Community detection in social network is a significant issue in the study of the structure of a network and understanding its characteristics. A community is a significant structure formed by nodes with more connections between them. In recent years, several algorithms have been presented for community detection in social networks among them label propagation algorithm is one of the fastest algorithms, but due to the randomness of the algorithm its performance is not suitable. In this paper, we propose an improved label propagation algorithm called memory-based label propagation algorithm (MLPA) for finding community structure in social networks. In the proposed algorithm, a simple memory element is designed for each node of graph and this element store the most frequent common adoption of labels iteratively. Our experiments on the standard social network datasets show a relative improvement in comparison with other community detection algorithms.IndexTerms-label propagation algorithm, community detection, social networks, complex networks.

In social network analysis, community detection is one of the significant tasks to study the structure and characteristics of the networks. In recent years, several intelligent and meta‐heuristic algorithms have been presented for community detection in complex social networks, among them label propagation algorithm (LPA) is one of the fastest algorithms for discovering community structures. However, due to the randomness of the LPA, its performance is not suitable for the general purpose of network analysis. In this study, the authors propose an improved version of the label propagation (called AntLP) algorithm using similarity indices and ant colony optimisation (ACO). The AntLP consists of two steps: in the first step, the algorithm assigns weights for edges of the input network using several similarity indices, and in the second step, the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network. In order to study the performance of the AntLP, several experiments are conducted on some well‐known social network datasets. Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity, normalised mutual information and running time.

Recently, electro-assisted extraction of ionic drugs from biological fluids through a supported liquid membrane and into an aqueous acceptor solution was introduced as a new sample preparation technique and has been termed electromembrane extraction (EME). In the present work, this microextraction technique combined with high-performance liquid chromatography and ultraviolet detection has been developed for detection of phenazopyridine (PP) as a local analgesic drug in human plasma and urine samples. From a 6.5 mL neutral aqueous sample, PP was extracted for 20 min through a thin supported liquid membrane of 2-nitrophenyl octyl ether sustained in the pores of the wall of a porous hollow fiber and into an aqueous acidic acceptor solution (25 L, containing negative electrode) by application of a DC electrical potential. The effects of several factors, including the nature of organic solvent, HCl concentration in donor and acceptor solutions, stirring speed, extraction time, and applied voltage on the extraction efficiency of the drug, were investigated and optimized. Satisfactory linearity ranges with correlation coefficients higher than 0.996 in different extraction media, admissible limits of detection (0.5 and 1.0 ng mL −1 in urine and plasma samples, respectively) and good repeatability and reproducibility (intra-and inter-assay precisions ranged between 3.7%-6.8% and 8.8%-12.5%, respectively) were obtained. The optimized EME procedure was applied to determine the concentration of PP in various matrices, such as plasma and urine samples, and satisfactory results were obtained.Résumé : Une nouvelle technique de préparation d'échantillons à partir de liquides biologiques appelée « extraction électromem-branaire » a récemment fait son apparition. Cette technique consiste à extraire des échantillons par voie électrochimique des médicaments ioniques dans une solution d'extraction aqueuse à travers une membrane liquide sur support. Dans le cadre des présents travaux, nous avons mis au point cette technique de microextraction en association avec la chromatographie liquide à haute performance à détection ultraviolette en vue d'effectuer la détection de la phénazopyridine, un analgésique local, dans des échantil-lons d'urine et de plasma humains. Nous avons utilisé un échantillon aqueux neutre de 6,5 mL pour en extraire la phénazopyridine pendant 20 min à travers une fine membrane liquide de 2-nitrophényloxyoctane supportée par incrustation dans les pores de la paroi d'une fibre poreuse creuse. Les échantillons ont été extraits dans une solution aqueuse acide (25 L, contenant une anode) en appliquant une différence de potentiel pour fournir un courant électrique continu. Nous avons étudié les effets de plusieurs facteurs, notamment la nature du solvant organique, la concentration d'acide chlorhydrique dans les échantillons et les solutions d'extraction, la vitesse d'agitation, le temps d'extraction et le potentiel appliqué, sur le rendement de l'extraction du médicament et avons optimisé ces paramètres. Nous avons obten...