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The field of identifying natural partitions in complex networks has witnessed enormous attention in recent years. However, the mathematical definition to unfold accurate structure for communities is still in need of more investigation. Further, the literature lacks any driving strategy to claim which mathematical definition is to be honored. Our contribution in this paper is threefold. First, we introduce a new score model for the community detection problem. Unlike other state-of-theart models, the proposed model aims to reflect the atom relationships of the nodes rather than that of the communities. The proposed model includes two new contradictory objective functions. The first objective is ruled in favor of tightly grouping the neighbours for each node within one community. The second objective, on the other hand, attempts to disturb the maximum number of external interactions for each node with respect to the node’s internal neighbours. The second contribution comes with designing a new algorithm, a multi-objective evolutionary algorithm based community detection in complex networks (MOEA-CD). The contradictory objectives composing the proposed score model are optimized simultaneously in the MOEA-CD. We also introduce a new local heuristic search operator, namely, Neighbour Node Centrality (NNC) strategy. The strategy is used in the MOEA-CD algorithm to improve its performance. The third contribution comes with proposing a new evaluation technique to validate the quality of the existing and the proposed score functions. The proposed technique is based on a random node migration strategy applied on the ground-truth partition for the network. Empirical evaluation of the proposed model against the state-of-the-art models clarifies that the proposed model is generally more correlated to represent the Normalised Mutual Information (NMI) between a candidate partition and the correct partition. Further, extensive experiments on both real-world and synthetic benchmarks of different complexities demonstrate that the proposed model can reveal more accurate community structures in comparison to the state-of-the-art models. The results also demonstrate the ability of the proposed (NNC) strategy to improve the detection accuracy of all tested models.

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In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather than the whole conductance as reported in the literature. Anti-Hebbian and Hebbian (AHaH) learning rules are used to mimic the changes in memristance of the memristors. This research will concentrate on the effect of conductance on an individual MSS to simulate the nanotechnology devices of the memristors. A single synapse is presented by a couple of memristors to mimic its resistance switching. The learning circuit of artificial synapses could be used in many applications, such as image processing and neural networks, for pattern classification of synapses, represented by a map of the memeristors. These synapses are essential elements for data processing and information storage in both real and artificial neural systems.