Search Results

1 - 6 of 6 items :

  • "local dependency" x
Clear All

information flow model for conflict and fission in small groups, Journal of Anthropological Research 33(4): 452-473. Zehnalova, S., Horak, Z., Kudelka, M. and Snael, V. (2013). Local dependency in networks, 5th International Conference on Intelligent Networking and Collaborative Systems (INCoS), Xi’an, China, pp. 250-254. Zhang, B. and Horvath, S. (2005). A general framework for weighted gene co-expression network analysis, Statistical Applications in Genetics and Molecular Biology 4(1): 1128. Zhu, Y., Ye, S. and Li, X. (2005). Distributed PageRank computation based on


According to economic geography literature, the success of firms is affected by the local context, in particular when firms are socio-spatially embedded. We expect this effect to be stronger when firms face an increase in local disorder. We analysed data on 344 firms (active in retail, eating and drinking establishments, personal services and private education, business services, cultural activities, manufacturing and building) in 108 Dutch residential neighbourhoods, and data on the changes in social and physical disorder of those neighbourhoods, to examine firm success determinants. We find that it is not the degree of disorder that matters to local firms turnover, but rather recent changes in local disorder. More in particular, we find that local firm turnover is negatively affected by an increase in local disorder, but only when a firm depends on daily visits from predominantly local customers. Our results suggest that physical and social local interventions to create safe and clean public spaces will indirectly positively influence local firms and subsequently, the neighbourhood economy. This spill-over effect is promising for both residents, who benefit from local amenities and local ‘buzz’, and local entrepreneurs, whose firm success is stimulated.

Mathematics and Computer Science 26(2): 297-308, DOI: 10.1515/amcs-2016-0021. Kudělka, M., Zehnalová, S., Horák, Z., Krömer, P. and Snášel, V. (2015). Local dependency in networks, International Journal of Applied Mathematics and Computer Science 25(2): 281-293, DOI: 10.1515/amcs-2015-0022. Li, F. (2012). Some results on tenacity of graphs, WEAS Transactions on Mathematics 11(9): 760-772. Li, F., Ye, Q. and Sheng, B. (2012). Computing rupture degrees of some graphs, WEAS Transactions on Mathematics 11(1): 23-33. Mader, W. (1967). Homomorphieegenshaften und mittlere

, ACM Transactions on Knowledge Discovery Data 10 (3): 28:1–28:43. Kudělka, M., Zehnalová, Š., Horák, Z., Krömer, P. and Snášel, V. (2015). Local dependency in networks, International Journal of Applied Mathematics and Computer Science 25 (2): 281–293, DOI: 10.1515/amcs-2015-0022. Lee, C.-Y. (2006). Correlations among centrality measures in complex networks, arXiv: 0605220. Moriano, P. and Finke, J. (2012). Power-law weighted networks from local attachments, Europhysics Letters 99 (1): 18002. Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C. and

personal/impersonal views in supervised and semi-supervised sentiment classification, Proc. 48th Annual Meeting of the Association for Computational Linguistics , 2010, pp. 414–423. ⇒ 66 [24] F. Li, M. Huang, X. Zhu, Sentiment analysis with global topics and local dependency, Association for the Advancement of Artificial Intelligence 10 (2010) 1371–1376. ⇒ 66 [25] C. Lin, Y. H. Lee, Joint sentiment/topic model for sentiment analysis, Proc. 18th ACM Conference on Information and Knowledge Management , 2009, pp. 375–384. ⇒ 66 [26] B. Liu, E. Blasch, Y. Chen, D. Shen, G

sense that it considers frequencies, by which activity relationships occur (Aalst, Weijters & Medeiros, 2003) in the event log. In addition to robustness of an event log, HeuristicsMiner is also capable of dealing with short loops and non-local dependencies. Broucke & Weerdt (2017) introduced the discovery technique Fodina that is based on HeuristicsMiner and which handles the noise in the log and discover duplicate activities. Flexible Heuristics Miner ( Weijters & Ribeiro, 2011 ) is yet another discovery technique based on HeristicsMiner. Similarly to previous