–86CrossRefGoogle Scholar
  7. Cullen I, Godson V (1975) Urban networks: the structure of activity patterns. Prog Plan 4:1–96CrossRefGoogle Scholar
  8. Fu Y, Xiong H, Ge Y, Yao Z, Zheng Y, Zhou Z-H (2014) Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ‘14. ACM, New York, pp 1047–1056Google Scholar
  9. Harshman RA (1970) Foundations of the parafac procedure: models and conditions for an“ explanatory” multimodal factor analysis. 84.Google Scholar
  10. Ji M, Sun Y, Danilevsky M, Han J, Gao J 2010 Graph regularized transductive classification on heterogeneous information networks. In: Proceedings of the 2010 European conference on machine learning and knowledge discovery in databases: part I, ECML PKDD’10. Springer, Berlin, pp 570–586CrossRefGoogle Scholar
  11. Jiang M, Cui P, Wang F, Xu X, Zhu W, Yang S (2014) Fema: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, NYC, NY, pp 1186–1195Google Scholar
  12. Kolda TG, Sun J (2008) Scalable tensor decompositions for multi-aspect data mining. In: Data mining, 2008. ICDM’08. Eighth IEEE international conference on, IEEE, Pisa, Italy, pp 363–372Google Scholar
  13. Lin Y-R (2014) Assessing sentiment segregation in urban communities. In: International conference on social computing (SocialCom 2014). ACE, Sydney, AustraliaGoogle Scholar
  14. Lin, Sun, Castro, Konuru, Sundaram, Kelliher (2009) Metafac. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (SIGKDD 2009). ACM, Paris, France, pp 527–536Google Scholar
  15. Maruhashi K, Guo F, Faloutsos C (2011) Multiaspectforensics: pattern mining on large-scale heterogeneous networks with tensor analysis. In: Proceedings of the third international conference on advances in social network analysis and mining, Kaohsiung, TaiwanGoogle Scholar
  16. Papalexakis E, Faloutsos C (2015) Fast efficient and scalable core consistency diagnostic for the parafac decomposition for big sparse tensors. In: Acoustics, Speech and Signal Processing (ICASSP), 2015. IEEE international conference on, IEEE, Brisbane, AustraliaGoogle Scholar
  17. Papalexakis E, Faloutsos C, Sidiropoulos N (2012) Parcube: sparse parallelizable tensor decompositions. Machine learning and knowledge discovery in databases, Bristol, UK, pp 521–536CrossRefGoogle Scholar
  18. Papalexakis EE, Pelechrinis K, Faloutsos C (2015) Location based social network analysis using tensors and signal processing tools. In: Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 I.E. 6th international workshop on, Cancun, Mexico pp 93–96Google Scholar
  19. Park R (1916) Suggestions for the investigations of human behavior in the urban environment. Am J Sociol 20(5):577–612CrossRefGoogle Scholar
  20. Sampson RJ, Morenoff JD, Gannon-Rowley T (2002) Assessing “neighborhood effects”: social processes and new directions in research. Ann Rev Sociol 28:443–478CrossRefGoogle Scholar
  21. Schmidt RO (1986) Multiple emitter location and signal parameter estimation. Antennas and Propag IEEE Trans 34(3):276–280CrossRefGoogle Scholar
  22. Shi C, Kong X, Yu PS, Xie S, Wu B (2012) Relevance search in heterogeneous networks. In: Proceedings of the 15th international conference on extending database technology, EDBT ‘12. ACM, New York, pp 180–191Google Scholar
  23. Sun Y, Han J (2013) Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor Newsl 14(2):20–28CrossRefGoogle Scholar
  24. Sun Y, Han J, Yan X, Yu PS, Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endow 4(11):992–1003Google Scholar
  25. Sun Y, Han J, Zhao P, Yin Z, Cheng H, Wu T (2009a) Rankclus: integrating clustering with ranking for heterogeneous information network analysis. In: Proceedings of the 12th international conference on extending database technology: advances in database technology, EDBT ‘09. ACM, New York, pp 565–576Google Scholar
  26. Sun Y, Yu Y, Han J (2009b) Ranking-based clustering of heterogeneous information networks with star network schema. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ‘09. ACM, New York, pp 797–806Google Scholar
  27. Symeonidis P, Papadimitriou A, Manolopoulos Y, Senkul P, Toroslu I (2011) Geo-social recommendations based on incremental tensor reduction and local path traversal. In: Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based social networks. ACM, Chicago, IL, pp 89–96Google Scholar
  28. Tensor data sets exemplifying problems in tensor modeling. http://www.models.life.ku.dk/nwaydata. Accessed 23 Aug 2016
  29. United nations-world urbanization prospects: the 2011 revision – highlights (2012) http://esa.un.org/unup. Accessed 21 May 2014
  30. Zhang K, Lin Y-R, Pelechrinis K (2016) EigenTransitions with hypothesis testing: the anatomy of urban mobility. In: Proceedings of the 10th international AAAI conference on weblogs and social media (ICWSM 2016), Cologne, GermanyGoogle Scholar
  31. Zhang F, Wilkie D, Zheng Y, Xie X (2013) Sensing the pulse of urban refueling behavior. In UbiComp 2013. ACM, Zurich, SwitzerlandGoogle Scholar
  32. Zhao Z, Cheng Z, Hong L, Chi EH (2015) Improving user topic interest profiles by behavior factorization. In: Proceedings of the 24th international conference on world wide web, pp 1406–1416. International World Wide Web Conferences Steering Committee, Florence, ItalyGoogle Scholar
  33. Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010) Collaborative filtering meets mobile recommendation: a user-centered approach. In: AAAI, Atlanta, GAGoogle Scholar
  34. Zheng Y, Capra L, Wolfson O, Yang H (2014a) Urban computing: concepts, methodologies, and applications. ACM transaction on intelligent systems and technologyGoogle Scholar
  35. Zheng Y, Liu T, Wang Y, Liu Y, Zhu Y (2014b) Diagnosing new york city’s noises with ubiquitous data. In UbiComp 2014. ACM, Seattle, WAGoogle Scholar

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© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information SciencesUniversity of PittsburghPittsburghUSA

Section editors and affiliations

  • Gao Cong
    • 1
  • Bee-Chung Chen
    • 2
  1. 1.School of Computer EngineeringNanyang Technological University (NTU)SingaporeSingapore
  2. 2.LinkedInSunnyvaleUSA