A space-time varying graph for modelling places and events in a network

References

• Aǹez, J., De La Barra, T., and Pérez, B., 1996. Dual graph representation of transport networks. Transportation Research Part B: Methodological. http://doi.org/10.1016/0191-2615(95)00024-0
   Web of Science ®Google Scholar
• Angles, R. and Gutierrez, C., 2017. An introduction to graph data management. Arxiv.Org, 1–43. doi:10.1007/978-1-4419-6045-0_1
   Google Scholar
• Baas, B., 2012. NoSQL spatial – neo4j versus PostGIS. Available from http://igitur-archive.library.uu.nl/student-theses/2012-0822-200532/UUindex.html
   Google Scholar
• Barabási, A.-L., 2009. Scale-free networks: a decade and beyond. Science, 325 (July), 11–12.
   PubMedGoogle Scholar
• Betsy, G. and Shashi, S., 2009. Time-aggregated graphs for modeling spatio-temporal networks. Encyclopedia of Database Systems, 3097–3098. doi:10.1007/978-0-387-39940-9_5053
   Google Scholar
• Blondel, V.D., et al., 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, 10. doi:10.1088/1742-5468/2008/10/P10008
   Google Scholar
• Brin, S. and Page, L., 1998. The anatomy of a large scale hypertextual web search engine. Computer Networks and ISDN Systems, 30 (1/7), 107–117. doi:10.1016/S0169-7552(98)00110-X
   Web of Science ®Google Scholar
• Chen, J., et al., 2001. A voronoi-based 9-intersection model for spatial relations. International Journal of Geographical Information Science, 15 (3), 201–220. doi:10.1080/13658810151072831
   Web of Science ®Google Scholar
• Cheung, A.K.L., O’sullivan, D., and Brierley, G., 2015. Graph-assisted landscape monitoring. International Journal of Geographical Information Science, 29 (4), 580–605. doi:10.1080/13658816.2014.989856
   Web of Science ®Google Scholar
• Ciglan, M., Averbuch, A., and Hluchy, L., 2012. Benchmarking traversal operations over graph databases. In Proceedings - 2012 IEEE 28th International Conference on Data Engineering Workshops, ICDEW 2012 186–189. doi:10.1109/ICDEW.2012.47
   Google Scholar
• Costes, B., Perret, J., Bucher, B., & Gribaudi, M. (2015). An aggregated graph to qualify historical spatial networks using temporal patterns detection. In 18th AGILE International Conference on Geographic Information Science. Lisbon, Portugal: AGILE.
   Google Scholar
• Couclelis, H., 1999. Space, time, geography. Geographical Information Systems, 29–38. Available from: http://www.geos.ed.ac.uk/~gisteac/gis_book_abridged/files/ch02.pdf
   Google Scholar
• Curtin, K.M., 2007. Network analysis in geographic information science: review, assessment, and projections. Cartography and Geographic Information Science, 34 (2), 103–111. doi:10.1559/152304007781002163
   Google Scholar
• De Virgilio, R., Maccioni, A., and Torlone, R. (2014). Model-driven design of graph databases. LNCS 8824.
   Google Scholar
• Mondo, G. Del, Rodríguez, M. A., Claramunt, C., Bravo, L., & Thibaud, R. (2013). Modeling consistency of spatio-temporal graphs. Data & Knowledge Engineering, 84, 59–80. http://doi.org/10.1016/j.datak.2012.12.007
   Google Scholar
• Egenhofer, M.J., 1993. A model for detailed binary topological relationships. Geomatica, 47 (414), 261–273.
   Google Scholar
• Ivarsson, T., et al., 2014. The neo4j manual v1. Vol. 3. (A. Nawroth, Ed.). USA: Neo-Technology. Available from: http://dist.neo4j.org/neo4j-manual-1.4.M03.pdf
   Google Scholar
• Khurana, U. and Deshpande, A., 2016. Storing and analyzing historical graph data at scale. Edbt, 65–76. Available from: http://arxiv.org/abs/1509.08960
   Google Scholar
• Koloniari, G., Souravlias, D., and Pitoura, E., 2013. On Graph Deltas for Historical Queries. In Proceedings of 1st Workshop on Online Social Systems (WOSS), in conjunction with VLDB (pp. 0–5). Istanbul, Turkey: WOSS.
   Google Scholar
• Lahiri, M. and Berger-Wolf, T.Y., 2008. Mining periodic behavior in dynamic social networks. 2008 Eighth IEEE International Conference on Data Mining, 373–382. doi:10.1109/ICDM.2008.104
   Google Scholar
• Lee, J., Oh, J., and Hwang, S. (2005). STRG-Index: Spatio-Temporal Region Graph Indexing for Large Video Databases. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data (pp. 718–729). Baltimore, Maryland: ACM DL.
   Google Scholar
• Miler, M., Medak, D., and Odobašić, D., 2014. A shortest path algorithm performance comparison in graph and relational database on a transportation network. PROMET - Traffic&Transportation, 26, 1. doi:10.7307/ptt.v26i1.1268
   Web of Science ®Google Scholar
• Moons, E., Brijs, T., and Wets, G., 2009. Improving moran ’ s index to identify hot spots in traffic safety. Geocomputation and Urban Planning, 117–132. doi:10.1007/978-3-540-89930-3_7
   Google Scholar
• Ren, C., Lo, E., Kao, B., Zhu, X., & Cheng, R. (2011). On Querying Historical Evolving Graph Sequences. In Proceedings of the VLDB Endowment (Vol. 4, pp. 726–737). Seattle, WA.
   Google Scholar
• Rocha, L.E.C., 2017. Dynamics of air transport networks: a review from a complex systems perspective. Chinese Journal of Aeronautics, 30 (2), 469–478. doi:10.1016/j.cja.2016.12.029
   Web of Science ®Google Scholar
• Rossi, R.A., et al. 2013. Modeling dynamic behavior in large evolving graphs. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining - WSDM ’13, 667. doi:10.1145/2433396.2433479
   Google Scholar
• Sadahiro, Y., 2001. Exploratory method for analyzing changes in polygon distributions. Environment and Planning B: Planning and Design, 28, 595–609. doi:10.1068/b2752
   Google Scholar
• Shekhar, S. and Oliver, D. (2010). Computational modeling of spatio-temporal social networks: a time-aggregated graph approach. In Specialist Meeting-Spatio-Temporal Constraints on Social Networks. Available from http://www.cs.umn.edu/~shekhar
   Google Scholar
• Stell, J., et al. 2011. Spatio-temporal evolution as bigraph dynamics. In Conference on Spatial Information Theory (COSIT) Lecture Notes in Computer Science. Maine USA.
   Google Scholar
• Strogatz, S., 2001. Exploring complex networks. Nature, 410 (6825), 268–276. doi:10.1038/35065725
   PubMed Web of Science ®Google Scholar
• Thibaud, R., et al., 2013. A Spatio-temporal graph model for marine dune dynamics analysis and representation. Transactions in GIS, 17 (5), 742–762. doi:10.1111/tgis.12006
   Web of Science ®Google Scholar
• Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y., and Wilkins, D. (2010). A Comparison of a Graph Database and a Relational Database: A Data Provenance Perspective. In Proceedings of the 48th annual Southeast regional conference (pp. 42–47). Oxford, Mississippi: ACM DL. http://doi.org/10.1145/1900008.1900067
   Google Scholar
• Von Landesberger, T., et al., 2015. Mobility graphs: visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Transactions on Visualization, Available from: http://apps.isiknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=10&SID=Z1pIMugVKe2yDt2Ciq9&page=1&doc=4
   Google Scholar
• Zaki, A. and Attia, M., 2016. Comprehensive Survey on Dynamic Graph Models. International Journal of Advanced Computer Science and Applications, 7 (2), 573–582. doi:10.14569/IJACSA.2016.070273
   Web of Science ®Google Scholar