Data Mining And Social Networking

In our first unit of sociology, I felt I could relate with the term social network, " The term social network refers to the people who are linked to one another(Henslin,116.)"Social networks include everyone you know. I can relate to social networking because I 'm on a social salsa team. When I joined my dance team , we all had the same passion, the love of dance. After a few years of performing and hanging with the same clique all the time for they have the same interests as me. After a while we all seemed to cluster together and they are now more than just a clique to me they are my fiends.

The film ‘How Social Networks Predict Epidemics’ by Nicholas Christakis explores the influence of social networks on people’s lives and how it can be used to predict epidemics.Social networks are key connectors among individuals in all societies today. They are avenues through which people learn and share many new things with others. Social networks are so instrumental in determining many issues like employment and salaries, and the transmission of diseases. It is, therefore, crucial to comprehend why and how the networks influence the patterns as they do.

As written by the authors social ties are usually discussed in terms of “structure and content (Umerson, crosnoe & Reczek, p.1). Structure refer to social integration which is a process that involved everyone within a circle to participate with the hope of achieving a peaceful and good relationship whereas social network can be thought of as a process whereby individuals build relation among each other and share similar idea and thoughts. Content which can be positive or negative refers to social support and stress.

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.

Social networks are seen by many people in society as a drain of time, resources and focus from more important tasks that can be accomplished in person. Yet these critics are missing a vital link of social networks and their embryonic state today

In social network analysis, people within a network are referred to as nodes, and the relationships between people are referred to as edges.7 Each pair of nodes (i, j) is either connected or disconnected, that is, the contact direction is not taken into account in the analysis of the influenza transmission. In the simplest data generation, the connections are defined as

In social network analysis, people within a network are referred to as nodes, and the relationships between people are referred to as edges.7 Each pair of nodes (i, j) is either connected or disconnected, that is, the contact direction is not taken into account in the analysis for the influenza transmission. In the simplest data generation, the connections are defined as

(In a socio-centric network analysis, by contrast, information is gathered from each person, about each person, in a relatively closed network.) A frequent focus of SNA studies is homophily, or the tendency of individuals who are similar in their beliefs, attitudes and behaviors to be linked more frequently and more closely in social networks than those who are dissimilar [11]. In his classic housing study, Festinger [12] found evidence of homophily based on propinquity, the tendency of people who live close together to be more connected. Social network analysis is also used to examine the structural characteristics of social networks.

The roots of theoretical constructs of SNA in graph theory has led to the use of mathematical concepts being borrowed and built upon to suit its needs. As mentioned before, a graph is a collection of nodes, strung together by edges. Hence, to get from one node to the next, one needs to travel along the edges. The number of edges traversed to reach from one node to another forms the notion of distance in graphs. More specifically, the geodesic distance, d(u,v), between two nodes is defined as the length of the shortest path between them (Bouttier et al. 2003). The conceptualization of ‘distance’ as the number of intermediate edges between nodes, capture the way nodes are embedded in the network (Hanneman & Riddle 2005, p.77). In terms of a social systems, being friends with an important person is always beneficial, as it potentially makes you ‘closer’ to many people in the network [Needs explanation?]. In SNA, the edges connecting two vertices are usually un-weighted. However, in certain application scenarios, the edges may have weights associated with them to represent factors like strength of a tie, or probability of forming a tie, or in case of spatial social networks, the geographic distance between the nodes. The weights on the edges adds another layer to the conceptualization of distance, and the geodesic distance calculation then, needs to account for the weights of the edges. If there is no path connecting the two vertices, i.e., if they belong to different connected

Using data mining techniques, such as graph mining and social network analysis on regional data sources could contribute great insights and improve operations. Social networking analysis is the study and analysis of networks involving social interaction. Types of

A social network is a set of people (or organizations or other social entities) connected by a set of social relationships such as friendship, co-working or information exchange. Social networks are connected through various social familiarities ranging from casual acquaintance to close familiar bonds. Social network analysis provides both a visual and a mathematical analysis of relationships. Social network analysis (SNA) is a quantitative analysis of relationships between individuals or organizations. By quantifying such social structures it is possible to identify most important actors, group formations or equivalent roles of actors within a social network. This paper presents various properties or analysis measures for social

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

“The experiments we conducted on a Facebook sample of 957,000 users and randomly generated graphs highlight the role and importance of weak ties. We characterized the overall statistical distribution of weak ties as a function of the size of a community and its density. We studied their role in information-diffusion processes, with results suggesting a connection between our definition of weak ties for OSNs and Mark Granovetter’s original definition” (Provetti, pg. 2).

In this paper make a case for concerning the proliferation of GPS enabled mobile devises and the popularity of social networking have recently light emitting diode to the zoom of Geo-Social Networks (GEOSN s). GEOSN s has created a fertile ground for new location based social interactions. These are expedited by GEOSN queries that extract helpful info combining each the social relationships and therefore the current location of the users.

Since the size of the graphs is increasing exponentially, many direct processes become more demanding. For example, LinkedIn—a well-known website for professional networking—that tries to connect professionals together worldwide. If a person is trying to get in touch with someone from the human resource department in a company by using LinkedIn, what the website does is try to find the shortest path to reach that person in that specific company, starting from his connections and moving on to friends of friend to reach the desired personal in the specified company. Similarly, this application of the shortest path can be used over and over in different scenarios, for example, finding routes from one point to another point in GPS navigation system. In many cases, when finding the shortest path, in a selected graph that consists of millions of nodes and edges the measurability and accuracy becomes more complex to measure, the shortest path query must respond to the request as fast as possible with high accuracy. Although the graph may comprise of a lot of nodes and edges but the shortest path must be calculated fast, as an example in car navigation (GPS) an alternative route could be used [1] to provide the driver with a driving route to the requested destination in a given situation the driver would prefer a quick response that is