Social Networks
A social network is a structure made up of social actors interacting and interconnected through relationship among them. The science of studying the social interpersonal relationship is called as Sociometry. Sociogram refers to the graphical representation of the social actors and their relationships. A Social network graph is a
Figure 1. Sociogram of dining-table partners
complex multidimensional graph. An example of a sociogram can be seen in Figure 1, which is from the book `Exploratory Social Network Analysis’ Figure 1 depicts the best choice of dining table partners. Each node of the graph points to a person/actor. The relationship between two actors need not be reciprocative and hence could be directed or undirected. A directed line is called an arc whereas an undirected line is an edge. Social network analysis of graph represented by the Figure 1 could lead to answers to questions such as who is the most/least popular dining partner? etc. This explains social network analysis in its simplest form. This is a key to understand the societal behavior.
A social network can be of many different types based on the relationship attribute. The different types of relationships possible are network on social networking sites, communication network (such as email), citation network, collaboration network, product co-purchasing network, road network, peer to peer network, online review network etc. All the above examples describe connection among people but each relationship is of different nature. Unlike Wireless Sensor Network, where connectivity between two sensor nodes depends on the communication range of a sensor node, social network formation purely depends on the relationship attribute.
Emergence of online social networking websites has revolutionized the study of human relationships. Social networking sites such as Facebook, Twitter, and Flickr have provided means to form social groups online. Earlier the social networking analysis was limited to information collected from individuals through dicult approaches. With the advent of online social networking websites the scale and accuracy of social network analysis has increased manifold. The term Monthly Active User (MAU) is widely used to report the active users of a social networking website. Facebook boasts the largest MAU of 1.57 billion users as of June 30 2016. Twitter another social networking website used for short message exchanges has around 313 million active users. The online social networks can be directed or undirected. Some examples of directed social network graph are citation network, twitter etc.
On the other hand, there are undirected social networks such as collaboration network, Facebook friend network. Though availability of super-fast computers paves a way to study networks of huge size, the number of users of a social networking website is growing enormously and there is a need to study the social networks. The need to study online social networks comes out of its applications. It is widely used in issues pertaining to national security such as, for intelligence/counter-intelligence to combat terrorism, analysis of call detail records to study the relationship of suspects etc.
A collaboration network is formed on the basis of scientic collaboration between authors of scientic journals submitted to a forum on a particular category. Each author is considered as a node and if an author i co-authored a paper with another author j then, the graph contains an undirected edge between two authors `i’ and `j’. E-mail communication covers all the email communication of a particular organization. Nodes of the network are the email addresses and an e-mail sent from one node to another denotes the edge. Also, in the eld of medicine, it is applied for protein-protein interaction, spread of infectious diseases such as AIDS etc. The enormous growth of social networks and its signicant applications has resulted in surge of interest in researching the structural and behavioral properties.
Typically the information collected by using WEB crawler software leaves us with improper or inaccurate information. Thus there is immense signicance in being able to extract the missing information from available data.
