Functioning of the Clothing Networks on the Global Markets - Comparative Analysis

In the clothing sector the partnership networks are being created between the economic subject, because such actions are aimed at minimising the risk, as well as to reducing the production and distribution costs. The most often encountered networks in the textile-clothing branch are the franchising networks. The present article concentrates on the competitiveness aspect of the global clothing networks. A comparative analysis of the action of the commercial clothing networks was made, in order to show some features of its operation and proceeding, while focusing on the specified elements of the marketing-mix strategy. The obtained results allowed to show the differences and similarities in the used marketing strategies.W niniejszym artykule skoncentrowano się na aspekcie konkurencyjności globalnych sieci odzieżowych. Dokonano analizy porównawczej działania odzieżowych sieci handlowych, w celu ukazania pewnych cech jego działalności i postępowania skupiając się na określonych elementach strategii marketing-mix. Uzyskane wyniki pozwoliły na pokazanie róznic i podobieństw w stosowanych strategiach marketingowych

Sequential Changepoint Approach for Online Community Detection

We present new algorithms for detecting the emergence of a community in large networks from sequential observations. The networks are modeled using Erdos-Renyi random graphs with edges forming between nodes in the community with higher probability. Based on statistical changepoint detection methodology, we develop three algorithms: the Exhaustive Search (ES), the mixture, and the Hierarchical Mixture (H-Mix) methods. Performance of these methods is evaluated by the average run length (ARL), which captures the frequency of false alarms, and the detection delay. Numerical comparisons show that the ES method performs the best; however, it is exponentially complex. The mixture method is polynomially complex by exploiting the fact that the size of the community is typically small in a large network. However, it may react to a group of active edges that do not form a community. This issue is resolved by the H-Mix method, which is based on a dendrogram decomposition of the network. We present an asymptotic analytical expression for ARL of the mixture method when the threshold is large. Numerical simulation verifies that our approximation is accurate even in the non-asymptotic regime. Hence, it can be used to determine a desired threshold efficiently. Finally, numerical examples show that the mixture and the H-Mix methods can both detect a community quickly with a lower complexity than the ES method.Comment: Submitted to 2014 INFORMS Workshop on Data Mining and Analytics and an IEEE journa