Social networking&it’s models
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This document provides an overview of social networking and its models. It begins with definitions of social networking and social networking models. It then discusses the history of social networking from early computer networks to modern sites like Facebook. Several key aspects of social networking are analyzed, including its importance, applications like modeling terrorist networks, and mathematical representations. The document also includes sections on literature reviews, case studies, and decision making processes within social networks. It provides an example analysis of a "Kite network" to demonstrate social network analysis techniques.
Social networking&it’s models
- 1. Social Networking & IT’s Models By JAGDISH.R
- 2. Contents • Definition • History Of Social Networking. • Analysis of Social Networking.(Kite Network) • Importance Of Social Networking. • Application Of Social networking in 9-11 Attack. • Mathematical Representation. • Literature Survey. • Case Studies.
- 3. Definition: • A Social Networking service is an online service, platform, or site that focuses on building and reflecting of social networks or social relations among people, who, for example, share interests and/or activities
- 4. Definition • The Social networking models are those models that add value to community environments supporting social networking, and are specifically applicable to the community-driven environments, where users create and share their vocabularies.
- 5. History • The potential for computer networking to facilitate new forms of computer-mediated social interaction was suggested early on. Efforts to support social networks via computer-mediated communication were made in many early online services, including Usenet, ARPANET, LISTSERV , and bulletin board services (BBS)
- 6. • Many of these early communities focused on bringing people together to interact with each other through chat rooms, and encouraged users to share personal information and ideas via personal webpages by providing easy-to- use publishing tools and free or inexpensive webspace
- 7. • New social networking methods were developed by the end of the 1990s, and many sites began to develop more advanced features for users to find and manage friends. • . Facebook, launched in 2004, has since become the largest social networking site in the world.Today, it is estimated that there are now over 200 active sites using a wide variety of social networking models
- 9. Social Networking Websites • What are they? • Tool for: • Communication • Expressing interests • “Nodes and Ties” • Recent phenomena • Digg, 2004 • Youtube, 2005 • Myspace, 2003 • Facebook, 2004
- 12. Developments • Third-party applications • Privacy settings • Instant messaging • News feed (Facebook) • Music (Myspace)
- 13. Areas were SN is implemented • It is Applicable in • Quality is been Marketing. accelerating its • It is Applicable in position in social Operations networks day by day. Management. • Influence of • No MIS=No Social Operation research Network. in Social Network.
- 14. Network • A Network exists were a group of individuals are involved in interaction.
- 16. Decision Making Process • Strengthening Organizations Strategies. • To recognize the Leaders. • Accelerate the level of Competition
- 17. MIS IN Social Network
- 19. Exercise on SNA: Kite Network
- 20. Social Network Analysis We measure Social Network in terms of: 1. Degree Centrality: The number of direct connections a node has. What really matters is where those connections lead to and how they connect the otherwise unconnected. 2. Betweenness Centrality: A node with high betweenness has great influence over what flows in the network indicating important links and single point of failure. 3. Closeness Centrality: The measure of closeness of a node which are close to everyone else. The pattern of the direct and indirect ties allows the nodes any other node in the network more quickly than anyone else. They have the shortest paths to all others.
- 21. Exercise on SNA: Kite Network • Who is the Connecter or Hub in the Network? • Who has control over what flows in the Network? • Who has best visibility of what is happening in the Network? • Who are peripheral players? Are they Important?
- 22. SNA and KMS (2) • Short distances transmit information accurately and in a timely way, while long distances transmit slowly and can distort the information. • Isolation - People that are not integrated well into a group and therefore, represent both untapped skills and a high likelihood of turnover. • Highly expert people - Not being utilized appropriately. • Organizational subgroups or cliques - Can develop their own subcultures and negative attitudes toward other groups.
- 23. Kite network Analysis • Degree Centrality:- • In the kite network above, Diane has the most direct connections in the network, making hers the most active node in the network. She is a 'connector' or 'hub' in this network. Common wisdom in personal networks is "the more connections, the better." This is not always so. What really matters is where those connections lead to -- and how they connect the otherwise unconnected! Here Diane has connections only to others in her immediate cluster -- her clique. She connects only those who are already connected to each other.
- 25. Application of SNA: • Realizing 9/11 Al- Qaeda Network. • Build a grass roots political campaign. • Determine influential journalists and analysts in the IT industry. • Map executive's personal network based on email flows. • Discover the network of Innovators in a regional economy. • Analyze book selling patterns to position a new book and many more……
- 26. Web Applications of Social Networks • Analyzing page importance – Page Rank • Related to recursive in-degree computation – Authorities/Hubs • Discovering Communities – Finding near-cliques • Analyzing Trust – Propagating Trust – Using propagated trust to fight spam • In Email • In Web page ranking
- 27. Society as a Graph People are represented as nodes.
- 28. Society as a Graph People are represented as nodes. Relationships are represented as edges. (Relationships may be acquaintanceship, friendship, co- authorship, etc.) Allows analysis using tools of mathematical graph theory
- 29. HOW SOCIAL NETWORKING WAS IMPLEMENTED IN 9-11 ATTACK • Literature Survey • Case Study The Black Hole of 9/11 BY DAVID J. ROTHKOPF | AUGUST 29, 2011
- 30. 19 Hijackers involved in 9-11 Attack And Their Networking
- 31. Khalid Sheikh Mohammed • History • Linked with Osama • Khalid Sheikh • He was born in Mohammed was a Kuwait in 1964. member of Osama bin Laden's terrorist • He is Mechanical group al- Qaeda organization, alth Engineer who ough he lived graduated from in Afghanistan, heading alQaeda's propaganda o Chowan College in perations from 1986. sometime around 1999.
- 34. Networking Process!! In late 1998 or early 1999, bin Laden gave approval for Mohammed to go forward with organizing the plot. Bin Laden was also involved in selecting people to participate in the plot, including choosing Mohamed Atta as the lead hijacker. Bin Laden had been pressuring KSM (Khalid Sheikh Mohammed) for months to advance the attack date.
- 37. Modeling Terrorist Networks One of the earliest and most influential maps was developed by Valdis Krebs (Krebs, 2001)
- 39. Within one week of the attack, We soon knew there were 19 hijackers, which planes they were on, and which nation's passports they had used to get into America. As more information about the hijackers' past was uncovered I decided to map links of three strengths (and corresponding thickness). Those living together or attending the same school or the same classes/training would have the strongest ties. Those travelling together and participating in meetings together would have ties of moderate strength and medium thickness. Finally, those who were recorded as having a single transaction together, or an occasional meeting, and no other ties, I classified as weak ties that were shown with the thinnest links in the network.
- 40. Key points! After one month of investigation it was 'common knowledge' that Mohamed Atta was the ring leader of this conspiracy.
- 42. Foot Steps of Atta! On September 10, 2001, Atta picked up Omari from the Milner Hotel in Boston, Massachusetts, and the two drove their rented Nissan Altima to a Comfort Inn in South Portland, Maine; on the way they were seen getting gasoline at an Exxon Gas Station. They arrived at 5:43 p.m. and spent the night in room 232. While in South Portland, they were seen making two ATM withdrawals, and stopping atWal-Mart. FBI also reported that "two middle-eastern men" were seen in the parking lot of a Pizza Hut
- 43. Atta and Omari arrived early the next morning, at 5:40 a.m., at the Portland International Jetport, where they left their rental car in the parking lot and boarded a 6:00 a.m. Atta (blue shirt) and Omari in the Portland International Jetport in Portland, Maine on the morning of 9/11
- 44. *The hijacking began at 8:14 a.m.—15 minutes after the flight departed—when beverage service would be starting. At this time, the pilots stopped responding to air traffic control, and the aircraft began deviating from the planned route. At 8:18 a.m., flight attendants Betty Ong and Madeline Amy Sweeney began making phone calls to American Airlines to report what was happening. Ong provided information about lack of communication with the cockpit, lack of access to the cockpit, and passenger injuries *At 8:24:38 a.m., a voice believed to be Atta's was heard by air traffic controllers, saying: "We have some planes. Just stay quiet and you will be OK. We are returning to the airport." "Nobody move, everything will be OK. If you try to make any moves you'll endanger yourself and the airplane. Just stay quiet..." "Nobody move please. We are going back to the airport. Don't try to make any stupid moves." The plane's transponder was turned off at 8:28 a.m. At 8:46:40 a.m., Atta crashed the Boeing 767 into the North Tower of the World Trade Center. This was the first aircraft to hit the Twin Towers on the morning of September 11, 2001
- 45. Car Dealer Adnan G. El Shukrijumah Linked to 9/11 Hijacker Mohamed
- 49. Abstract:- Social networks are becoming more and more popular with the advent of numerous online social networking services. In this paper, we explore social rating networks, which record not only social relations but also user ratings for items. We distinguish two types of user behaviour: adopting an item and adopting a rating value for that item. We propose models to analyze and measure the influence of neighbours on both item and rating adoption behaviour of users.
- 50. • The main contributions of this paper are as follows: • We analyze the effect of social influence and correlation influence on item adoption and rating adoption in the Flixster and the Epinions dataset (section IV). • We present models for item and rating adoption, based on so-called influence coefficients (section V.A), and for the actual rating behaviour of users, based on their neighbours' ratings (section V.B).
- 51. • We introduce the concept of social authority of individual users and a way to inject social authority into a recommender to improve the accuracy of recommendation in social networks (section VII).
- 52. CONCLUSION Social networks are becoming more and more popular with the advent of numerous social networking services online such as Facebook, MySpace, Flixster, etc. which allow complex interactions among users. In this paper we focused on social rating networks: social networks in which users can express ratings on items. We explored the effect of social and correlation influence on the behaviour of users. We analyzed and modelled the item adoption and rating adoption behaviour in social and similarity networks. We proposed a simple model for rating behaviour of users. Our experiments on Epinions and Flixster demonstrated that the influence coefficients in social networks are higher than those in similarity networks.
- 53. Literature Survey-2 • Seeking New Social Networking Models How applications will adapt to the upcoming network bandwidth perimeters and impact the Social Web topology.
- 54. • Abstract:- The Web is rapidly evolving form a human-to-machine to a human-to-human communication means. Unfortunately the current proliferation of Social Networking Web sites is generating fragmentation and lack of interoperability. In order to be in contact with friends users must be subscribed and upload their contents to the same online Social networking provider. In this paper we propose a concrete alternative to the current state of the art: overcoming vertical silos' approach and enabling open, distributed and Context Aware Social Networking being respectful of users’ privacy and data ownership. Telecom Italia is investing in this research area within EU FP7 project SOCIETIES.
- 55. INTRODUCTION • Despite the increasing success of the current isolated online Social Networking initiatives, several concerns are intrinsic to this mechanism data ownership. • In addition to that, the upcoming NGAN (Next generation access network) is promising a symmetric link of 100 Mb it to residential customer’s premises.
- 56. • A federated Social Networking platform could therefore permit individuals to be part of the Social Web and securely share their own multimedia resources in a fully distributed manner. USE CASE: BRIDGING PERSONAL SOCIAL ISLANDS
- 57. Accessing smart home contents from the Internet.
- 58. CONCLUSIONS Time has come to replace the Social Networking silo approach and unblock novel Social Area Network paradigms Decentralizing Identity management and profile information will avoid a centralized control and ownership of data. A Social aware sharing of intelligent devices services may furthermore be seen as a compelling use case for the Internet of Things paradigm.
- 59. Refrences • Del Valle, ST, Hyman, JM, Hethcote, HW, and Eubank, SG. in review. Mixing patterns between age • groups using social networks. • Dugatkin, LA andWilson, DS. 1991. Rover: a strategy for exploiting cooperators in a patchy environment. • Am. Nat. 138:687-701. • Fishbein M, Higgins, DL, Rietmeijer, C & Wolitski, RJ. 1999. Community-level HIV Intervention in • 5 Cities: Final outcome data from the CDC AIDS Community Demonstration Projects. American • Journal of Public Health, 89(3): 336-345. • Hirshleifer, D and Rasmusen, E. 1989. Cooperation in a repeated Prisoners’ Dilemma with ostracism. • J. Econ. Behav. Organiz. 12: 87-106. • Hyman, JM, and Stanley, EA. 1988. Using Mathematical Models to Understand the AIDS epidemic. • Math. Biosci., 90:415-473.
- 62. Thanking You