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.

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