Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous research avenues in sociology, literature, media, biology, computer science, sports, and more. Therefore, certain studies review and discuss some research verticals of SNA, such as viral marketing, information diffusion, clustering, link prediction, and so on, to provide background knowledge and understanding. These studies still lack the SNA process, tools, and practical aspects in multidisciplinary applications. Inspired by these facts, we have discussed the background, process, tools, and application of SNA. First, we have presented a detailed description of the SNA process. Thereafter, we presented a comparative analysis of SNA tools and languages. Finally, we have discussed the various applications corresponding to SNA research verticals.

Consistently the web and online media producing terabytes of images. Furthermore, the picture stockpiling and important recovery is a difficult task. Content Based Image Retrieval (CBIR) is the most common way of recovering pertinent photos regarding query images from the assortment of images. CBIR profoundly relies upon three elements: determination, extraction, and portrayal of elements/features. The proposed review work discusses about these factors in detail. It begins with examining the need for CBIR and its applications. Subsequent sections discuss selecting features such as Color, Texture, Shape, and Descriptors. Feature extraction and their representation are analysed straightaway. Further, the investigation of the most recent papers and their techniques for CBIR is presented. A CBIR has also been applied using Deep Learning Techniques in recent years. An overview of these techniques is presented in this work. The proposed article is an outline of 214 papers covering everything conceivable analysis performed in the field of CBIR.

The information deployment on social networks through word-of-mouth spreading by online users has contributed well to forming opinions, social groups, and connections. This process of information deployment is known as information diffusion. Its process and models play a significant role in social network analysis. Seeing this importance, the present paper focuses on the process, model, deployment, and applications of information diffusion analysis. First, this article discusses the background of the diffusion process, such as process, components, and models. Next, information deployment in social networks and their application have been discussed. A comparative analysis of literature corresponding to applications like influence maximization, link prediction, and community detection is presented. A brief description of performative evaluation metrics is illustrated. Current research challenges and the future direction of information diffusion analysis regarding social network applications have been discussed. In addition, some open problems of information diffusion for social network analysis are also presented.