Finding community structure in spatially constrained complex networks

References

• Adamic, L.A., et al., 2000. Power-law distribution of the World Wide Web. Science, 287, 2115. doi:10.1126/science.287.5461.2115a
   Web of Science ®Google Scholar
• Bagler, G., 2008. Analysis of the airport network of India as a complex weighted network. Physica A: Statistical Mechanics and Its Applications, 387 (12), 2972–2980. doi:10.1016/j.physa.2008.01.077
   Web of Science ®Google Scholar
• Barabási, A.-L. and Oltvai, Z.N., 2004. Network biology: understanding the cell’s functional organization. Nature Reviews Genetics, 5 (2), 101–113. doi:10.1038/nrg1272
   PubMed Web of Science ®Google Scholar
• Barber, M.J., Fischer, M.M., and Scherngell, T., 2011. The community structure of research and development cooperation in Europe: evidence from a social network perspective. Geographical Analysis, 43 (4), 415–432. doi:10.1111/j.1538-4632.2011.00830.x
   Web of Science ®Google Scholar
• Barthélemy, M., 2011. Spatial networks. Physics Reports, 499 (1–3), 1–101. doi:10.1016/j.physrep.2010.11.002
   Web of Science ®Google Scholar
• Berlingerio, M., et al., 2013. Multidimensional networks: foundations of structural analysis. World Wide Web, 16 (5–6), 567–593. doi:10.1007/s11280-012-0190-4
   Web of Science ®Google Scholar
• Carr, A., 2011. Highlight CEO Paul Davison launches elastic network of a different color [online]. Available from: http://www.fastcompany.com/1825725/highlight-ceo-paul-davison-launches-elastic-network-different-color [Accessed 9 December 2014].
   Google Scholar
• Chen, C., 2003. Mapping scientific frontiers: the quest for knowledge visualization. London: Springer-Verlag.
   Google Scholar
• Chen, C., 2006. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57 (3), 359–377. doi:10.1002/asi.20317
   Web of Science ®Google Scholar
• Cho, E., Myers, S.A., and Leskovec, J., 2011. Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, 21–24 August, San Diego, CA. New York: ACM. 1082–1090. doi:10.1145/2020408.2020579
   Google Scholar
• Clauset, A., Newman, M.E.J., and Moore, C., 2004. Finding community structure in very large networks. Physical Review E, 70 (6), 066111. doi:10.1103/PhysRevE.70.066111
   PubMed Web of Science ®Google Scholar
• Crucitti, P., Latora, V., and Porta, S., 2006. Centrality measures in spatial networks of urban streets. Physical Review E, 73 (3), 036125. doi:10.1103/PhysRevE.73.036125
   Web of Science ®Google Scholar
• Expert, P., et al., 2011. Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Sciences, 108 (19), 7663–7668. doi:10.1073/pnas.1018962108
   PubMed Web of Science ®Google Scholar
• Flake, G.W., et al., 2002. Self-organization and identification of web communities. Computer, 35 (3), 66–70. doi:10.1109/2.989932
   Web of Science ®Google Scholar
• Gao, S., et al., 2013. Discovering spatial interaction communities from mobile phone data. Transactions in GIS, 17 (3), 463–481. doi:10.1111/tgis.12042
   Web of Science ®Google Scholar
• Girvan, M. and Newman, M.E.J., 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99 (12), 7821–7826. doi:10.1073/pnas.122653799
   PubMed Web of Science ®Google Scholar
• Guimera, R., et al., 2005. The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proceedings of the National Academy of Sciences, 102 (22), 7794–7799. doi:10.1073/pnas.0407994102
   PubMed Web of Science ®Google Scholar
• Guo, D., 2008. Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22 (7), 801–823. doi:10.1080/13658810701674970
   Web of Science ®Google Scholar
• Guo, D.S., 2009. Flow mapping and multivariate visualization of large spatial interaction data. IEEE Transactions on Visualization and Computer Graphics (TVCG: Proc. of InfoVis’09), 15 (6), 1041–1048. doi:10.1109/TVCG.2009.143
   PubMed Web of Science ®Google Scholar
• Jiang, B., 2007. A topological pattern of urban street networks: universality and peculiarity. Physica A: Statistical Mechanics and its Applications, 384 (2), 647–655. doi:10.1016/j.physa.2007.05.064
   Web of Science ®Google Scholar
• Jiang, B., 2009. Street hierarchies: a minority of streets account for a majority of traffic flow. International Journal of Geographical Information Science, 23 (8), 1033–1048. doi:10.1080/13658810802004648
   Web of Science ®Google Scholar
• Kernighan, B.W. and Lin, S., 1970. An efficient heuristic procedure for partitioning graphs. The Bell System Technical Journal, 49 (2), 291–307. doi:10.1002/j.1538-7305.1970.tb01770.x
   Google Scholar
• Lambiotte, R., et al., 2008. Geographical dispersal of mobile communication networks. Physica A: Statistical Mechanics and its Applications, 387 (21), 5317–5325. doi:10.1016/j.physa.2008.05.014
   Web of Science ®Google Scholar
• Liben-Nowell, D., et al., 2005. Geographic routing in social networks. Proceedings of the National Academy of Sciences, 102 (33), 11623–11628. doi:10.1073/pnas.0503018102
   PubMed Web of Science ®Google Scholar
• Liu, Y., et al., 2014. Analyzing relatedness by Toponym co-occurrences on web pages. Transactions in GIS, 18 (1), 89–107. doi:10.1111/tgis.12023
   Web of Science ®Google Scholar
• McCarthy, C., 2011. New photo app color hopes to see the future [online]. Available from: http://www.cnet.com/news/new-photo-app-color-hopes-to-see-the-future/ [Accessed 9 December 2014].
   Google Scholar
• Michalis, F., Petros, F., and Christos, F., 1999. On power-law relationships of the internet topology. ACM SIGCOMM Computer Communication Review, 29 (4), 251–262. doi:10.1145/316194.316229
   Google Scholar
• Newman, M.E.J., 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103 (23), 8577–8582. doi:10.1073/pnas.0601602103
   PubMed Web of Science ®Google Scholar
• Newman, M.E.J. and Girvan, M., 2004. Finding and evaluating community structure in networks. Physical Review E, 69 (2), 026113. doi:10.1103/PhysRevE.69.026113
   PubMed Web of Science ®Google Scholar
• Onnela, J.-P., et al., 2011. Geographic constraints on social network groups. PLoS ONE, 6 (4), e16939. doi:10.1371/journal.pone.0016939
   PubMed Web of Science ®Google Scholar
• Palla, G., et al., 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435 (7043), 814–818. doi:10.1038/nature03607
   PubMed Web of Science ®Google Scholar
• Pothen, A., Simon, H., and Liou, K., 1990. Partitioning sparse matrices with eigenvectors of graphs. SIAM Journal on Matrix Analysis and Applications, 11 (3), 430–452. doi:10.1137/0611030
   Web of Science ®Google Scholar
• Ravasz, E., et al., 2002. Hierarchical organization of modularity in metabolic networks. Science, 297 (5586), 1551–1555. doi:10.1126/science.1073374
   PubMed Web of Science ®Google Scholar
• Scellato, S., et al., 2010, Distance matters: geo-social metrics for online social networks. In: Proceedings of the 3rd conference on online social networks. Boston, MA: USENIX Association, 8.
   Google Scholar
• Scellato, S., et al., 2011, Socio-spatial properties of online location-based social networks. In: 5th International AAAI conference on weblogs and social media (ICWSM), 17–21 July, Barcelona. Menlo Park, CA: The AAAI Press, 329–336.
   Google Scholar
• Seary, A.J. and Richards, W.D., 2003. Spectral methods for analyzing and visualizing networks: an introduction. In: R. Breiger, K. Carley, and P. Pattison, eds. Dynamic social network modeling and analysis. Washington, DC: The National Academies Press, 209–228.
   Google Scholar
• Sen, P., et al., 2003. Small-world properties of the Indian railway network. Physical Review E, 67 (3), 036106. doi:10.1103/PhysRevE.67.036106
   PubMed Web of Science ®Google Scholar
• Shi, J. and Malik, J., 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), 888–905. doi:10.1109/34.868688
   Web of Science ®Google Scholar
• Strogatz, S.H., 2001. Exploring complex networks. Nature, 410 (6825), 268–276. doi:10.1038/35065725
   PubMed Web of Science ®Google Scholar
• Tian, Y., Hankins, A.R.A., and Patel, A.J.M., 2008, Efficient aggregation for graph summarization. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 9–12 June, Vancouver. New York: ACM, 567–580. doi:10.1145/1376616.1376675
   Google Scholar
• Tyler, J.R., Wilkinson, D.M., and Huberman, B.A., 2005. E-mail as spectroscopy: automated discovery of community structure within organizations. The Information Society, 21 (2), 143–153. doi:10.1080/01972240590925348
   Web of Science ®Google Scholar
• Wang, J., et al., 2011. Exploring the network structure and nodal centrality of China’s air transport network: a complex network approach. Journal of Transport Geography, 19 (4), 712–721. doi:10.1016/j.jtrangeo.2010.08.012
   Web of Science ®Google Scholar
• Watts, D.J. and Strogatz, S.H., 1998. Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442. doi:10.1038/30918
   PubMed Web of Science ®Google Scholar
• Wu, Z. and Leahy, R., 1993. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (11), 1101–1113. doi:10.1109/34.244673
   Web of Science ®Google Scholar
• Xu, X., et al., 2007, SCAN: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, 12–15 August, San Jose, CA. New York: ACM, 824–833. doi:10.1145/1281192.1281280
   Google Scholar
• Yang, Y., et al., 2012. Predicting links in multi-relational and heterogeneous networks. In: 2012 IEEE 12th international conference on data mining (ICDM), 10–13 December, Brussels. IEEE, 755–764. doi:10.1109/ICDM.2012.144
   Google Scholar
• Zhou, Y., Cheng, A.H., and Yu, A.J.X., 2009. Graph clustering based on structural/attribute similarities. Proceedings of the VLDB Endowment, 2 (1), 718–729. doi:10.14778/1687627.1687709
   Google Scholar