Graph Theory, World Wide Web, Online Social Network

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Social Network Analysis
Abstract-Social Network Analysis (SNA) techniques is used to find shared interest and trust. This paper presents an idea about why SNA is needed and its implications associated with it. To understand better structure of a social network SNA is very most important. Moreover, in future distributed online social networks will popular and bandwidth intensive, it will create more impact on internet traffic. This analysis needs graphical representations.
Key words- Social Network Analysis (SNA),Graph theory, World Wide Web, Online Social Networks (OSNs). I. INTRODUCTION
Social Network Analysis (SNA) is stated as the mapping and measuring relationships and also flows between two people or groups and other connected
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Terrorist detection and prediction are carried out by SNA tools. Also used to search research papers having similar interest. SNA and data mining techniques are used in medical referral process i.e it will helpful to discover diseases. One more application of SNA is clustering the web using query graph using returning URL and search terms. Clique partitioning method is used to find cluster. SNA can be integrated with semantic web, www etc. Emergency information management and disaster management also uses SNA techniques. Other applications are like analyzing farm animals remains on the network; discover emergent communities of interest between faculty in different…show more content…
Researchers are always looking at degree of a particular node.Geodesc distance and diameter includes distance between two nodes. It consists of shortest path between two nodes. This is used more in network analysis that more concern with smallest distance in large network consisting many undesired paths also. Cliques and Subgroups behavior of a network will help SNA to divide in particular cluster. It is very important aspects of social networks. Maximum flow this means that the strength of a tie between two nodes is depends on the weakest link in the path. Centrality is a kind of measure that follows the influence of a node which contains in the network. The node which is remain in the center have more influenced than nodes who are less central in the network.There are many approaches to identifying centrality are as follows-degree, closeness and betweeness centrality.Small world effect is related with the smaller distance between two nodes in the network. Clustering Coefficient is defined as the ratio of number of closed triplets centered around v to the number of triplets centered around v where v is the focal

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