Social Network Analysis : Approaches, Challenges And Mining Data Essay

Social Networking in a more analytical context is defined as a community of network members which are technically called Nodes. Nodes most commonly differentiate as persons or organisations; if a singular unit can be connected this can be identified as a node. The connections of nodes are usually made based on relations of common interest. Network analysts study the pattern of attraction towards masses of nodes that are connected and this is the structure of how a social network is formed (Scott, Carrington 2011, pp. 11-12).

In this day and age, social media has become an integral part of our lives. It has created a platform for people to share information instantly and communicate with people all around the world. Facebook is the most outstanding example of successful social media network. According to the first quarter 2015 earnings announcement by Facebook, the site has achieved 1.44 billion active users per month, and 65% of which are daily active users. Other social networking sites such as Twitter, Instagram and LinkedIn are also a powerful means of communication. With such a large audience, social networking sites provide ample opportunities to acquire information, network, and connect with friends.

Social Networking is basically a map of relationships between individuals. It indicates ways in which they are connected through various social familiarities -- friends, close friends, school mates, casual acquaintance, family, business, etc. The theory views social relationships in terms of nodes and ties . Nodes are individuals in the network while ties are the relationships between 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.

“4. Social networks have removed all the communication and interaction barriers, and now one can communicate his/her perception and thoughts over a variety of topics. Students and experts are able to share and communicate with like-minded people and can ask for input and opinion on a particular topic.

Social networks have immensely shaped as well as changed the contemporary society. Human interactions, in various settings, such as the workplace and social spaces have been extremely re-designed that a person can communicate, interact, and share his or her daily life experiences with whomever he or she pleases with just a few clicks of the button. Digital social networking has influenced all age groups and aspects of human life. Therefore, if implemented in the correct manner, it stands exceedingly useful in helping individuals and entities, such as businesses flourish and become prosperous (1). Digital social networking could be defined as an organization of persons, brought closer, often by

Social media has changed a lot in the past ten years. Along with social media, the amount of data that is generated from social media has increased massively. “At present, there are over 288 million active monthly users of Twitter who post more than 500 million tweets per day, as well as 1.35 billion monthly active users on Facebook and 187 million active monthly users on LinkedIn”. (Nguyen and Jung, 2016:137). The usage of social media currently shows that so much data is generated every day. Finding ways to utilise this data is a key issue that business have. Many big business have Facebook pages and Twitter accounts so it is crucial for business to gain a competitive edge by taking advantage of online data. Social media data can come in other forms as well. The most mainstream categories of social media include wikis, blogs, microblogs, informal organizations, video and photograph sharing and online groups (Chen et al., 2015). Facebook and Twitter are the two most popular forms but there are other forms of social media that can produce information. Making key utilisation of social media is at the top of many firms’ plans (Lam et al., 2016). To unlock the beneficial insights of social media many tool and techniques can be used. This literature review will look at sentiment analysis and web mining techniques. The focus will also look at how useful analysing data online can be to businesses.

In social graphs, an edge between a pair of nodes only indicates the existence of a relationship between two corresponding participants. Social graphs can be analyzed to

The widespread use of social media sites such as Facebook, Instagram, Twitter and LinkedIn have been generating large amounts of data which are interchangeably known as social data. This data is usually user-generated, inconsistent, and incomplete and have geo information. This data can be used by researchers or companies to extract meaningful information about human behavior, product consumption, trends in markets, etc. Extraction and study of this data for useful purposes is called data mining of social media. Since the number of techniques used to mine data are increasing rapidly with time, it has become imperative to represent the data in a form which can be easily understood. Data mining may consist of the following stages. First, data is extracted,

On exploring Link prediction on the social networks, it lead to various studies in terms of its nature and its peculiarities. As we all know that Social Network is dynamic in nature. New links comes into picture and new relations are formed in later times. Based on the approach we assign the weight to each link from the starting node and ending node. Score (A, B) between the node A and B. The ranks between the pairs (A, B) and (X, Y) is the shortest path in G. One most used measure is the common neighbors. The more and more number of surrounding neighbors similar between A and B would determine that would be possible of forming link in future.

Social networks are popular infrastructures for communication, relations, and information sharing on the internet, popularly known as social network such as My Space Twitter and Facebook supply communication, storage and social applications for hundreds of millions users. Users set up social links to friends and influence their social links to distribute content, organise measures for particular users or share resources, therefore, these social networks provide proposal for organising event users to users communication, and also between the internet’s most accepted destinations. Currently work has been seen as a form of group of socially improved applications that influence the correlation from social networks to improve security and performance of online social network applications as well as spam email improvement Garriss, (2006) On one hand, significant interactive with friends relationship are essential to developed confidence and trustworthiness in the system.

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

Note that initially, we might have some information about the members of social networks in terms of some attributes, then this is important that how we model the information to a graph for which we apply the algorithms.

We connect with friends via social media or a video gets viral, here user is a Vertex and other connected users produce an edge, therefore videos get viral when reached to certain connections. In sociology, economics, political science, medicine, social biology, psychology, anthropology, history, and related fields, one often wants to study a society by examining the structure of connections within the society. This could befriend networks in a high school or Facebook, support networks in a village or political/business

Social networks with complex structure and large scale emerged with the development of social network sites. Various network communities gradually form complex structural pattern in the production and living of people. The competitive advantages and community distribution in networks can be obtained through analyzing the structure of community. Therefore the research key point of the current data mining field is how to find out the potential structure of such large-scale social networks. Currently, most of real networks have overlapping communities. All the users can be allocated to different communities according to different allocation rules. But the complex structure of network and mass node information are difficulties for mining