The Importance Of Social Networks

Social networks are growing day by day. For modular representation of Graph $G(V,E)$ first phase of the design issue is to modularize the network having border nodes\cite{newman2006modularity}. Boarder nodes

            Before the internet, “social networking” simply referred to “a network of social interactions and personal relationships” (“social network”). This was a very concrete definition of the term, as it included the entirety of people a person would converse with (“social”) in order to establish a bond between themselves and the other person (“network”). Now, with social media sites such as

Community is basically a social unit where like-minded people interact, network and hold conversations on topics of mutual interest. Of late, many Facebook communities have come into play, and have also commanded immense popularity. The virtual presence of these groups means that all those who have access to Internet can join them if it interests them. There are so many communities on Facebook catering to a wide range of people and their interests – it is hard for a person to not find a community, which does not interest them.

In the last decade, social networks like Facebook [1] have emerged as popular medium of social interaction and information dissemination. From a social web data mining perspective, Facebook stores a wealth of data about people and their interests. As more and more users are creating their own content on Facebook, there is a growing interest to mine this data for use in personalized information access services, recommender systems, tailored advertisements, and other applications that can benefit from personalization.

Figure 9. Community detection on the drug side-effect network using walktrap community detection algorithm. There were 61 communities detected in the network with the largest community consists of 35 drugs and smallest ones consist of only two drugs.

The term “network” is commonly used to describe a structure that can be either physical

As humans constantly use digital media and computational devices in their daily lives, more and more digital traces are left behind. With the addition of mobile devices, the amount of data left behind has been exponentially growing. This data is essential to identify the networks that are made between people. SocioPatterns is a research collaboration that adopts a data-driven methodology with the aim of uncovering fundamental patterns in social dynamics and coordinated human activity. This is very hard for them because with the ever changing population and characteristics, technology must be evolved. To achieve its scientific goals,

The focus of the report will be the challenges faced by mining social network data, approaches to solve these challenges by various graph based analysis methods.

There are many types of social Network; Social Connection, multimedia sharing, professional, informational, educational, academic and hobbies.

The objective of this article is twofold. First, we seek to determine how global parameters of the social network, such as average path length and clustering, affect diffusion processes. Second, we attempt to identify early

And various graph-theoretical tools have been developed and used to analyze this network topology and also modeled networks with such topology. In this dissertation, the theory and key technology challenges within social networks are thoroughly investigated and various novel findings are presented.

Social network behaviour of the users can be complex to understand due to the amount

Another form of attack on online social networks is the Social Link Disclosure based attacks, in which the goal of an attacker is to obtain knowledge of a significant fraction of the links in the network. The value of participating in an online social network for a user lies majorly in the ability to leverage the structure of the social network graph. But, knowledge of this social graph by parties other than the service provider paves the way for powerful data mining, some of which may not be desirable to the users. The ability to conduct these analyses on social networks is becoming increasingly important in the data mining, database and theoretical computer science communities [4.32]. Since nodes in social

We discuss three algorithms say ABCD, PC, and FB from [1,2,3] respectively; which are graph based social networks clustering algorithms. For most cases, we ignore the claims of related papers and just talk about what the algorithms practically do and how can be controlled.

Modular Network: Temporary arrangements among partners that can be assembled and reassembled to adapt to the environment: also called a virtual network.