Social networks have been there in some or the other form since the time humans have started interacting. Social network Theory is the study how people, organizations or groups interact within their networks. To create a network using Twitter trending topic to define each city as a vertex, If there is at least one common trend topic between two cities, there is an edge and each edge is weighted according to the number of trendy topics. Network topology doesn't usually change in such scenario as the number of nodes is fixed few metrics that could be used to infer the node's importance and which could explain the type of predictive analysis are Node centrality, Clustering coefficient and Degree centrality. Social media analysis holds a great potential as the they are becoming more huge and complex each day. Read more at: http://dataconomy.com/2017/01/data-mining-predictive-analytics/

Growing social networks have caused rapid development of tools for understanding the interactions between members of the network. The Department of Computer Engineering, at TOBB University, in Ankara, Turkey used Social Network Analysis (SNA) tools to identify the biomarkers present in patient genomic data. Genomic databases comprise of about 20000 genes. Such an approach dramatically decreased the features that need to be analyzed to find useful biomarkers. The team demonstrated their proof of principle with three types of cancer: lymphoma, colon cancer and leukemia. They combined clustering and classification to help in detecting the links between the various genes to validate the outcome. Now they shall extend it with a view to improve diagnostics and tailoring therapy for individual patients based on their personal Biological Network Analysis. Read more at: http://medicalxpress.com/news/2015-06-social-networking-analysis-cancer-biomarkers.html