A social network is a social structure made up of a set nodes, which represents social actors (such as people, organizations), and edges or lines represents relationship between these nodes or actors. Social networks have important roles in the dispersal of information and innovation, the analysis of such networks, attracted much attention in the research area. The analysis of social network can be done as a whole, which means the representations of all of its actors and identification of structures, present in that social network, that lead to the presence of communities. In the method of community detection, the main aim is to partition the network into dense regions of the graph, and those dense regions typically correspond to entities which are closely related, and can hence be said to belong to a community. In any complex network, communities are able to exchange and offer information because members in one community have similar tastes and desires. The determination of such communities is useful in the context of a variety of applications in social-network analysis, including customer segmentation, recommendations, link inference, and vertex labeling and influence analysis. This paper presents a survey on community detection approaches, which have already been proposed, and also discussing the type of social networks on which those proposed approaches are applicable. This survey can play a significant role in the analysis and evaluation of community detection approaches in different application domains.

The movies like the "Avathar" are a good example of the stunning visual effects that the animation could bring into a movie. The 3D wireframe models are converted to 3D photorealistic images using a process called the rendering. This rendering process is offered as a service in the cloud, where the animation files to be rendered are split into frames and rendered in the cloud resources and are popularly known as Rendering-as-a-Service (RaaS). As this is gaining high popularity among the animators community, this work intends to enable the animators to: (a) Gain basic knowledge about Rendering-as-a-Service (RaaS). (b) Understand the variety in the RaaS service models through the taxonomy (c) Explore, compare and classify the RaaS services quickly using the tree-structured taxonomy of services. In this paper, the various characteristics of the RaaS services are organized in the form of a tree to enable quick classification and comparison of the RaaS services. To enhance the understandability, three popular RaaS services have been classified and verified according to the proposed tree-structured taxonomy.

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Cloud services that provide a complete environment for the animators to render their files using the resources in the cloud are called Cloud Renderfarm Services. The objective of this work is to rank and compare the performance of these services using two popular Multi Criteria Decision Making (MCDM) Algorithms namely the Analytical Hierarchical Processing (AHP) and SAW (Simple Additive Weighting) methods. The performance of three real time cloud renderfarm services are ranked and compared based on five Quality of Service (QoS) attributes that are important to these services namely the Render Node Cost, File Upload Time, Availability, Elasticity and Service Response Time. The performance of these cloud renderfarm services are ranked in four different simulations by varying the weights assigned for each QoS attribute and the ranking obtained are compared. The results show that AHP and SAW assigned similar ranks to all three cloud renderfarm services for all simulations.