Community Structure Characterization

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry

Genetic Diversity and Antimicrobial Resistance of Escherichia coli from Human and Animal Sources Uncovers Multiple Resistances from Human Sources

Escherichia coli are widely used as indicators of fecal contamination, and in some cases to identify host sources of fecal contamination in surface water. Prevalence, genetic diversity and antimicrobial susceptibility were determined for 600 generic E. coli isolates obtained from surface water and sediment from creeks and channels along the middle Santa Ana River (MSAR) watershed of southern California, USA, after a 12 month study. Evaluation of E. coli populations along the creeks and channels showed that E. coli were more prevalent in sediment compared to surface water. E. coli populations were not significantly different (P = 0.05) between urban runoff sources and agricultural sources, however, E. coli genotypes determined by pulsed-field gel electrophoresis (PFGE) were less diverse in the agricultural sources than in urban runoff sources. PFGE also showed that E. coli populations in surface water were more diverse than in the sediment, suggesting isolates in sediment may be dominated by clonal populations.Twenty four percent (144 isolates) of the 600 isolates exhibited resistance to more than one antimicrobial agent. Most multiple resistances were associated with inputs from urban runoff and involved the antimicrobials rifampicin, tetracycline, and erythromycin. The occurrence of a greater number of E. coli with multiple antibiotic resistances from urban runoff sources than agricultural sources in this watershed provides useful evidence in planning strategies for water quality management and public health protection

On the evaluation of community detection algorithms on heterogeneous social media data

One fundamental problem in social networks is the identification of groups of elements (also known as communities) when group membership is not explicitly available. Community detection has proven to be valuable in diverse domains such as biology, social sciences and bibliometrics. Thus, several community detection techniques have been developed. Nonetheless, as real networks are very heterogenous, the question of how communities should be assessed remains open. Whilst there are several works that have analysed the performance of diverse community detection algorithms over artificial graph benchmarks, the evaluation over real social networks has received comparatively less attention. Motivated by the lack of such studies, this chapter focuses on the analysis of the performance of community detection algorithms over social media networks, and the quantification of the structural properties of the discovered communities.Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Relative Validity Criteria for Community Mining Algorithms

Alberta Innovates Centre for Machine LearningNSERCFapespCNP

L. Symons, Agricultural geography

Béguin Hubert. L. Symons, Agricultural geography. In: Tiers-Monde, tome 11, n°41, 1970. Education et développement. Etudes sur la formation, l'enseignement et la planification des ressources humaines. pp. 225-226