the network: a data structure that links domains
TRANSCRIPT
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The Network:A Data Structure that Links Domains
Marko A. Rodriguez
Los Alamos National LaboratoryVrije Universiteit Brussel
University of California at Santa Cruz
http://www.soe.ucsc.edu/~okram
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
About me.
• Marko Antonio Rodriguez.
• Bachelors of Science in Cognitive Science from U.C. San Diego.• Minor in the Arts in Computer Music from U.C. San Diego.• Masters of Science in Computer Science from U.C. Santa Cruz.• Visiting Researcher at the Center for Evolution, Complexity, and
Cognition at the Free University of Brussels.• Ph.d. in Computer Science from U.C. Santa Cruz [soon].
o I defend November 15, 2007!• Researcher at the Los Alamos National Laboratory since 2005.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Research trends.
• MESUR: Metrics from Scholarly Usage of Resources.(http://www.mesur.org)
• Neno/Fhat: A Semantic Network Programming Language and VirtualMachine Architecture. (http://neno.lanl.gov)
• CDMS: Collective Decision Making Systems. (http://cdms.lanl.gov)
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
What is a network?
• A network is a data structure that is used to connect vertices/nodes/dots bymeans of edges/links/lines.
• Networks are everywhere.o Social: friendship, trust, communication, collaboration.o Technological: web-pages, communication, software dependencies, circuits.o Scholarly: journals, authors, articles, institutions.o Natural: protein interaction, neural, food web.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The undirected network.
• There is the undirected network of common knowledge.o Sometimes called an undirected single-relational network.o e.g. vertex i and vertex j are “related”.
• The semantic of the edge denotes the network type.o e.g. friendship network, collaboration network, etc.
i j
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Example undirected network.
Herbert
Marko
Aric
Ed
Zhiwu
AlbertoJen
Johan
Luda
Stephan
Whenzong
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The directed network.
• Then there is the directed network of common knowledge.o Sometimes called a directed single-relational network.o For example, vertex i is related to vertex j, but j is not related to i.
i j
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Example directed network.
Muskrat
Bear
Fish
Fox
Meerkat
LionHuman
Wolf
Deer
Beetle
Hyena
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The semantic network.
• Finally, there is the semantic networko Sometimes called a directed multi-relational network.o For example, vertex i is related to vertex j by the semantic s, but j is not
related to i by the semantic s.
i js
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Example semantic network.
SantaFe
Marko
NewMexico
Ryan
California
UnitedStatesLANL
livesInworksWith
cityOf
originallyFrom
stateOfstateOf
locatedIn
hasLab
Cells
Atoms
madeOf
madeOf
researches
Oregon
southOf
hasResident
Arnold
governerOf
northOf
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
What are the techniques for analysis?
• Degree statisticso How many in- and out-edges does vertex i have?o What is the maximum and minimum in- and out-degree of the network?
• Shortest-path metricso What is the smallest number of steps to get from vertex i to vertex j?o How many of the shortest-paths go through vertex i?
• Power metricso What vertices are the most “influential”?
• Metadata distributionso What is the probability that a vertex of type x is connected to a vertex of type y?
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Degree statistics.
Muskrat
Bear
Fish
Fox
Meerkat
LionHuman
Wolf
Deer
Beetle
Hyena
out = 4in = 0
out = 1in = 1
out = 0in = 2
out = 2in = 1
out = 1in = 1
out = 1in = 1
out = 3in = 0
out = 0in = 4
out = 0in = 1
out = 1in = 0
out = 1in = 3
Max_out = 4Max_in = 4Min_out = 0Min_in = 0
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Shortest-path between Marko and Aric.
Herbert
Marko
Aric
Ed
Zhiwu
AlbertoJen
Johan
Luda
Stephan
Whenzong
Shortest path = 1
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Eccentricity of Marko.
Herbert
Marko
Aric
Ed
Zhiwu
AlbertoJen
Johan
Luda
Stephan
Whenzong
Eccentricity = 3
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Radius of the network.
Herbert
Marko
Aric
Ed
Zhiwu
AlbertoJen
Johan
Luda
Stephan
Whenzong
Radius = 3
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Diameter of the network.
Herbert
Marko
Aric
Ed
Zhiwu
AlbertoJen
Johan
Luda
Stephan
Whenzong
Diameter = 4
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Closeness of Marko.
Herbert
Marko
Aric
Ed
Zhiwu
AlbertoJen
Johan
Luda
Stephan
Whenzong
Closeness = 0.0526
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Shortest-path metrics.
Shortest-path Eccentricity
DiameterRadius
Closeness Betweenness
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Power metrics.
• Eigenvector Centralityo Rank vertices according to the primary eigenvector of the adjacency matrix
representing the network.o In the language of Markov chains, find the stationary probability distribution of the
chain.
• PageRanko Ensure a real-valued ranking by introducing a “teleportation-network” which
ensures strong connectivity (used by Google).
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The components to calculate astationary probability distribution.
• Take a single “random walker”.
• Place that random walker on any random vertex in the network.
• At every time step, the random walker transitions from its current node to anadjacent node in the network (i.e. takes a random outgoing edge from its currentnode.)
• Anytime the random walker is at a node, increment a “times visited” counter by 1.
• Let this algorithm run for an “infinite” amount of time.
• Normalize the “times visited” counters.o That is your centrality vector.
a
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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66785
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66784
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Random walker example.
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c
b
d
0.167
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0.167
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
PageRank.
• The random walker has 0.85 probability of using G as its propagation networkand a 0.15 probability of using H as its propagation network (Google’s publishedalpha value).
• Every node is reachable by every other node and thus, is strongly connected.• A strongly connected network guarantees a stationary probability distribution.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Metadata distribution metrics.
• Scalar Assortativityo What’s the probability of encountering a node with degree x?o What’s the probability of encountering a node with degree x that is
connected to a node of degree y?o What’s the probability of encountering a node with degree x that is
connected to a node of degree y that is connected to a node of degree z?o …
• Discrete Assortativityo What’s the probability of encountering a node with metadata x?o What’s the probability of encountering a node with metadata x that is
connected to a node of metadata y?o What’s the probability of encountering a node with metadata x that is
connected to a node of metadata y that is connected to a node of metadataz?
o …
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The CENS network dataset.
• Center for Embedded Network Sensing at U.C. Los Angeles.• “An interdisciplinary and multi-institutional venture, CENS involves hundreds of
faculty, engineers, graduate student researchers, and undergraduate studentsfrom multiple disciplines at the partner institutions of University of California atLos Angeles (UCLA), University of Southern California (USC), University ofCalifornia Riverside (UCR), California Institute of Technology (Caltech),University of California at Merced (UCM), and California State University at LosAngeles (CSULA).”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Everything is metadata.
Marko
Affilation LANLDepartment Research LibraryGender MaleJobRank Ph.D. StudentBuidling P362Lab Prototyping TeamAdvisor Johan BollenDegree 2
…. ….
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The CENS coauthorship network.
• UCLA - red• USC - orange• Coventry - green• …
• Ph.D. - ellipse• Professor - hexagon• …
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
1st-order degree distributions in CENS coauthorship network.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
2nd-order degree distributions in CENS coauthorship network.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
2nd-order degree assortativity in CENS coauthorship network.
• Pearson correlation on edge degrees.o r \in [-1,1]
• r = 0.212
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
2nd-order degree assortativity in other networks.
• Physics coauthorship: 0.363• Biology coauthorship: 0.127• Mathematics coauthorship: 0.120• Film actor collaborations: 0.208
• Internet: -0.189• World Wide Web: -0.065
• Neural network: -0.163• Marine food web: -0.247
• Random graph: 0.0• Regular graph: 1.0
Newman, M.J., “Assortative Mixing in Networks”, Physical Review Letters, 89(20), 2002.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Metadata path frequencies.
• 1064.0 UCLA UCLA• 442.0 USC USC• 336.0 USC UCLA• 76.0 MIT UCLA• 58.0 UCLA UCM• 32.0 Caltech UCLA
• 376.0 Phd Professor• 254.0 Phd Researcher• 242.0 Researcher Professor• 184.0 Phd Phd• 142.0 Professor Professor
• 1186.0 Male Male• 508.0 Male Female• 78.0 Female Female
• 304.0 US US• 156.0 India US• 70.0 India India• 58.0 US China• 36.0 India China• 28.0 China China• 24.0 US Italy• 18.0 Iran India• 14.0 Iran US• 12.0 Greece India
• 750.0 CS CS• 388.0 EE CS• 340.0 EE EE• 84.0 CS CivilEng• 78.0 Biology CS• 74.0 CivilEng CivilEng
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
2nd-order metadata assortativity in CENS coauthorshipnetwork.
• 0.696 Gender• 0.641 Affiliation• 0.513 Department• 0.482 Advisor• 0.426 Lab• 0.319 Building• 0.290 Origin• 0.168 JobRank• 0.042 Room
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
3rd-order metadata assortativity in CENS coauthorshipnetwork.
• 0.471 Gender• 0.435 Affiliation• 0.290 Department• 0.225 Origin• 0.207 Advisor• 0.195 Lab• 0.170 Building• 0.032 JobRank• 0.004 Room
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Metadata compression.
• 229.0 US:Male US:Female• 228.0 US:Male US:Male• 197.0 US:Male India:Male• 121.0 India:Male India:Male• 76.0 India:Male US:Female• 36.0 US:Male China:Male• 30.0 US:Female US:Female• 21.0 Taiwan:Male China:Male• 19.0 US:Male Italy:Male• 18.0 India:Male South Korea:Male• 17.0 India:Male China:Male• 17.0 India:Male Australia:Male• 16.0 China:Male China:Male• 16.0 India:Male Greece:Male• 16.0 US:Male Mexico:Male
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
3rd-order metadata compression.
• 1402.0 UCLA:Phd UCLA:Professor UCLA:Phd• 879.0 UCLA:Researcher UCLA:Professor UCLA:Phd• 605.0 UCLA:Professor UCLA:Professor UCLA:Phd• 512.0 UCLA:Researcher UCLA:Professor UCLA:Researcher• 380.0 UCLA:Researcher UCLA:Professor UCLA:Professor• 304.0 USC:Phd UCLA:Professor UCLA:Phd• 294.0 USC:Phd USC:Professor USC:Phd• 272.0 UCLA:Phd UCLA:Phd UCLA:Phd• 270.0 UCLA:Professor UCLA:Phd UCLA:Phd
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Breather.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Example semantic network.
SantaFe
Marko
NewMexico
Ryan
California
UnitedStatesLANL
livesInworksWith
cityOf
originallyFrom
stateOfstateOf
locatedIn
hasLab
Cells
Atoms
madeOf
madeOf
researches
Oregon
southOf
hasResident
Arnold
governerOf
northOf
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
What is the Semantic Web?
• The figurehead of the Semantic Web initiative, Tim Berners-Lee, describes theSemantic Web as
o “... an extension of the current web in which information is given well-definedmeaning, better enabling computers and people to work in cooperation.”
• Perhaps not the best definition. It implies a particular application space--namelythe “web metadata and intelligent agents” space.
• My definition is that the Semantic Web iso “a highly-distributed, standardized semantic network data model--a URG (Uniform
Resource Graph). It’s a uniform way of graphing resources.”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
What is a resource?
• Resource = Anything.o Anything that can be identified.
• The Uniform Resource Identifier (URI):o <scheme name> : <hierarchical part> [ ? <query> ] [ # <fragment> ]
- http://www.lanl.gov- urn:uuid:550e8400-e29b-41d4-a716-446655440000- urn:issn:0892-3310- http://www.lanl.gov#MarkoRodriguez
– prefix it to make it easier on the eyes -- lanl:MarkoRodriguez
• The Semantic Webo “first identify it, then relate it!”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The technologies of the Semantic Web.
• Resource Description Framework (RDF): The foundation technology of theSemantic Web. RDF is a highly-distributed, semantic network data model. InRDF, URIs and literals (e.g. ints, doubles, strings) are related to one another intriples.
o <lanl:marko> <lanl:worksWith> <lanl:jhw>o <lanl:jhw> <lanl:wrote> <lanl:LAUR-07-2028>o <lanl:LAUR-07-2028> <lanl:hasTitle> “Web-Based Collective Decision Making
Systems”^^<xsd:string>
• RDF Schema (RDFS): The ontology is to the Semantic Web as the schema is tothe relational database.
o “Anything of rdf:type lanl:Human can lanl:drive anything of rdf:type lanl:Car.”
• Triple-Store: The triple-store is to semantic networks what the relationaldatabase is to the data table.
o a.k.a. semantic repository, graph database, RDF database.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
RDF and RDFS.
lanl:marko lanl:cookie
lanl:Human lanl:Food
lanl:isEatingrdf:type rdf:type
lanl:isEating
rdfs:domainrdfs:range
ontologyinstance
RDF is not a syntax. It’s a data model. Various syntaxes exist to encode RDF including RDF/XML, N-TRIPLE, TRiX, N3, etc.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
The triple-store.
SELECT ?a ?c WHERE { ?a type human ?a wrote ?b ?b type article ?c wrote ?b ?c type human ?a != ?c }
• There are two primary ways to distribute information on the Semantic Web.o 1.) publish RDF/XML document on a web server.o 2.) expose a public interface to an RDF triple-store.
• The triple store is to semantic networks what the relational database is to datatables.
o Storing and querying triples in a triple store.o SPARQL\Update query language.
- like SQL, but for triple-stores.
INSERT ?a coauthor ?c WHERE { ?a type human ?a wrote ?b ?b type article ?c wrote ?b ?c type human ?a != ?c }
DELETE ?s ?p ?o WHERE { ?s ?p ?o }
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Triple-store vs. relational database.
Triple-store Relational Database
SQL InterfaceSPARQL Interface
SELECT (?x4) WHERE { ?x1 dc:creator lanl:LAUR-06-2139. ?x1 lanl:hasFriend ?x2 . ?x2 lanl:worksFor ?x3 . ?x3 lanl:collaboratesWith ?x4 . ?x4 lanl:hasEmployee ?x1 . }
SELECT collaboratesWithTable.ordId2 FROM personTable, authorTable, articleTable, friendTable, hasEmployeeTable, organizationTable, worksForTable, collaboratesWithTable WHERE personTable.id = authorTable.personId AND authorTable.articleId = "dc:creator LAUR-06-2139" AND personTable.id = friendTable.personId1 AND friendTable.personId2 = worksForTable.personId AND worksForTable.orgId = collaboratesWithTable.orgId2 AND collaboratesWithTable.ordId2 = personTable.id
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Semantic network metrics?
• What does it means to run a shortest-path calculation on a semantic network?o Shortest-path along which semantic--which edge type(s)?
• What does it mean to calculate PageRank on a semantic network?o What are legal semantics for the random walker?
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Shortest-path metrics in a semantic network?
lanl:marko lanl:johanlanl:hasFriend
lanl:bob
lanl:jill
lanl:chuck
lanl:hasFriend
lanl:hasFriendlanl:hasFriend
lanl:livesInSameCityAs
“What is the shortest path between lanl:marko and lanl:jill by taking only lanl:hasFriend edges?”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
PageRank in a semantic network?
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
?
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
? ?
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based geodesics and random walkers.
• How do you port many of the undirected and directed single-relational networkanalysis algorithms over to the semantic network domain?
o My solution is what I call a grammar.
• Nearly every network analysis algorithm can be represented in terms of a walkertraversing a network.
o Geodesics.o PageRanko Metadata paths.o etc.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Components of a grammar-based walker.
• A walker.o Discrete element.
• A grammar.o An abstract representation of legal path for the walker take.
- e.g. “you can traverse a lanl:friendOf edge from a lanl:Human to anotherlanl:Human.”
- Also includes rules: “increment a counter.”, “don’t ever return to this vertex.”
• A data set that respects the ontological “expectations” of the grammar.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based PageRank example.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
0
0
0
“Take only lanl:wrote out-edge to a resource ofrdf:type lanl:Article. Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human.Increment only lanl:Humans. Make sure that thelanl:Human seen before is not the samelanl:Human currently. Repeat infinitely.”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based PageRank example.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
“Take only lanl:wrote out-edge to a resource ofrdf:type lanl:Article. Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human.Increment only lanl:Humans. Make sure that thelanl:Human seen before is not the samelanl:Human currently. Repeat infinitely.”
1
0
0
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based PageRank example.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
1
0
0
“Take only lanl:wrote out-edge to a resource ofrdf:type lanl:Article. Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human.Increment only lanl:Humans. Make sure that thelanl:Human seen before is not the samelanl:Human currently. Repeat infinitely.”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based PageRank example.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
1
0
1
“Take only lanl:wrote out-edge to a resource ofrdf:type lanl:Article. Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human.Increment only lanl:Humans. Make sure that thelanl:Human seen before is not the samelanl:Human currently. Repeat infinitely.”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based PageRank example.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
1
0
1
“Take only lanl:wrote out-edge to a resource ofrdf:type lanl:Article. Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human.Increment only lanl:Humans. Make sure that thelanl:Human seen before is not the samelanl:Human currently. Repeat infinitely.”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based PageRank example.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
2
0
1
“Take only lanl:wrote out-edge to a resource ofrdf:type lanl:Article. Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human.Increment only lanl:Humans. Make sure that thelanl:Human seen before is not the samelanl:Human currently. Repeat infinitely.”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammar-based PageRank example.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
2
0
1
“Take only lanl:wrote out-edge to a resource ofrdf:type lanl:Article. Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human.Increment only lanl:Humans. Make sure that thelanl:Human seen before is not the samelanl:Human currently. Repeat infinitely.”
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Grammars create implicit relationships.
lanl:marko
lanl:p1
lanl:wrote
lanl:johan
lanl:wrote
lanl:chuck
lanl:hasFriend
lanl:Article
rdf:type
rdf:type
lanl:Human
rdf:type
rdf:type
lanl:hasCoauthor
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Conclusions.
• Many data sets can be represented as a network of “actors”.• There exists many network analysis algorithms.
o Shortest-path metrics.o Eigenvector-based metrics.o Assortativity coefficients.
• The semantic network data structure is a less studied data model.o Semantic Web community doesn’t take a network approach to their substrate.
• The grammar technique can be used to port many of the common networkanalysis algorithms to the semantic network domain.
o Grammar-based geodesics.o Grammar-based random walkers.
Marko A. RodriguezUniversity of New Mexico, September 14, 2007
Related publications.
• Rodriguez, M.A., Watkins, J.H., Bollen, J., Gershenson, C., “Using RDF to Model the Structure andProcess of Systems”, International Conference on Complex Systems, Boston, Massachusetts, LAUR-07-5720, October 2007.
• Rodriguez, M.A., Bollen, J., Van de Sompel, H., “A Practical Ontology for the Large-Scale Modeling ofScholarly Artifacts and their Usage”, 2007 ACM/IEEE Joint Conference on Digital Libraries, pages 278-287, Vancouver, Canada, ACM/IEEE Computing, doi:10.1145/1255175.1255229, LA-UR-07-0665, June2007.
• Rodriguez, M.A., "Social Decision Making with Multi-Relational Networks and Grammar-BasedParticle Swarms", 2007 Hawaii International Conference on Systems Science (HICSS), pages 39-49,Waikoloa, Hawaii, IEEE Computer Society, ISSN: 1530-1605, doi:10.1109/HICSS.2007.487, LA-UR-06-2139, January 2007.
• Rodriguez, M.A., "A Multi-Relational Network to Support the Scholarly Communication Process",International Journal of Public Information Systems, volume 2007, issue 1, pages 13-29, ISSN: 1653-4360,LA-UR-06-2416, March 2007.
• Rodriguez, M.A., “Mapping Semantic Networks to Undirected Networks”, LA-UR-07-5287, August 2007.• Rodriguez, M.A., Watkins, J.H., “Grammar-Based Geodesics in Semantic Networks”, LA-UR-07-4042,
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