18-09-2014, 11:08 AM
Social network analysis
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Abstract:
Social network analysis is increasing rapidly in popularity, both in academic research and in management consulting. The concept of network has become the metaphor for understanding organizations. Academics see the network paradigm as away to escape from the atomism of traditional social science in which individual behavior—such as adoption of an innovation—is analyzed solely in terms of the attributes of the individual (e.g., openness to change, stake in the outcome, etc.) and not in terms of interpersonal transmission, influence processes, and other relational variables. Management practitioners are interested in network methodology because it provides away to make
INTRODUCTION:
Online social networks (OSNs) (e.g., Social Network, Twitter) are now among the most popular sites on the Web. An OSN provides a powerful means of establishing social connections and sharing, organizing, and finding content. For example, Social Network presently has over 500 million users. Unlike current file or video sharing systems (e.g., Bit Torrent and YouTube), which are mainly organized around content, OSNs are organized around users. OSN users establish friendship relations with real world friends or virtual friends, and post their profiles and content such as photos, videos, and notes to their personal pages.
Social Network Analysis (SNA) means set of techniques developed to study the structural information contained in social entities’ interactions (e.g., communication and relationships). In Social Networks (SNs), every node represents a social entity (it may be a person, a group of persons, or an organization), and an edge between two nodes represents an interaction between corresponding entities. Generally, SNs can be used to study many types of interactions between different types of entities. Node does not request videos from neighbors with marked connections A full understanding of the phenomena cannot be obtained by simply “summing” the results obtained from each interaction together. BKBs do not require a full conditional probability table for each random variable and the set of variables it depends on.
Modern social network analysis has been studied for nearly 70 years. Network analysis approaches are attracted a large amount of research interest, and there have been huge computer software tools and methodologies developed to do this analysis. These analysis tools and techniques can analyse SNs and it can provide the insights. However, development of sophisticated social network analysis methods even now also faces a lot of challenges. If we Compare with the great strides made in social network analysis approaches over the years, research into SN construction (i.e., gathering, using, and combining data for SNs) has been lagging. SN-based modeling frameworks are built for a specific domain (e.g., geographic, religious, and application specific), which cannot be readily mapped to another domains. After , many complex applications require the combination of multiple SNs across various domains. As a result the problem of effectively handling a mass of social data gathered from multiple domains is one of the pressing problems faced by SNA researchers.
REALTED WORK:
1)
Title : Inside Social Network Analysis
Author : Kate Ehrlich1 and Inga Carboni2
Year : 2008
Description : A management consulting firm hopes to win a lucrative contract with a large international financial institution. After weeks of intense preparation, the team sends off a proposal. Shortly thereafter, they learn that contract was given to a competitor with whom the client had worked previously. Almost six months later, one of the team members finds out that another group at the management consulting firm had worked on a project with the prospective client and had gained an in-depth knowledge of its business operations. Why, asked the frustrated team member, wasn’t this critical knowledge shared with the team?
2)
Title : Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
Author : J. Stern, Ed., Keith W. Ross
Year : 1999
Description : This paper investigates a novel computational problem, namely the Composite Residuosity Class Problem, and its applications to public-key cryptography. We propose a new trapdoor mechanism and derive from this technique three encryption schemes: a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA. Our cryptosystems, based on usual modular arithmetic’s, are provably secure under appropriate assumptions in the standard model.
3)
Title : Protocols for Secure Computations
Author : Stephen P. Borgatti, José Luis Molina
Year : 2003
PROPSED FRAMEWORK:
The scope of our project for CISNs is to define mathematical structures and mappings to allow crossover of social information/theories/relations into the computational domain for a generic scenario. It should also be noted that our goal is not to define or identify culture. It is a domain-independent framework that takes into account the inherent uncertainty and incompleteness of cultural data. Our framework will also seamlessly link cultural factors to entity behavior. Since a CISN leverages probabilistic reasoning networks and intent-based behavioral models, it has a rigorous foundation. Because of the varied meaning and significance of culture across domains of social science, we employ a very broad definition of culture and focus on representing culture in a manner relevant to the model, as identified by the SMEs and domain experts. In addition to infusion of culture, the CISN has the capability to model and analyze social processes at multiple scales in social systems
MODULE DESCRIPTION
User Interface Design
This is the first step of our project. In this module generates the CON (Contact Opportunity Network) that represents the communication aspects that we seek to model in the final CISN. Contacts between entities can be formed in a number of ways, and each way may be represented by an SN. For example, networks representing membership in a club, transportation networks (roads, rails), etc., can be used to calculate contact opportunity in the form of edge weights in the CON. This component consists of algorithms to extract and combine information from multiple networks
Creating Social Relationship
This is the second step of our project in this we are going to collecting the all registered user details from database and matching with currently registering user details based upon that we can specifies the some related friends when he his login to our SN. After users in other video sharing websites are driven to watch videos by interests, while in Social Network, the followers of a source node (i.e., video owner) are driven to watch almost all of the source’s videos primarily by social relationship, and non-followers watch a certain amount of videos mainly driven by interest (I2). According to these differentiating aspects, we design the P2P overlay structure
Social Network Analysis applications:
Social network analysis (SNA) is the methodical analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.) These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines.
Multi agent System Application:
Multi agent systems are a new paradigm for understanding and building distributed systems, where it is assumed that the computational components are autonomous: able to control their own behavior in the furtherance of their own goals. The first edition of An Introduction to multi agent Systems was the first contemporary textbook in the area, and became the standard undergraduate reference work for the field. This second edition has been extended with substantial new material on recent developments in the field, and has been revised and updated throughout. It provides a comprehensive, coherent, and readable introduction to the theory and practice of multi agent systems, while presenting a wealth of discussion topics and pointers into more advanced issues for those wanting to dig deeper
CONCLUSION AND FUTURE WORK
Currently, Social Network Analysis users are often faced with incomplete and skewed SN data sets because of missing cultural elements. In this paper, we present a generic approach that can be used to systematically model cultural influences and infuse them into SNs. We implemented our approach and tested it with two sets of experiments. Based on the analysis of our experimental results, we validated the effectiveness of our approach and demonstrated that infusing SNs with culture is a promising technology to aid SN analysts in identifying significant groups and ties in the face of data overload.
In future work the detailed design of each component in our framework will be described. One important future task is to study how to systematically store, select, and fuse BKFs in order to build cultural fragments. Currently, the infusion of cultural information into SNs is done based on linear combination. Another significant task for us is to research the most effective methods for infusion in various applications. In the future, we will continue to develop the framework in a plug-and-play fashion and then explore the use of various (and sometimes competing) social theories, both to add rigor to the modeling of an actor’s intent and actions, and to compare and contrast the effects and validity of those theories. Scalability becomes an important issue in real-world scenarios, where numerous actors and large social networks are the norm. We will leverage previous work on parallel/distributed any time anywhere algorithms, to build scalable algorithms for analyzing CISNs. We will also improve the scalability of the CISN model by developing parallel/distributed methodologies for Bayesian reasoning.