COMMUNITY DETECTION IN COMPLEX NETWORKS AND APPLICATION TO DENSE WIRELESS SENSOR NETWORKS LOCALIZATION

Complex network analysis is applied in numerous researches. Features and characteristics of complex networks provide information associated with a network feature called community structure. Naturally, nodes with similar attributes will be more likely to form a community. Community detection is described as the process by which complex network data are analyzed to uncover organizational properties, and structure; and ultimately to enable extraction of useful information. Analysis of Wireless Sensor Networks (WSN) is considered as one of the most important categories of network analysis due to their enormous and emerging applications. Most WSN applications are location-aware, which entails precise localization of the deployed sensor nodes. However, localization of sensor nodes in very dense network is a challenging task. Among various challenges associated with localization of dense WSNs, anchor node selection is shown as a prominent open problem. Optimum anchor selection impacts overall sensor node localization in terms of accuracy and consumed energy. In this thesis, various approaches are developed to address both overlapping and non-overlapping community detection. The proposed approaches target small-size to very large-size networks in near linear time, which is important for very large, densely-connected networks. Performance of the proposed techniques are evaluated over real-world data-sets with up to 106 nodes and syntactic networks via Newman\u27s Modularity and Normalized Mutual Information (NMI). Moreover, the proposed community detection approaches are extended to develop a novel criterion for range-free anchor selection in WSNs. Our approach uses novel objective functions based on nodes\u27 community memberships to reveal a set of anchors among all available permutations of anchors-selection sets. The performance---the mean and variance of the localization error---of the proposed approach is evaluated for a variety of node deployment scenarios and compared with random anchor selection and the full-ranging approach. In order to study the effectiveness of our algorithm, the performance is evaluated over several simulations that randomly generate network configurations. By incorporating our proposed criteria, the accuracy of the position estimate is improved significantly relative to random anchor selection localization methods. Simulation results show that the proposed technique significantly improves both the accuracy and the precision of the location estimation

Synthesis and Antioxidant Activities of [5-fluoro N, N'-bis (salicylidene) ethylenediamine] and [3, 5-fluoro N, N'-bis (salicylidene) ethylenediamine] Manganese (III) Complexes

ABSTRACT: Antioxidants act as free radical scavengers in the oxidation processes. Thus, they will certainly play diverse roles in the biological systems and the therapy of a wide variety of diseases. Regarding this fact, in the present study, we synthesized two new salen ligand compounds by the condensation of ethylendiamine and salicylaldehyde derivatives in excellent yields. The structures of these ligands were confirmed by IR, 1 H NMR and mass spectroscopy techniques. Furthermore, we evaluated the relative dismutase, catalase and peroxidase activities of the newly synthesized complexes named as EUKs 131, 132, 141 and 142 relative to EUKs 108 and 8, as the reference compounds. The results demonstrated that all Mn-salen complexes (EUKs) illustrated significant dismutase, catalase and peroxidase activities. EUKs 131 and 8 showed the most catalase and peroxidase activities while their dismutase activities were almost the same as the other compounds. In addition, our data indicated that the biological activities of the EUKs are modulated by manganese element as well as the types and the positions of substituents on the ligand

Modularity maximization using completely positive programming

Community detection is one of the most prominent problems of social network analysis. In this paper, a novel method for Modularity Maximization (MM) for community detection is presented which exploits the Alternating Direction Augmented Lagrangian (ADAL) method for maximizing a generalized form of Newman’s modularity function. We first transform Newman’s modularity function into a quadratic program and then use Completely Positive Programming (CPP) to map the quadratic program to a linear program, which provides the globally optimal maximum modularity partition. In order to solve the proposed CPP problem, a closed form solution using the ADAL merged with a rank minimization approach is proposed. The performance of the proposed method is evaluated on several real-world data sets used for benchmarks community detection. Simulation results shows the proposed technique provides outstanding results in terms of modularity value for crisp partitions

Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization

Soft overlapping clustering is one of the notable problems of community detection. Extensive research has been conducted to develop efficient methods for nonoverlapping and crisp-overlapping community detection in large-scale networks. In this article, fast fuzzy modularity maximization (FFMM) for soft overlapping community detection is proposed. FFMM exploits novel iterative equations to calculate the modularity gain associated with changing the fuzzy membership values of network vertices. The simplicity of the proposed scheme enables efficient modifications, reducing computational complexity to a linear function of the network size, and the number of communities. Moreover, to further reduce the complexity of FFMM for very large networks, multicycle FFMM (McFFMM) is proposed. The proposed McFFMM reduces complexity by breaking networks into multiple subnetworks and applying FFMM to detect their communities. Performance of the proposed techniques is demonstrated with real-world data and the Lancichinetti-Fortunato-Radicchi benchmark networks. Moreover, the performance of the proposed techniques is evaluated versus some state-of-the-art soft overlapping community detection approaches. Results show that the McFFMM produces a remarkable performance in terms of overlapping modularity with fuzzy memberships, computational time, number of detected overlapping nodes, and overlapping normalized mutual information

Linear Time Community Detection by a Novel Modularity Gain Acceleration in Label Propagation

IEEE Community detection is an important problem in complex network analysis. Among numerous approaches for community detection, label propagation (LP) has attracted a lot of attention. LP selects the optimum community (i.e., label) of a network vertex by optimizing an objective function (e.g., Newman\u27s modularity) subject to the available labels in the vicinity of the vertex. In this paper, a novel analysis of Newman\u27s modularity gain with respect to label transitions in graphs is presented. Here, we propose a new form of Newman\u27s modularity gain calculation that quantifies available label transitions for any LP based community detection. The proposed approach is called Modularity Gain Acceleration (MGA) and is simplified and divided into two components, the local and global sum-weights. The Local Sum-Weight (LSW) is the component with lower complexity and is calculated for each candidate label transition. The General Sum-Weight (GSW) is more computationally complex, and is calculated only once per each label. GSW is updated by leveraging a simple process for each node-label transition, instead of for all available labels. The MGA approach leads to significant efficiency improvements by reducing time consumption up to 85% relative to the original algorithms with the exact same quality in terms of modularity value which is highly valuable in analyses of big data sets