Social Networks
Dr. Bhavani Thuraisingham
June 2010

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What are Social Networks
Social Network Views: Science, Technology, Culture
Social Network Concepts
Social Networks and Knowledge Management
Social Networks and Semantic Web
Applications
Directions
References:
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 ce.sharif.edu/~m_jamali/resources/WI06_SNA.ppt (WI 2006) ic.ucsc.edu/~wsack/fdm20c/fall2008/Lectures/social-networks.ppt

SOCIAL NETWORKS
HTTP://WWW.FLAIRANDSQUARE.COM/ARCHIVES/167
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A social network site allows people who share interests to build a ‘trusted’ network/ online community. A social network site will usually provide various ways for users to interact, such as IM
(chat/ instant messaging), email, video sharing, file sharing, blogging, discussion groups, etc.
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The main types of social networking sites have a ‘theme’, they allow users to connect through image or video collections online (like Flicker or You Tube) or music (like My Space, lastfm). Most contain libraries/ directories of some categories, such as former classmates, old work colleagues, and so on (like
Face book, friends reunited, Linked in, etc). They provide a means to connect with friends (by allowing users to create a detailed profile page), and recommender systems linked to trust.

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Face book - A social networking website. Initially the membership was restricted to students of Harvard University. It was originally based on what first-year students were given called the “face book” which was a way to get to know other students on campus. As of July 2007, there over 34 million active members worldwide. From September 2006 to September 2007 it increased its ranking from 60 to 6th most visited web site, and was the number one site for photos in the United States.
Twitter- A free social networking and micro-blogging service that allows users to send “updates” (text-based posts, up to 140 characters long) via SMS, instant messaging, email, to the Twitter website, or an application/ widget within a space of your choice, like MySpace, Facebook, a blog, an RSS
Aggregator/reader.
My Space - A popular social networking website offering an interactive, usersubmitted network of friends, personal profiles, blogs, groups, photos, music and videos internationally. According to AlexaInternet, MySpace is currently the world’s sixth most popular English-language website and the sixth most popular website in any language, and the third most popular website in the
United States, though it has topped the chart on various weeks. As of
September 7, 2007, there are over 200 million accounts.

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 social network analysis is an interdisciplinary social science;
Sociologists, computer scientists, physicists and mathematicians have made large contributions to understanding networks in general (as graphs) and thus contributed to an understanding of social networks
[Social network analysis] is grounded in the observation that social actors
[i.e., people] are interdependent and that the links [i.e., relationships] among them have important consequences for every individual [and for all of the individuals together]. ... [Relationships] provide individuals with opportunities and, at the same time, potential constraints on their behavior.
... Social network analysis involves theorizing, model building and empirical research focused on uncovering the patterning of links among actors. It is concerned also with uncovering the antecedents and consequences of recurrent patterns. (from Linton C. Freeman)

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“Sociograms” were invented in 1933 by Moreno.
In a sociogram, the actors are represented as points in a two-dimensional space. The location of each actor is significant. E.g. a “central actor” is plotted in the center, and others are placed in concentric rings according to “distance” from this actor.
Actors are joined with lines representing ties, as in a social network. In other words a social network is a graph, and a sociogram is a particular 2D embedding of it.
These days, sociograms are rarely used (most examples on the web are not sociograms at all, but networks). But methods like MDS (Multi-Dimensional
Scaling) can be used to lay out Actors, given a vector of attributes about them.
Social Networks were studied early by researchers in graph theory (Harary et al.
1950s). Some social network properties can be computed directly from the graph.
Others depend on an adjacency matrix representation (Actors index rows and columns of a matrix, matrix elements represent the tie strength between them).

 email, newsgroups, and weblogs
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 search engines: e.g., Google (http://google.com)
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Google’s Page Rank algorithm gives more weight to popular webpages.
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A webpage is considered popular if many other webpages link to it.
collaborative filtering and/or recommender systems; e.g., amazon.com’s feature: “People who bought this book also bought...”

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What is Your Network?
When your connections invite their connections, your Network starts to grow.
Your Network is your connections, their connections, and so on out from you at the center.
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How do you classify users?
Your Network contains professionals out to “three degrees” — that is, friendsof-friends-of-friends. If each person had 10 connections (and some have many more) then your network would contain 10,000 professionals.
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How do you see who is in your Network?
LinkedIn lets you see your network as one large group of searchable professional profiles.

SOCIAL NETWORKS AS
POPULAR CULTURE
 e.g., six degrees of kevin bacon
 bacon number: definition http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_B acon
 kevin bacon has a bacon number of 0
 an actor, A, has a bacon number of 1 if s/he appeared in a movie with kevin bacon
 an actor, B, has a bacon number of 2 if s/he appear in a movie with A
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. social software; e.g., facebook, friendster, orkut,

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A structural approach to understanding social interaction.
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Networks consist of Actors and the
Ties between them.
We represent social networks as graphs whose vertices are the actors and whose edges are the ties.
Edges are usually weighted to show the strength of the tie.
In the simplest networks, an Actor is an individual person.
A tie might be “is acquainted with”. Or it might represent the amount of email exchanged between persons A and B.

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Effects of urbanization on individual wellbeing
World political and economic system
Community elite decision-making
Social support, Group problem solving
Diffusion and adoption of innovations
Belief systems, Social influence
Markets, Sociology of science
Exchange and power
Email, Instant messaging, Newsgroups
Co-authorship, Citation, Co-citation
SocNet software, Friendster
Blogs and diaries, Blog quotes and links

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Balance: important in exchange networks
In a two-person network (dyad), exchange of goods, services and cash should be balanced.
More generally, exchanges of “favors” or “support” are likely to be quite balanced.
Role: what role does the actor perform in the network?
Role is defined in terms of Actors’ neighborhoods.
The neighborhood is the set of ties and actors connected directly to the current actor.
Actors with similar or identical neighborhoods are assigned the same role.
What is the related idea from semiotics?
Paradigm: interchangability. Actors with the same role are interchangable in the network.

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Prestige: How important is the actor in the network?
Related notions are status and centrality.
Centrality reifies the notion of “peripheral vs. central participation” from communities of practice.
Key notions of centrality were developed in the 1970’s, e.g.
“eigenvalue centrality” by Bonacich.
Most of these measures were rediscovered as quality measures for web pages:
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Indegree
Pagerank = eigenvalue centrality
HITS ?= two-mode eigenvalue centrality

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Actor
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An “actor” is a basic component for SNs. Actors can be:
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Individual people, Corporations, Nation-States, Social groups
Modes
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If all the actors are of the same type, the network is called a onemode network. If there are two groups of actor then it is a twomode network.
E.g. an affiliation network is a two-mode network. One mode is individuals, the other is groups to which they belong. Ties represent the relation: person A is a member of group B.
Ties
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A tie is the relation between two actors. Common types of ties include:
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Friendship, Amount of communication, Goods exchanged, Familial relation (kinship), Institutional relations

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Because human relations are rich and unbounded, drawing meaningful boundaries for network analysis is a challenge.
There are two main approaches:
Realist: boundaries perceived by actors themselves, e.g. gang members or ACM members.
Nominalist: Boundaries created by researcher: e.g. people who publish in ACM CHI.
To deal with large networks, sampling is necessary. Unfortunately, randomly sampled graphs will typically have completely different structure. Why?
One approach to this is “snowballing”. You start with a random sample. Then extend with all actors connected by a tie. Then extend with all actors connected to the previous set by a tie…

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Social networks are formed between Web pages by hyperlinking to other Web pages.
A hyperlink is usually an explicit indicator that one
Web page author believes that another page is related or relevant.
The possibility to publish and gather personal information, a major factor in the success of the Web
Two Major Tasks
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Social Network Extraction from the Web
Social Network Analysis
Social Networking Services (SNS).
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Friendster; Orkut

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Bibliographic Metrics
 bibliographic coupling
 co-citation coupling

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Weblogs have become prominent social media on the Internet that enable users to quickly and easily publish content including highly personal thoughts.
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Bloggers might list one another’s blogs in a Blogroll and might read, link to a post , or comment on other blogs’ posts (A post is the smallest part of a blog which has some contents and readers can comment on it. A post also has a date of publish).

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Semantic Web: having data on the Web defined and linked in a way that it can be used by people and processed by machines in a ”wide variety of new and exciting applications”
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SW and SN models support each other:
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Semantic Web enables online and explicitly represented social information
 social networks, especially trust networks, provide a new paradigm for knowledge management in which users
”outsource” knowledge and beliefs via their social networks

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Drawbacks to Centralized Social Networks
 the information is under the control of the database owner
 centralized systems do not allow users to control the information they provide on their own terms
The friend-of-a-friend(FOAF) project is a first attempt at a formal, machine processable representation of user profiles and friendship networks.
The Swoogle Ontology Dictionary shows that the class foaf:Person currently has nearly one million instances spread over about 45,000
Web documents.
The FOAF ontology is not the only one used to publish social information on the Web.
For example, Swoogle identifies more than 360 RDFS or OWL classes defined with the local name ”person”.

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Knowledge representation.
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Small number of common ontologies
Knowledge management.
 efficient and effective mechanisms for accessing knowledge, especially social networks, on the Semantic Web
Social network extraction, integration and analysis
 extracting social networks correctly from the noisy and incomplete knowledge on the (Semantic) Web
Provenance and trust aware distributed inference.
 manage and reduce the complexity of distributed inference by utilizing provenance of knowledge

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Why Social Networks in
KMS?
People
Technology
KM
Organization
Processes
Knowledge Management involves people, technology, and processes in
Overlapping parts.

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Why are we studying
Social Networks ?
Social
Networks
What ties Information Architecture,
Knowledge Management and
Social Network Analysis more closely together is the reciprocal relationship between people and content.
Information
Architecture
Knowledge
Management
Systems

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Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities.
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The nodes in the network are the people and groups while the links show relationships or flows between the nodes.

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.
Application of SNA: Building the 9/11 Al- Qaeda Network .

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Reduce Complexity
Geo-social networks
Integrating concepts from semantic web, social network, and knowledge management
Geo-social semantic web
Visualizing social networks
Security and Privacy
Mining and analysis of social networks
Predicting what the memebrs would do next

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Social Networks
Social Networks and 9/11 Terrorists
Social Networks and Baseball Drug Use
Social Networks and Expert Finder

SOCIAL NETWORKS
HTTP://WWW.FLAIRANDSQUARE.COM/ARCHI
VES/167
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A social network site allows people who share interests to build a
‘trusted’ network/ online community. A social network site will usually provide various ways for users to interact, such as IM (chat/ instant messaging), email, video sharing, file sharing, blogging, discussion groups, etc.

The main types of social networking sites have a ‘theme’, they allow users to connect through image or video collections online (like Flicker or You Tube) or music (like My Space, lastfm). Most contain libraries/ directories of some categories, such as former classmates, old work colleagues, and so on (like Face book, friends reunited, Linked in, etc).
They provide a means to connect with friends (by allowing users to create a detailed profile page), and recommender systems linked to trust.

SOCIAL NETWORK ANALYSIS OF 9/11 TERRORISTS
(WWW.ORGNET.COM)
Early in 2000, the CIA was informed of two terrorist suspects linked to al-Qaeda.
Nawaf Alhazmi and Khalid Almihdhar were photographed attending a meeting of known terrorists in Malaysia. After the meeting they returned to Los Angeles, where they had already set up residence in late 1999.

SOCIAL NETWORK ANALYSIS OF 9/11 TERRORISTS
What do you do with these suspects? Arrest or deport them immediately?
No, we need to use them to discover more of the al-Qaeda network.
Once suspects have been discovered, we can use their daily activities to uncloak their network. Just like they used our technology against us, we can use their planning process against them. Watch them, and listen to their conversations to see...
•who they call / email
•who visits with them locally and in other cities
•where their money comes from
The structure of their extended network begins to emerge as data is discovered via surveillance.

SOCIAL NETWORK ANALYSIS OF 9/11 TERRORISTS
A suspect being monitored may have many contacts -- both accidental and intentional. We must always be wary of 'guilt by association'. Accidental contacts, like the mail delivery person, the grocery store clerk, and neighbor may not be viewed with investigative interest.
Intentional contacts are like the late afternoon visitor, whose car license plate is traced back to a rental company at the airport, where we discover he arrived from Toronto (got to notify the
Canadians) and his name matches a cell phone number (with a Buffalo, NY area code) that our suspect calls regularly. This intentional contact is added to our map and we start tracking his interactions -- where do they lead? As data comes in, a picture of the terrorist organization slowly comes into focus.
How do investigators know whether they are on to something big? Often they don't. Yet in this case there was another strong clue that Alhazmi and Almihdhar were up to no good -- the attack on the
USS Cole in October of 2000. One of the chief suspects in the Cole bombing [Khallad] was also present [along with Alhazmi and Almihdhar] at the terrorist meeting in Malaysia in January 2000.
Once we have their direct links, the next step is to find their indirect ties -- the 'connections of their connections'. Discovering the nodes and links within two steps of the suspects usually starts to reveal much about their network. Key individuals in the local network begin to stand out. In viewing the network map in Figure 2, most of us will focus on Mohammed Atta because we now know his history.
The investigator uncloaking this network would not be aware of Atta's eventual importance. At this point he is just another node to be investigated.

Figure 2 shows the two suspects and
SOCIAL NETWORK ANALYSIS OF 9/11 TERRORISTS

Figure 2 shows the two suspects and
Atta's eventual importance. At this point he is just another node to be investigated.
SOCIAL NETWORK ANALYSIS OF 9/11 TERRORISTS

SOCIAL NETWORK ANALYSIS OF 9/11 TERRORISTS
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We now have enough data for two key conclusions:
All 19 hijackers were within 2 steps of the two original suspects uncovered in 2000!
Social network metrics reveal Mohammed Atta emerging as the local leader
With hindsight, we have now mapped enough of the 9-11 conspiracy to stop it. Again, the investigators are never sure they have uncovered enough information while they are in the process of uncloaking the covert organization. They also have to contend with superfluous data.
This data was gathered after the event, so the investigators knew exactly what to look for.
Before an event it is not so easy.
As the network structure emerges, a key dynamic that needs to be closely monitored is the activity within the network. Network activity spikes when a planned event approaches. Is there an increase of flow across known links? Are new links rapidly emerging between known nodes?
Are money flows suddenly going in the opposite direction? When activity reaches a certain pattern and threshold, it is time to stop monitoring the network, and time to start removing nodes.
The author argues that this bottom-up approach of uncloaking a network is more effective than a top down search for the terrorist needle in the public haystack -- and it is less invasive of the general population, resulting in far fewer "false positives".

Figure 2 shows the two suspects and
SOCIAL NETWORK ANALYSIS OF STEROID USAGE IN
BASEBALL (WWW.ORGNET.COM)
When the Mitchell Report on steroid use in Major League Baseball [MLB], was published, people were surprised at who and how many players were mentioned. The diagram below shows a human network created from data found in the Mitchell Report. Baseball players are shown as green nodes. Those who were found to be providers of steroids and other illegal performance enhancing substances appear as red nodes. The links reveal the flow of chemicals -from provider to player.

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SOCIAL NETWORKING FOR KNOWLEDGE MANAGEMENT
EXAMPLES
WWW.ORGNET.COM
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Managing the 21st Century Organization
Networks of Adaptive/Agile Organizations
Best Practice: Organizational Network Mapping
Discovering Communities of Practice
Data-Mining E-mail
Finding Leaders on your Team
Post-Merger Integration
Knowledge Sharing in Organizations
Innovation happens at the Intersections
Partnerships and Alliances in Industry
Decision-Making in Organizations
New Organizational Structures

Figure 2 shows the two suspects and
KNOWLEDGE SHARING NETWORK: FINDING EXPERTS
(WWW.ORGNET.COM)
Organizational leaders are preparing for the potential loss of expertise and knowledge flow due to turnover, downsizing, outsourcing, and the coming retirements of the baby boom generation. The model network (previous chart) is used to illustrate the knowledge continuity analysis process.
Each node in this sample network (previous chart) represents a person that works in a knowledge domain. Some people have more / different knowledge than others. Employees who will retire in 2 years or less have their nodes colored red. Those who will retire in 3-4 years are colored yellow.
Those retiring in 5 years or later are colored green.
A gray, directed line is drawn from the seeker of knowledge to the source of expertise. A-->B indicates that A seeks expertise / advice from B. Those with many arrows pointing to them are sought often for assistance.
The top subject matter experts -- SMEs -- in this group are nodes 29, 46, 100, 41, 36 and 55.
The SMEs were discovered using a network metric in InFlow that is similar to how the
Google search engine ranks web pages -- using both direct and indirect links.
Of the top six SMEs in this group, half are colored red[100] or yellow[46, 55]. The loss of person 46 has the greatest potential for knowledge loss. 90% of the network is within
3 steps of accessing this key knowledge source.

Figure 2 shows the two suspects and
KNOWLEDGE SHARING IN ORGANIZATIONS: FINDING
EXPERTS

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Detecting coalitions and subgroups
Conducting a political campaign
Marketing a drug by a pharmaceutical company
Forming a travel network
Many more - - - - -

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Introduction to Social Networks
Properties of Social Networks
Social Network Analysis Basics
Examples
Data Privacy Basics
Privacy and Social Networks

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Social networks have important implications for our daily lives.
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Spread of Information
Spread of Disease
Economics
Marketing
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Social network analysis could be used for many activities related to information and security informatics.
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Terrorist network analysis

* http://jheer.org/enron/

ROMANTIC RELATIONS AT “JEFFERSON HIGH SCHOOL”

“SMALL-WORLD” EXAMPLE: SIX DEGREES OF
KEVIN BACON

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Social network data is represented a graph
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Individuals are represented as nodes
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Nodes may have attributes to represent personal traits
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Relationships are represented as edges
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Edges may have attributes to represent relationship types
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Edges may be directed
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Common Social Network Mining tasks
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Node classification
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Link Prediction

Lindamood et al. 09 &
Heatherly et al. 09
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Graph represented by a set of homogenous vertices and a set of homogenous edges
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Each node also has a set of Details, one of which is considered private .

Lindamood et al. 09 &
Heatherly et al. 09
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Collection of techniques that use node attributes and the link structure to refine classifications.
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Uses local classifiers to establish a set of priors for each node
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Uses traditional relational classifiers as the iterative step in classification

Lindamood et al. 09 &
Heatherly et al. 09
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Class Distribution Relational Neighbor
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Weighted-Vote Relational Neighbor
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Network-only Bayes Classifier
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Network-only Link-based Classification

Lindamood et al. 09 &
Heatherly et al. 09
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167,000 profiles from the Facebook online social network
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Restricted to public profiles in the
Dallas/Fort Worth network
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Over 3 million links

Lindamood et al. 09 &
Heatherly et al. 09
Diameter of the largest component
Number of nodes
Number of friendship links
Total number of listed traits
Total number of unique traits
Number of components
Probability Liberal
Probability Conservative
16
167,390
3,342,009
4,493,436
110,407
18
.45
.55

Lindamood et al. 09 &
Heatherly et al. 09
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Details only: Uses Naïve Bayes classifier to predict attribute
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Links Only: Uses only the link structure to predict attribute
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Average: Classifies based on an average of the probabilities computed by Details and Links

Lindamood et al. 09 &
Heatherly et al. 09
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Attempt to predict the value of the political affiliation attribute
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Three Inference Methods used as the local classifier
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Relaxation labeling used as the Collective
Inference method

Lindamood et al. 09 &
Heatherly et al. 09
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Ensures that no ‘false’ information is added to the network, all details in the released graph were entered by the user
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Details that have the highest global probability of indicating political affiliation removed from the network

Lindamood et al. 09 &
Heatherly et al. 09
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Ensures that the link structure of the released graph is a subset of the original graph
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Removes links from each node that are the most like the current node

Trait Name Trait Value
Group
Group
Group
Group
Group
Group
Group legalize same sex marriage every time i find out a cute boy is conservative a little part of me dies equal rights for gays the democratic party not a bush fan people who cannot understand people who voted for bush government religion disaster
Lindamood et al. 09 &
Heatherly et al. 09
Weight Liberal
46.16066789
39.68599463
33.83786875
32.12011605
31.95260895
30.80812425
29.98977927

Trait Name
Group
Group
Group
Group
Group
Group
Group
Group
Group
Lindamood et al. 09 &
Heatherly et al. 09
Trait Value george w bush is my homeboy college republicans texas conservatives bears for bush kerry is a fairy aggie republicans keep facebook clean i voted for bush protect marriage one man one woman
Weight Conservative
45.88831329
40.51122488
32.23171423
30.86484689
28.50250433
27.64720818
23.653477
23.43173116
21.60830487

Lindamood et al. 09 &
Heatherly et al. 09
Trait Name activities
Employer favorite tv shows grad school hometown
Relationship Status religious views looking for
Trait Value amnesty international hot topic queer as folk computer science mumbai in an open relationship agnostic whatever i can get
Weight Liberal
4.659100601
2.753844959
9.762900035
1.698146579
3.566007713
1.617950632
3.15756412
1.703651985

Lindamood et al. 09 &
Heatherly et al. 09
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Conducted on 35,000 nodes which recorded political affiliation
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Tests removing 0 details and 0 links, 10 details and 0 links, 0 details and 10 links, and 10 details and 10 links
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Varied Training Set size from 10% of available nodes to 90%
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Results are documented in papers