P1: qVa Trim: 6.125in×9.25in Top: 0.5in Gutter: 0.75in
CUUS2079-BIB CUUS2079-Zafarani 978 1 107 01885 3 January 13, 2014 15:59
Bibliography 303Easley, D., and Kleinberg, J.M. 2010.Networks, crowds, and markets. Cambridge Uni-
vesity Press.
Eberhart, R.C., Shi, Y., and Kennedy, J. 2001.Swarm intelligence. Morgan Kaufmann.
Edmonds, J., and Karp, R.M. 1972. Theoretical improvements in algorithmic efficiency
for network flow problems.Journal of the ACM (JACM), 19 (2).
Ellison, Nicole B., et al. 2007. Social network sites: definition, history, and scholarship.
Journal of Computer-Mediated Communication, 13 (1), 210–230.
Engelbrecht, A.P. 2005. Fundamentals of computational swarm intelligence.Recherche,
67 (2).
Erdos, P., and R ̋ ́enyi, A. 1960.On the evolution of random graphs. Akademie Kiado. ́
Erdos, P., and R ̋ enyi, A. 1961. On the strength of connectedness of a random graph. ́
Acta Mathematica Hungarica, 12 (1).
Erdos, P., and R ̋ ́enyi, A. 1959. On random graphs.Publicationes Mathematicae Debre-
cen, 6 , 290–297.
Ester, M., Kriegel, H.P., Sander, J., and Xu, X. 1996. A density-based algorithm for
discovering clusters in large spatial databases with noise.Proceedings of the second
international conference on Knowledge Discovery and Data Mining, AAAI Press,
226–231.
Faloutsos, M., Faloutsos, P., and Faloutsos, C. 1999. On power-law relationships of the
internet topology. In:ACM SIGCOMM Computer Communication Review, 29.
Fisher, D. 1987. Improving inference through conceptual clustering.Proceedings of the
1987 AAAI conference, 461–465.
Floyd, R.W. 1962. Algorithm 97: shortest path.Communications of the ACM, 5 (6).
Ford, L.R., and Fulkerson, D.R. 1956. Maximal flow through a network.Canadian
Journal of Mathematics, 8 (3), 399–404.
Fortunato, S. 2009. Community detection in graphs.Physics Reports, 486 (3–5).
Friedman, J., Hastie, T., and Tibshirani, R. 2009.The elements of statistical learning.
Vol. 1. Springer Series in Statistics.
Gale, D. 1996. What have we learned from social learning?European Economic Review,
40 (3).
Gao, Huiji, Wang, Xufei, Barbier, Geoffrey, and Liu, Huan. 2011a. Promoting coordi-
nation for disaster relief – from crowdsourcing to coordination. In:Social computing,
behavioral-cultural modeling and prediction. Springer, pp. 197–204.
Gao, H., Barbier, G., and Goolsby, R. 2011b. Harnessing the Crowdsourcing Power of
Social Media for Disaster Relief.Intelligent Systems, IEEE, 26 (3), 10–14.
Gao, H., Tang, J., and Liu, H. 2012a. Exploring Social-Historical Ties on Location-Based
Social Networks. In:Proceedings of the sixth international conference on Weblogs
and Social Media.
Gao, H., Tang, J., and Liu, H. 2012b. Mobile Location Prediction in Spatio-Temporal
Context.Nokia Mobile Data Challenge Workshop.
Gao, Huiji, Tang, Jiliang, and Liu, Huan. 2012c. gSCorr: modeling geo-social correla-
tions for new check-ins on location-based social networks. In:Proceedings of the 21st
ACM international conference on Information and Knowledge Management.ACM,
pp. 1582–1586.
Gibson, D., Kumar, R., and Tomkins, A. 2005. Discovering large dense subgraphs in
massive graphs. In:Proceedings of the 31st international conference on Very Large
Data Bases. VLDB Endowment.