The proposed algorithm in this research is based on the multi-agent particle swarm optimization as a collective intelligence due to the connection between several simple components which enables them to regulate their behavior and relationships with the rest of the group according to certain rules. As a result, self-organizing in collective activities can be seen. Community structure is crucial for many network systems, the algorithm uses a special type of coding to identify the number of communities without any prior knowledge. In this method, the modularity function is used as a fitness function to optimize particle swarm. Several experiments show that the proposed algorithm which is called Multi Agent Particle Swarm is superior compared with other algorithms. This algorithm is capable of detecting nodes in Overlapping communities with high accuracy. The point in using the previously presented PSO algorithms for community detection is that they recognize non-Overlapping communities, and this goes back to the representation of genes by these methods, but the use of multi-agent collective intelligence by our algorithm has led to the identification of nodes in Overlapping communities. The results show that the nodes that are shared between a set of agents, these nodes are active nodes that create an overlap in the communities. Our experimental results show that when a member node is more than one community, this node is a good candidate to be selected as the active node, which has led to the creation of Overlapping networks.

In network analysis, the community is considered as a group of nodes that is densely connected with respect to the rest of the network. Detecting the community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There are various approaches in literature for community, Overlapping or disjoint, detection in networks. In recent years, many researchers have concentrated on feature learning and network embedding methods for nodes clustering. These methods map the network into a lower-dimensional representation space. In this paper, we propose a model for learning graph representation using deep neural networks. In this method, a nonlinear embedding of the original graph is fed to stacked auto-encoders for learning the model. Then an Overlapping clustering algorithm is employed to extract Overlapping communities. The effectiveness of the proposed model is investigated by conducting experiments on standard benchmarks and real-world datasets of varying sizes. Empirical results exhibit that the presented method outperforms some popular community detection methods.

Detecting communities plays a vital role in studying group level patterns of a social network and it can be helpful in developing several recommendation systems such as movie recommendation, book recommendation, friend recommendation and so on. Most of the community detection algorithms can detect disjoint communities only, but in the real time scenario, a node can be a member of more than one community at the same time, that leads to Overlapping communities. A novel approach is proposed to detect such Overlapping communities by extending the definition of newman’ s modularity for Overlapping communities. The proposed algorithm is tested on LFR benchmark networks with Overlapping communities and on real-world networks. The performance of the algorithm is evaluated using popular metrics such as ONMI, Omega Index, F-score and Overlap modularity and the results are compared with its competent algorithms. It is observed that extended modularity gain can detect highly modular structures in complex networks with Overlapping communities.

Social networks are not only tools for communication but also represent one of the key potentials in business and commerce. One of the most significant issues in this field is clustering nodes and extracting effective and useful patterns from them, known as community detection. A major challenge in community detection within social networks is the vast number of nodes, which makes any kind of analysis difficult. Another challenge is the overlap of cluster members, referred to as Overlapping communities. In such networks, each node may belong to multiple groups. Considering overlaps between communities—especially in large-scale networks—poses significant challenges in accurately detecting and identifying communities. Therefore, many studies tend to overlook this issue. In this paper, an approach is proposed to address these challenges. The most time-consuming step in the proposed algorithm, identifying influential nodes, is performed in parallel. Moreover, overlaps between communities are taken into account and analyzed. The results of evaluating the proposed method, in comparison with other existing methods, indicate its superiority in terms of the uniformity of the detected communities.

Many Scholars believe that “, Imagined communities”,is one of a most influential Books in Late 20th. Based on the Anderson’, s Point of view, Nationalism is not a false capitalist consciousness, instead it is produced by one of a fundamental aspects of capitalism. “, Print Capitalism”,in its historical context, is a type of economic corporation which is not only affected on the formation and distribution of culture, but it also was a part of capitalist production as well. His contribution was to show how print industry helped development of national units. A critical review can declare Anderson’, s analysis is less practical and useful. It could not explain how a nation is distinguished from other types of community which he has constructed in the book. In addition, his analysis faces three serious problems: his understanding of nation is something classless, asocial and universal,minorities, marginal and subaltern groups are absent in his analysis, and finally he is unconsidered to sexuality structures and women’, s reproductive role in generating a nation. These obstacles base his construction of citizenship and nation upon white, middle class, educated European men.

Relationship of civil servants and political authorities and sphere of powers of both in administration and policy-making is one of main issues of the contemporary governance. There is no doubt that politics is not administration and a politician is different with a public servant. Therefore, it seems there is no problem to distinguish politics from administration. Nevertheless, specification of exact boundaries of the two spheres and its cause is not clear. This dichotomy is not founded on a priori logic; but it is a fact in the most of modern states. Therefore, in view of conceptual, institutional and individual actors, there is a separation as well as Overlapping between politics and administration that being debated in the article with a descriptive method. According to author, norms specified at a democratic constitution and emphasis it gives to values such as justice, democracy, responsibility, autonomy and morality provides sufficient evidences for keeping this demarcation and simultaneously makes an interaction between the two spheres. Although the way of demarcating and specification of points of distinction, Overlapping and interaction between the two spheres of 'governing and service' depends on characteristics of constitutionalism and administrative tradition of states.