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SOCIAL NETWORKS

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Title: SOCIAL NETWORKS


1
Knowledge Management, Semantic Web and Social
Networking Social Networks
Dr. Bhavani Thuraisingham
June 2010

2
Outline of Part I
  • 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
  • ce.sharif.edu/m_jamali/resources/WI06_SNA.ppt
    (WI 2006)
  • ic.ucsc.edu/wsack/fdm20c/fall2008/Lectures/social
    -networks.ppt

3
Social Networkshttp//www.flairandsquare.com/arch
ives/167
  • 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.

4
Popular Social Networks
  • 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, user-submitted network
    of friends, personal profiles, blogs, groups,
    photos, music and videos internationally.
    According to AlexaInternet, MySpace is currently
    the worlds 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.

5
Social networks Interdisicplinary Field
  • 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)

6
Social Networks History
  • 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).

7
Social networks as technology
  • email, newsgroups, and weblogs
  • search engines e.g., Google (http//google.com)
  • Googles Page Rank algorithm gives more weight to
    popular webpages.
  • A webpage is considered popular if many other
    webpages link to it.
  • collaborative filtering and/or recommender
    systems e.g., amazon.coms feature People who
    bought this book also bought...

8
Technology LinkedIn
  • 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.
  • How do you classify users?
  • Your Network contains professionals out to
    three degrees that is, friends-of-friends-of-f
    riends. If each person had 10 connections (and
    some have many more) then your network would
    contain 10,000 professionals.
  • How do you see who is in your Network?
  • LinkedIn lets you see your network as one large
    group of searchable professional profiles.

9
Social networks as popular culture
  • e.g., six degrees of kevin bacon
  • bacon number definition http//en.wikipedia.org/w
    iki/Six_Degrees_of_Kevin_Bacon
  • 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
  • . social software e.g., facebook, friendster,
    orkut,

10
Social Networks More formal definition
  • A structural approach to understanding social
    interaction.
  • 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.

11
Social Network Examples
  • Effects of urbanization on individual well-being
  • 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

12
Social Networks Basic Questions
  • 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 areinterchangable in the network.

13
Social Networks Basic Questions
  • 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
    1970s, e.g. eigenvalue centrality by Bonacich.
  • Most of these measures were rediscovered as
    quality measures for web pages
  • Indegree
  • Pagerank eigenvalue centrality
  • HITS ? two-mode eigenvalue centrality

14
Social Network Concepts
  • Actor
  • An actor is a basic component for SNs. Actors
    can be
  • Individual people, Corporations, Nation-States,
    Social groups
  • Modes
  • If all the actors are of the same type, the
    network is called a one-mode network. If there
    are two groups of actor then it is a two-mode
    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
  • A tie is the relation between two actors. Common
    types of ties include
  • Friendship, Amount of communication, Goods
    exchanged, Familial relation (kinship),
    Institutional relations

15
Practical issues Boundaries and Samples
  • 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

16
The Web as a Social Network
  • 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
  • Social Network Extraction from the Web
  • Social Network Analysis
  • Social Networking Services (SNS).
  • Friendster Orkut

17
Inferring Communities in Web
  • Bibliographic Metrics
  • bibliographic coupling
  • co-citation coupling

18
Blogsphere as a Social Network
  • Weblogs have become prominent social media on the
    Internet that enable users to quickly and easily
    publish content including highly personal
    thoughts.
  • Bloggers might list one anothers 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).

19
Semantic Web and Social Network
  • 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
  • SW and SN models support each other
  • 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

20
Semantic Web and Social Network
  • 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 foafPerson 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.

21
SW and SNA (issues)
  • Knowledge representation.
  • 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

22
Social networks and KMS
  • Why Social Networks in KMS?

People
KM
Organization Processes
Technology
Knowledge Management involves people, technology,
and processes in Overlapping parts.
23
Social Networks and KMS
  • Why are we studying Social Networks ?

What ties Information Architecture, Knowledge
Management and Social Network Analysis more
closely together is the reciprocal relationship
between people and content.
24
Social Network Analysis
  • Social network analysis SNA is the mapping and
    measuring of relationships and flows between
    people, groups, organizations, computers or other
    information/knowledge processing entities.
  • The nodes in the network are the people and
    groups while the links show relationships or
    flows between the nodes.

25
Social Network Analysis (SNA)
  • 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.
26
Directions
  • 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

27
Outline of Part II
  • Social Networks
  • Social Networks and 9/11 Terrorists
  • Social Networks and Baseball Drug Use
  • Social Networks and Expert Finder

28
Social Networkshttp//www.flairandsquare.com/arch
ives/167
  • 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.

29
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.
30
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.

31
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.
32
Social Network Analysis of 9/11 Terrorists
Figure 2 shows the two suspects and
33
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.
                                                  
                                                  
                                                  
                                                  
         Figure 3 shows the direct
34
Social Network Analysis of 9/11 Terrorists
  • 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".

35
Social Network Analysis of Steroid Usage in
Baseball (www.orgnet.com)
Figure 2 shows the two suspects and
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.
36
Social Networking for Knowledge Management
Exampleswww.orgnet.com
  • 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

37
Knowledge Sharing Network Finding Experts
(www.orgnet.com)


Figure 2 shows the two suspects and
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--gtB 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 red100
or yellow46, 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.
38
Knowledge Sharing in Organizations Finding
Experts


Figure 2 shows the two suspects and
39
Other Applications
  • Detecting coalitions and subgroups
  • Conducting a political campaign
  • Marketing a drug by a pharmaceutical company
  • Forming a travel network
  • Many more - - - - -

40
Outline of Part IV
  • Introduction to Social Networks
  • Properties of Social Networks
  • Social Network Analysis Basics
  • Examples
  • Data Privacy Basics
  • Privacy and Social Networks

41
Social Networks
  • Social networks have important implications for
    our daily lives.
  • Spread of Information
  • Spread of Disease
  • Economics
  • Marketing
  • Social network analysis could be used for many
    activities related to information and security
    informatics.
  • Terrorist network analysis

42
Enron Social Graph
http//jheer.org/enron/
43
Social Networks
44
Romantic Relations at Jefferson High School
45
Small-World Example Six Degrees of Kevin Bacon
46
Social Network Mining
  • Social network data is represented a graph
  • Individuals are represented as nodes
  • Nodes may have attributes to represent personal
    traits
  • Relationships are represented as edges
  • Edges may have attributes to represent
    relationship types
  • Edges may be directed
  • Common Social Network Mining tasks
  • Node classification
  • Link Prediction

47
Some Experimental Results
  • Raymond Heatherly, Murat Kantarcioglu, and
    Bhavani ThuraisinghamThe University of Texas at
    Dallas
  • Jack LindamoodFacebook

48
Graph Model
Lindamood et al. 09 Heatherly et al. 09
  • Graph represented by a set of homogenous vertices
    and a set of homogenous edges
  • Each node also has a set of Details, one of which
    is considered private.

49
Collective Inference
Lindamood et al. 09 Heatherly et al. 09
  • Collection of techniques that use node attributes
    and the link structure to refine classifications.
  • Uses local classifiers to establish a set of
    priors for each node
  • Uses traditional relational classifiers as the
    iterative step in classification

50
Relational Classifiers
Lindamood et al. 09 Heatherly et al. 09
  • Class Distribution Relational Neighbor
  • Weighted-Vote Relational Neighbor
  • Network-only Bayes Classifier
  • Network-only Link-based Classification

51
Experimental Data
Lindamood et al. 09 Heatherly et al. 09
  • 167,000 profiles from the Facebook online social
    network
  • Restricted to public profiles in the Dallas/Fort
    Worth network
  • Over 3 million links

52
General Data Properties
Lindamood et al. 09 Heatherly et al. 09
Diameter of the largest component 16
Number of nodes 167,390
Number of friendship links 3,342,009
Total number of listed traits 4,493,436
Total number of unique traits 110,407
Number of components 18
Probability Liberal .45
Probability Conservative .55
53
Inference Methods
Lindamood et al. 09 Heatherly et al. 09
  • Details only Uses Naïve Bayes classifier to
    predict attribute
  • Links Only Uses only the link structure to
    predict attribute
  • Average Classifies based on an average of the
    probabilities computed by Details and Links

54
Predicting Private Details
Lindamood et al. 09 Heatherly et al. 09
  • Attempt to predict the value of the political
    affiliation attribute
  • Three Inference Methods used as the local
    classifier
  • Relaxation labeling used as the Collective
    Inference method

55
Removing Details
Lindamood et al. 09 Heatherly et al. 09
  • Ensures that no false information is added to
    the network, all details in the released graph
    were entered by the user
  • Details that have the highest global probability
    of indicating political affiliation removed from
    the network

56
Removing Links
Lindamood et al. 09 Heatherly et al. 09
  • Ensures that the link structure of the released
    graph is a subset of the original graph
  • Removes links from each node that are the most
    like the current node

57
Most Liberal Traits
Lindamood et al. 09 Heatherly et al. 09
Trait Name Trait Value Weight Liberal
Group legalize same sex marriage 46.16066789
Group every time i find out a cute boy is conservative a little part of me dies 39.68599463
Group equal rights for gays 33.83786875
Group the democratic party 32.12011605
Group not a bush fan 31.95260895
Group people who cannot understand people who voted for bush 30.80812425
Group government religion disaster 29.98977927
58
Most Conservative Traits
Lindamood et al. 09 Heatherly et al. 09
Trait Name Trait Value Weight Conservative
Group george w bush is my homeboy 45.88831329
Group college republicans 40.51122488
Group texas conservatives 32.23171423
Group bears for bush 30.86484689
Group kerry is a fairy 28.50250433
Group aggie republicans 27.64720818
Group keep facebook clean 23.653477
Group i voted for bush 23.43173116
Group protect marriage one man one woman 21.60830487
59
Most Liberal Traits per Trait Name
Lindamood et al. 09 Heatherly et al. 09
Trait Name Trait Value Weight Liberal
activities amnesty international 4.659100601
Employer hot topic 2.753844959
favorite tv shows queer as folk 9.762900035
grad school computer science 1.698146579
hometown mumbai 3.566007713
Relationship Status in an open relationship 1.617950632
religious views agnostic 3.15756412
looking for whatever i can get 1.703651985
60
Experiments
Lindamood et al. 09 Heatherly et al. 09
  • Conducted on 35,000 nodes which recorded
    political affiliation
  • Tests removing 0 details and 0 links, 10 details
    and 0 links, 0 details and 10 links, and 10
    details and 10 links
  • Varied Training Set size from 10 of available
    nodes to 90
  • Results are documented in papers
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