Titlebook: Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining; Nitin Agarwal,Nima Dokoohaki,Serpil To

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Predictive Analysis on Twitter: Techniques and Applications essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques

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Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Models local topological features of a network. This is relevant, for example, when fitting a random graph model to a real-world network. With respect to existing measures the model might look like a good fit, however, the local topology might be very different. In the article we propose a new characteriz

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Domain-Specific Use Cases for Knowledge-Enabled Social Media Analysisccounts readily generate Big Data marked by velocity, volume, value, variety, and veracity challenges. This type of Big Data analytics already supports useful investigations ranging from research into data mining and developing public policy to actions targeting an individual in a variety of domains

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Harrowing

Deepak Kakadia,Jin Yang,Alexander Gilgure is a group of users in which everyone is a friend to all other group members. Interactions between cliques’ members are studied in different networks for knowledge extraction. We introduced the concept of “weighted cliques” in comparison with classical cliques to provide better understanding of us

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Common Network Pharmacology Databases,the analysis of overlapping communities. Overlapping community structures are suitable indicators as for a real analysis in this domain. As such, we propose a two-phase algorithm based on two significant rather simple social dynamics named Disassortative degree Mixing and Information Diffusion—this