The Network: A Data Structure that Links Domains
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The Network: A Data Structure that Links Domains The Network: A Data Structure that Links Domains Presentation Transcript

  • The Network: A Data Structure that Links Domains Marko A. Rodriguez Los Alamos National Laboratory Vrije Universiteit Brussel University of California at Santa Cruz marko@lanl.gov http://www.soe.ucsc.edu/~okram Marko A. Rodriguez University 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. Rodriguez University 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 Virtual Machine Architecture. (http://neno.lanl.gov) • CDMS: Collective Decision Making Systems. (http://cdms.lanl.gov) Marko A. Rodriguez University of New Mexico, September 14, 2007
  • What is a network? • A network is a data structure that is used to connect vertices/nodes/dots by means 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. Rodriguez University 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. Rodriguez University of New Mexico, September 14, 2007
  • Example undirected network. Jen Alberto Whenzong Luda Aric Herbert Zhiwu Ed Johan Stephan Marko Marko A. Rodriguez University 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. Rodriguez University of New Mexico, September 14, 2007
  • Example directed network. Human Lion Hyena Deer Fish Muskrat Meerkat Fox Wolf Beetle Bear Marko A. Rodriguez University of New Mexico, September 14, 2007
  • The semantic network. • Finally, there is the semantic network o 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 j s Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Example semantic network. LANL hasLab UnitedStates Arnold researches locatedIn stateOf stateOf governerOf Atoms cityOf NewMexico SantaFe California madeOf hasResident originallyFrom Ryan northOf livesIn southOf Cells madeOf worksWith Oregon Marko Marko A. Rodriguez University of New Mexico, September 14, 2007
  • What are the techniques for analysis? • Degree statistics o 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 metrics o 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 metrics o What vertices are the most “influential”? • Metadata distributions o What is the probability that a vertex of type x is connected to a vertex of type y? Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Degree statistics. Max_out = 4 Max_in = 4 Min_out = 0 out = 2 Min_in = 0 in = 1 out = 3 Human in = 0 out = 1 Lion Hyena in = 0 out = 0 in = 2 out = 0 out = 1 in = 4 Deer in = 1 Fish out = 1 Muskrat Meerkat in = 3 out = 1 Fox in = 1 out = 1 Wolf out = 0 in = 1 Beetle in = 1 Bear out = 4 in = 0 Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Shortest-path between Marko and Aric. Jen Alberto Whenzong Luda Aric Herbert Zhiwu Ed Johan Stephan Marko Shortest path = 1 Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Eccentricity of Marko. Jen Alberto Whenzong Luda Aric Herbert Zhiwu Ed Johan Stephan Marko Eccentricity = 3 Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Radius of the network. Jen Alberto Whenzong Luda Aric Herbert Zhiwu Ed Johan Stephan Marko Radius = 3 Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Diameter of the network. Jen Alberto Whenzong Luda Aric Herbert Zhiwu Ed Johan Stephan Marko Diameter = 4 Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Closeness of Marko. Jen Alberto Whenzong Luda Aric Herbert Zhiwu Ed Johan Stephan Marko Closeness = 0.0526 Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Shortest-path metrics. Shortest-path Eccentricity Radius Diameter Closeness Betweenness Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Power metrics. • Eigenvector Centrality o 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. • PageRank o Ensure a real-valued ranking by introducing a “teleportation-network” which ensures strong connectivity (used by Google). Marko A. Rodriguez University of New Mexico, September 14, 2007
  • The components to calculate a stationary probability distribution. • Take a single “random walker”. • Place that random walker on any random vertex in the network. a • At every time step, the random walker transitions from its current node to an adjacent node in the network (i.e. takes a random outgoing edge from its current node.) • Anytime the random walker is at a node, increment a “times visited” counter by 1. 1 • Let this algorithm run for an “infinite” amount of time. • Normalize the “times visited” counters. o That is your centrality vector. 0.0123 Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 0 0 a d 0 0 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 0 1 a d 0 0 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 1 1 a d 0 0 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 1 1 a d 1 0 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 1 1 a d 1 1 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 2 1 a d 1 1 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 2 1 a d 1 2 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 2 2 a d 1 2 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 3 2 a d 1 2 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 3 2 a d 2 2 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 3 2 a d 2 3 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 4 2 a d 2 3 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 133321 66785 a d 66784 133310 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Random walker example. b 0.332 0.167 a d 0.167 0.332 c Marko A. Rodriguez University of New Mexico, September 14, 2007
  • PageRank. • The random walker has 0.85 probability of using G as its propagation network and a 0.15 probability of using H as its propagation network (Google’s published alpha 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. Rodriguez University of New Mexico, September 14, 2007
  • Metadata distribution metrics. • Scalar Assortativity o 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 Assortativity o 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 metadata z? o … Marko A. Rodriguez University 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 students from multiple disciplines at the partner institutions of University of California at Los Angeles (UCLA), University of Southern California (USC), University of California Riverside (UCR), California Institute of Technology (Caltech), University of California at Merced (UCM), and California State University at Los Angeles (CSULA).” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Everything is metadata. Affilation LANL Department Research Library Gender Male JobRank Ph.D. Student Buidling P362 Lab Prototyping Team Advisor Johan Bollen Degree 2 Marko …. …. Marko A. Rodriguez University of New Mexico, September 14, 2007
  • The CENS coauthorship network. • UCLA - red • USC - orange • Coventry - green • … • Ph.D. - ellipse • Professor - hexagon • … Marko A. Rodriguez University of New Mexico, September 14, 2007
  • 1st-order degree distributions in CENS coauthorship network. Marko A. Rodriguez University of New Mexico, September 14, 2007
  • 2nd-order degree distributions in CENS coauthorship network. Marko A. Rodriguez University 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. Rodriguez University 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. Rodriguez University of New Mexico, September 14, 2007
  • Metadata path frequencies. • 1064.0 UCLA UCLA • 376.0 Phd Professor • 442.0 USC USC • 254.0 Phd Researcher • 336.0 USC UCLA • 242.0 Researcher Professor • 76.0 MIT UCLA • 184.0 Phd Phd • 58.0 UCLA UCM • 142.0 Professor Professor • 32.0 Caltech UCLA • 1186.0 Male Male • 304.0 US US • 508.0 Male Female • 156.0 India US • 78.0 Female Female • 70.0 India India • 58.0 US China • 750.0 CS CS • 36.0 India China • 388.0 EE CS • 28.0 China China • 340.0 EE EE • 24.0 US Italy • 84.0 CS CivilEng • 18.0 Iran India • 78.0 Biology CS • 14.0 Iran US • 74.0 CivilEng CivilEng • 12.0 Greece India Marko A. Rodriguez University of New Mexico, September 14, 2007
  • 2nd-order metadata assortativity in CENS coauthorship network. • 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. Rodriguez University of New Mexico, September 14, 2007
  • 3rd-order metadata assortativity in CENS coauthorship network. • 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. Rodriguez University 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. Rodriguez University 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. Rodriguez University of New Mexico, September 14, 2007
  • Breather. Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Example semantic network. LANL hasLab UnitedStates Arnold researches locatedIn stateOf stateOf governerOf Atoms cityOf NewMexico SantaFe California madeOf hasResident originallyFrom Ryan northOf livesIn southOf Cells madeOf worksWith Oregon Marko Marko A. Rodriguez University of New Mexico, September 14, 2007
  • What is the Semantic Web? • The figurehead of the Semantic Web initiative, Tim Berners-Lee, describes the Semantic Web as o “... an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” • Perhaps not the best definition. It implies a particular application space--namely the “web metadata and intelligent agents” space. • My definition is that the Semantic Web is o “a highly-distributed, standardized semantic network data model--a URG (Uniform Resource Graph). It’s a uniform way of graphing resources.” Marko A. Rodriguez University 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 Web o “first identify it, then relate it!” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • The technologies of the Semantic Web. • Resource Description Framework (RDF): The foundation technology of the Semantic Web. RDF is a highly-distributed, semantic network data model. In RDF, URIs and literals (e.g. ints, doubles, strings) are related to one another in triples. 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 to the 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 relational database is to the data table. o a.k.a. semantic repository, graph database, RDF database. Marko A. Rodriguez University of New Mexico, September 14, 2007
  • RDF and RDFS. lanl:Human lanl:Food rdfs:range rdfs:domain lanl:isEating ontology rdf:type rdf:type instance lanl:isEating lanl:marko lanl:cookie 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. Rodriguez University of New Mexico, September 14, 2007
  • The triple-store. • 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 data tables. o Storing and querying triples in a triple store. o SPARQLUpdate query language. - like SQL, but for triple-stores. SELECT ?a ?c WHERE INSERT ?a coauthor ?c WHERE { ?a type human { ?a type human ?a wrote ?b ?a wrote ?b ?b type article ?b type article ?c wrote ?b ?c wrote ?b ?c type human ?c type human ?a != ?c } ?a != ?c } DELETE ?s ?p ?o WHERE { ?s ?p ?o } Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Triple-store vs. relational database. Triple-store Relational Database SPARQL Interface SQL Interface SELECT (?x4) SELECT collaboratesWithTable.ordId2 WHERE { FROM personTable, authorTable, articleTable, friendTable, hasEmployeeTable, organizationTable, worksForTable, ?x1 dc:creator lanl:LAUR-06-2139. collaboratesWithTable ?x1 lanl:hasFriend ?x2 . WHERE ?x2 lanl:worksFor ?x3 . personTable.id = authorTable.personId AND ?x3 lanl:collaboratesWith ?x4 . authorTable.articleId = "dc:creator LAUR-06-2139" AND ?x4 lanl:hasEmployee ?x1 . } personTable.id = friendTable.personId1 AND friendTable.personId2 = worksForTable.personId AND worksForTable.orgId = collaboratesWithTable.orgId2 AND collaboratesWithTable.ordId2 = personTable.id Marko A. Rodriguez University 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. Rodriguez University of New Mexico, September 14, 2007
  • Shortest-path metrics in a semantic network? lanl:hasFriend lanl:marko lanl:johan lanl:hasFriend lanl:hasFriend lanl:livesInSameCityAs lanl:chuck lanl:bob lanl:hasFriend lanl:jill “What is the shortest path between lanl:marko and lanl:jill by taking only lanl:hasFriend edges?” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • PageRank in a semantic network? lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote ? ? ? lanl:marko lanl:johan lanl:hasFriend lanl:chuck Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based geodesics and random walkers. • How do you port many of the undirected and directed single-relational network analysis 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 walker traversing a network. o Geodesics. o PageRank o Metadata paths. o etc. Marko A. Rodriguez University 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 another lanl: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. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based PageRank example. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko 0 lanl:johan 0 lanl:hasFriend “Take only lanl:wrote out-edge to a resource of rdf: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 the lanl:chuck 0 lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based PageRank example. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko 1 lanl:johan 0 lanl:hasFriend “Take only lanl:wrote out-edge to a resource of rdf: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 the lanl:chuck 0 lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based PageRank example. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko 1 lanl:johan 0 lanl:hasFriend “Take only lanl:wrote out-edge to a resource of rdf: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 the lanl:chuck 0 lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based PageRank example. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko 1 lanl:johan 1 lanl:hasFriend “Take only lanl:wrote out-edge to a resource of rdf: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 the lanl:chuck 0 lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based PageRank example. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko 1 lanl:johan 1 lanl:hasFriend “Take only lanl:wrote out-edge to a resource of rdf: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 the lanl:chuck 0 lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based PageRank example. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko 2 lanl:johan 1 lanl:hasFriend “Take only lanl:wrote out-edge to a resource of rdf: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 the lanl:chuck 0 lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammar-based PageRank example. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko 2 lanl:johan 1 lanl:hasFriend “Take only lanl:wrote out-edge to a resource of rdf: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 the lanl:chuck 0 lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Grammars create implicit relationships. lanl:Human lanl:Article rdf:type rdf:type rdf:type rdf:type lanl:p1 lanl:wrote lanl:wrote lanl:marko lanl:johan lanl:hasCoauthor lanl:hasFriend lanl:chuck Marko A. Rodriguez University 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 network analysis algorithms to the semantic network domain. o Grammar-based geodesics. o Grammar-based random walkers. Marko A. Rodriguez University of New Mexico, September 14, 2007
  • Related publications. • Rodriguez, M.A., Watkins, J.H., Bollen, J., Gershenson, C., “Using RDF to Model the Structure and Process 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 of Scholarly 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, June 2007. • Rodriguez, M.A., "Social Decision Making with Multi-Relational Networks and Grammar-Based Particle 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, June 2007. • Rodriguez, M.A., Bollen, J., “Modeling Computations in a Semantic Network”, LA-UR-07-3678, May 2007. • Rodriguez, M.A., “General-Purpose Computing on a Semantic Network Substrate”, LA-UR-07-2885, April 2007. • Rodriguez, M.A., “Grammar-Based Random Walkers in Semantic Networks”, LA-UR-06-7791, November 2006. Marko A. Rodriguez University of New Mexico, September 14, 2007