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Social Networks and the Semantic Web

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Title: Social Networks and the Semantic Web


1
Social Networks and the Semantic Web
  • Peter Mika
  • BI/FEW, BO/FSW
  • Vrije Universiteit, Amsterdam

2
Contents
  • Motivation I.
  • The explosive mix of social networks and the Web
  • A brief history of Network Science
  • Social Network Analysis
  • SNA in entrepreneurship
  • Motivation II.
  • Two problems in search of a solution
  • Contributions

3
Motivation I.
  • Excitement two vibrant application areas
    converging rapidly
  • Social networks meet the Web
  • The Semantic Web meets social networks

Hardly a surprise personal information made the
first Web
4
Social networks meet the Web
  • Making Friendsters in High Places (Wired News,
    July 17, 2003)
  • Will Microsoft Wallop Friendster? (Wired News,
    Nov 8, 2003)
  • Social Nets Find Friends in VCs (Wired News, Nov
    17, 2003)
  • Google spawns social networking service (CNET,
    Jan 22, 2004)
  • The Technology of the Year Social Network
    Applications (Business 2.0, November 2003)
  • And the backlash
  • Social Nets Not Making Friends (Wired News, Jan
    28, 2004)
  • Too many confirmations to answer
  • Pretendsters, fakesters
  • No clear goal
  • Different definitions of friendship etc.
  • Next generation social networks AND

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10
Links
  • Friendship-network sites
  • www.orkut.com
  • www.friendster.com (over 5 million users)
  • www.linkedin.com
  • www.tribe.net
  • www.tickle.com (formerly Emode)
  • www.ryze.com
  • www.flickr.com
  • www.wiw.hu
  • Weblogs
  • Paolo Massa's blog
  • Clay Shirky's blog
  • Judith Meskill's blog

11
Friend-of-a-Friend
  • Friend-of-a-Friend (FOAF) a standard vocabulary
    for recording personal information in a machine
    readable format (RDF)
  • FOAF documents contain information such as
  • name
  • homepage
  • image
  • depiction
  • interests
  • projects
  • publications
  • memberships
  • etc.

http//www.foaf-project.org/
12
Example
  • ltfoafPerson rdfIDFrankvHgt
  • ltfoafnamegt
  • Frank van Harmelen
  • lt/foafnamegt
  • ltfoafmbox_sha1sumgt
  • 241021fb0e6289f92815fc210f9e9137262c252e
  • lt/foafmbox_sha1sumgt
  • ltfoafhomepage rdfresource"http//www.cs.vu.nl/
    frankh" /gt
  • ltfoafimg rdfresource"http//www.cs.vu.nl/fran
    kh/figs/FvH-2003.jpg" /gt
  • ltfoafknows rdfresourcehttp//...HansAkkerman
    s /gt
  • ltfoafknows rdfresourcehttp//...PeterMika
    /gt
  • lt/foafPersongt

Thousands of these documents exist already on the
(Semantic) Web. All of them are linked together
through the knows relationship. This network is
also known as the FOAF-web.
13
Applications
  • FOAF Explorer Text browser
  • FOAF Naut graphical browser
  • Codepiction browse the network in images

14
foafnaut
15
Codepiction
16
Codepiction
17
Network Science
  • Graph theory meets real life
  • Models to capture what is common of networks
    observed in the physical world
  • Part of complex systems research in physics
  • Broad applications
  • Social Network Analysis
  • Biology
  • Chemistry
  • Engineering

18
Graph theoretical concepts
  • Degree of a node
  • number of incoming / outgoing links
  • Average Shortest Path, lav
  • average shortest path over all pairs of vertices
    between which a path exists.
  • Clustering Coefficient, C
  • The clustering coefficient, C, represents the
    average fraction of existing connections between
    nearest neighbours of a vertex.
  • Relative size of the largest component, S
  • The relative size of the largest component is
    simply the size of the largest component divided
    by the total number of nodes.

19
Networks and graphs
  • Euler (1736) bridges of Königsberg
  • Later, Cauchy, Hamilton,
  • Cayley,Kirchhoff, Pólya et al.
  • Regular graphs
  • Each node has the same degree
  • High clustering, high paths lengths
  • e.g. the lattice of atoms
  • Nothing particular happened in the next 200 years

20
Erdos-Rényi
  • Eight papers on Random graphs (1959)
  • Given a fixed set of nodes.
  • Choose two nodes and if you roll six with a dice,
    place a link between them.
  • Poisson degree distribution (Not regular, but in
    large networks all nodes will have an average
    degree.)
  • Low clustering, low paths lengths
  • Explains important network phenomena
  • six degrees of separation (next)
  • the emergence of a giant component
  • A tipping point when the average degree in a
    graph reaches 1 a giant cluster emerges
  • Physics percolation or phase transition
  • Dominates thinking over complex networks until
    recently

21
Poisson distribution
22
Power-law distribution
  • Example Top 50 terms in TIME articles
  • Also called Zipf-law in linguistics


23
Six degrees
  • The (Stanley) Milgram experiment (1967)
  • Two targets
  • a stock broker in Boston, MA and the wife of a
    divinity grad student in Sharon, MA
  • Starting points
  • random people in Wichita, Kansas and Omaha,
    Nebraska
  • Chain letter to forward to a personal
    acquaintance who is more likely to know the
    target person
  • 42 of the 160 letters make it back, average chain
    is 5.5 long (overestimation!)
  • six degrees of separation, small world
  • Avg. distance increases logarithmically with
    network size
  • Species in food webs (2), molecules in the cells
    (3), neurons in the brain of C. Elegans (14), The
    Web (19)

24
Granovetter
  • Granovetter (1969) The strength of weak ties
  • How people network (use their social
    connections) to find a job?
  • Mostly through acquaintances (weak ties). Close
    friends are also friends of each other -gt unable
    to provide reach, access to new information
  • A small and clustered world densely knit network
    of close friends acquaintances connecting them
  • Impossible to explain by Erdos-Rényi (random
    graphs)
  • high degree of clustering

25
Watts Strogatz
  • Watts-Strogatz model (Nature, 1998)
  • First successful attempt to reconcile clustering
    and random graphs
  • Very few extra links cut back the separation
    drastically, while not alter the clustering
    coefficient significantly

26
Barabási
  • The Barabási Model scale-free networks
  • Growth Starting with a small number of nodes, at
    every time step, add a new node with m edges that
    link to m nodes already present in the system.
  • Preferential attachment When choosing the nodes
    to which the new node connects, assume that the
    probability that a new node will be connected to
    a particular node depends on the degree of that
    node. Highly connected nodes are more likely to
    become even more connected. (The rich get
    richer, first movers advantage)
  • The result is a network with a few highly
    connected nodes (hubs), while a considerable
    proportion of nodes have a degree of only one or
    two. In fact, the degree distribution ends up
    following a power law. (P(k) k-3)

27
Networks as competitive systems
  • Barabási number of links is a function of time.
    First movers take all.
  • How could new kids on the block succeed in the
    world of Barabási?
  • Google, Boeing, Palm
  • Barabási each node has a fitness.
  • Preferential attachment is driven by the fitness
    connectivity product (fitness links)
  • Speed of acquiring links is governed by fitness
  • Still a power law?

28
Evolution
  • Evolution of a network depends on the fitness
    distribution
  • In fit-get-rich networks, power structure
    survives at all moments, the winners lead is
    never significant
  • In some networks, the winner-takes-all. The
    fittest node gets almost all links, leading to a
    star topology. (1 hub, many tiny nodes)
  • Example Microsoft ?

29
Criticism to the state-of-the-art
  • Does this help us as individuals?
  • Path finding is local search in real life
  • We have 150 weak ties on average
  • Would George Bush lend me his car?
  • Not all ties are equal
  • Effects of similarity, proximity lacking from
    models

30
Implications for architectures
  • Network Robustness
  • Error refers to random node failure.
  • Attack refers to preferential removal of the
    most connected nodes.
  • Robustness can be measured as the relative size,
    S, and the average shortest path, lav, of the
    largest cluster.
  • Simulation shows that scale free networks are
    more robust to error but less robust to attack
    than both random and exponential networks.

31
Social Network Analysis
  • Social Network Analysis (SNA) is the study of
    social relations among a set of actors. 1 2
  • Network analysis is distinguished from other
    fields of sociology by a focus on relationships
    between actors rather than attributes of actors.
  • sense of interdependence a molecular rather than
    atomistic view (network view)
  • belief that structure affects substantive
    outcomes (emergent effects)
  • SNA focuses on the various roles individuals play
    in social networks, the various kinds of
    relations that may exist and looks for
    viable/most efficient structures. SNA also scored
    successes in explaining network dynamics such as
    the spread of epidemics, fashions, inventions,
    technologies etc.

32
Network research in Entrepreneurship
  • Application of SNA to entrepreneurial (business)
    networks
  • i.e. nodes are entrepreneurs or their firms,
    links are business relations
  • Applications to Business Venturing, Management,
    Strategy, Organizational Studies
  • Key concept embeddedness
  • Entrepreneurs are said to be embedded in their
    social network
  • Business ties are said to be relationally
    embedded
  • Focus on enterprise formation period or small to
    medium enterprises
  • Investigates how certain properties of networks
    effect the success or failure of the enterprise
  • Financial success
  • Innovativeness

33
Research foci
  • Network content
  • Information and advice
  • Emotional support
  • Legitimation
  • Relationship governance
  • Trust
  • Power and influence
  • Threat of ostracism and loss of reputation
  • Network structure
  • Structural holes (Burt)
  • Weak vs. Strong ties (Granovetter)
  • Network as independent vs. network as dependent
    variable

34
Findings
  • Embeddedness as a result of local search
  • Lack of information, lack of resources to pursue
    broad searches
  • previous linkages guide partner selection
  • Social identity theory similarity strengthens
    self-image, similar people are treated more
    favorably
  • Similarity can be cause (precondition) or result
    similarity breeds interaction vs. interaction
    breeds similarity

35
Two sides of embeddedness
  • Enabling effect of strong ties
  • Strong ties result in better information
    sharing/concerted acting, lower resistance
    (trustworthiness), comfort (familiarity),
    sometimes limiting of sharing (prevent leakage)
  • Especially if first mover partners become tied
    up later
  • Constraining effect of strong ties
  • Over-embeddedness actors become locked-in,
    homogeneity (similarity) increases
  • makes it more difficult to break out (discover
    information, resource niches) and diversify
    (learn, innovate)
  • non-group members increasingly isolated
  • No possibility for gaining advantage by brokerage
    (Burt)
  • Contingency
  • Different mix of strong vs. weak ties is
    appropriate
  • at different stages of the enterprise
  • for different activities

36
Motivation II.
  • Two problems in search of a common solution
  • How can the Semantic Web benefit from a machine
    understanding of the social networks of agents?
  • In what way can Social Network science benefit
    from Semantic Web research and technology?

37
Problem cluster the SW perspective
  • An ontology is a shared, formal conceptualization
    of a domain Gruber93.
  • Ontologies build upon a shared understanding
    within a community.
  • This understanding represents an agreement of
    experts over the concepts and relationships that
    are present in a domain.
  • The social factor in ontology-based KM.
  • Ontologies are expressed in machine processable,
    logic-based representations.
  • This allows computers to manipulate ontologies.
  • The machine factor in ontology-based KM.
  • Yet, current SW technology ignores the social
    aspects of knowledge.
  • Result hot potatoes, scalability problems

38
Hot potatoes
  • Which one of these are considered difficult
    problems in ontology research?
  • Ontology acquisition (automated)
  • Ontology development (manual)
  • Ontology evaluation and measures of ontology
    quality
  • Ontology representation
  • Ontology query
  • Ontology-based platforms
  • MAS, P2P, Grid, Web Services
  • Ontology management
  • Ontology storage
  • Ontology-based reasoning
  • Ontology versioning
  • Ontology alignment, merging and mapping
  • Ontology presentation and visualization
  • Ontology-based search
  • Ontology-based query
  • Ontology-based collaboration
  • And what do they have in common?

39
Scalability problems
  • Ontologies for Information Management Balancing
    Formality, Stability, and Sharing Scope
    ElstAbecker2001

40
What does this mean for the Semantic Web?
41
Contingency
  • An architecture of the Semantic Web that reflects
    the social nature of communication and knowledge

42
Problem clusterthe Social Science perspective
  • A characteristic (and to some critics, a
    weakness) of research on networks is the lack of
    a core theory that in turn yields a set of
    well-defined propositions from which network
    constructs are defined
  • The result is a loose federation of approaches
    (Burt, 1980) in which researchers often debate
    how concepts are operationalized rather than the
    underlying theoretical arguments themselves
    (Hoang and Antoncic, 2003)
  • Result propositions are difficult to compare for
    their dependence on operationalization

43
Contribution
  • A core ontology of social networks and
    relationships
  • Formulate network theories in a semi-formal
    manner, explicate link between theory and case
    study data
  • Case study the Semantic Web research community
  • Action-oriented
  • Beyond FOAF Towards a social structure for the
    Semantic Web

44
The End
  • Your questions, my pleasure.

45
Visualizations
  • Standalone tools
  • Pajek
  • http//vlado.fmf.uni-lj.si/pub/networks/pajek/
  • NetMiner (commercial)
  • http//www.netminer.com
  • 3D
  • Kinemages (3D data format)
  • http//kinemage.biochem.duke.edu/
  • several layout softwares
  • Developers tools
  • CAIDA (several tools)
  • http//www.caida.org/tools/visualization/
  • TouchGraph (Java API)
  • http//www.touchgraph.com/index.html
  • http//touchgraph.sourceforge.net/
  • Jung (Java API for network analysis)
  • http//jung.sourceforge.net/
  • ATT GraphViz (layout sw, C/C)
  • http//www.research.att.com/sw/tools/graphviz/
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