Background And Motivation On Online Social Networks

Social networking has not only transformed the way people communicate, but they have also changed the kind and amount of information that is accessible.

Appealing for social networks because of there is a capability to reach lots of consumers in networks that are online and it may skip detection methods and behavioral that aim to stop unsolicited contact that is wide-spread. Not as feeling is increased, if the sufferer sees the attacker, and a greater likelihood is that a social engineering attack is going to be successful [1].

In order for people to mine valuable data from social network graphs they must first be given information about the network. Even without explicit information about the nodes, an attacker may use structural information about the nodes and graph itself (e.g. node degree) to identify who the individuals are that the nodes represent. Simple graph-wise randomization addresses this problem by deleting k randomly chosen edges and replacing them with k randomly chosen edges, however a problem arises since data-miners depend on these structural attributes to properly analyze the social network. Fard and Wang [fard-wang] propose a structure-aware algorithm for the randomization of social network edges as well as a formal definition of “link privacy“ with respect to a probabilistic threshold. Their motivation is to help conceal sensitive links by using randomization techniques, without disturbing the actual structure of the graph, which is achieved through local neighborhood perturbation. This is needed so that graphs can be analyzed without the link structure being left entirely vulnerable to attackers. The goal of their algorithm is to make it so that an adversary cannot know if a link in the original graph exists from having a link in the new graph.

Social network analysis is used to study the pattern of communication and relationships among the members of a social network. The interaction on the social network is assumed to be reflective of how the individual interacts and is an insight into the members’ behavior patterns (Costa, 2012). The graph theory has been used effectively to understand a variety of unrelated problems from organizational behavior to spread of infectious diseases. In social network research, various algorithms have been designed in conjunction with concepts from relational learning, web mining and inductive logic to perform predictive as well as descriptive analysis. As described in Karamon (2008) some of

Background and Motivation: To tackle most of the privacy network problems, almost all the social network sites have provided various privacy and security options to the user, but these options do not deal the privacy and security of persons who are part of that information. For example: A group pic being posted by Harry may contain pic of Ron, Hermione etc. While uploading, the Social Networking Sites only takes care about the privacy options of Alice and not of others. So, by this survey, we are motivated to search and explore various security models in privacy network that deals with this

The evaluation of links amongst the users of a social network is one of the most common and convenient method to detect mechanisms. \textbf{Social-Graph} based techniques majorly make use of these links to detect Sybils in the network. A social-graph like its name suggests is a graph of all the users and the relationships among themselves in the given social network. The social graph used in Sybil-detection mechanism tries to analyze the network in a different aspect than the regular graph. The social-graph based techniques consider all the users of the network as the \textbf{nodes} in the graph and the users which can be trusted are called the \textbf{honest nodes}. \textbf{Sybil nodes} are the nodes that are or can be a potential threat

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

ef{fig:LCC_attack} shows the topological resilience of Facebook social network and other complex networks from different network domains. The topological resilience of their corresponding

The Facebook dataset [12] was present in the form of an edgelist. The dataset was read and its corresponding graph was generated, which serves as the original network representation. The random graphs – Erdos–Renyi (ER) random

The assumption of knowing that someone is connected to someone you already know and trust can be one of the most basic reasons to create a new relationship or form new connections. Social networks act as bridges to people or groups who were once disconnected. These connections can be used to share information or opportunities.

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.

“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).

Structure-based analytics (Social network analytics): This type of analytics deals with gaining intelligence from the participants’ relationships and creating structural attributes of a social network. The structure of a social network is created with the help of nodes and edges, as a network graph, where each participant is represented by a node and each edge represents the relationship between two participants. We discuss two kinds of graphs, social graphs and activity graphs [34].

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.

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.