The Network Data Structure in Computing
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The document discusses network data structures and semantic networks. It provides examples of undirected, directed, and semantic networks. It describes how PageRank can be applied to semantic networks using grammar-based random walkers that follow the relationships between nodes defined in an ontology. It also lists related publications by the author on modeling systems and computations as semantic networks.
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![What is a resource? Resource = Anything. Anything that can be identified. The Uniform Resource Identifier (URI): <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 “ first identify it, then relate it! ”](https://image.slidesharecdn.com/super-computer-100428102426-phpapp01/85/The-Network-Data-Structure-in-Computing-30-320.jpg)
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A Semantic Multimedia Web (Part 2)
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Semantic web
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Linked Open Data
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EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014
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When?
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2. Two scenarios involving expressing claims in RDFa and linking from a homepage are presented, showing how trust can come from linked information.
3. Recent and emerging trends in user interfaces, search engines, and services are moving towards a more machine-readable web where pages make claims and datasets are interconnected.
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mm-ADT: A Virtual Machine/An Economic Machine
mm-ADT: A Virtual Machine/An Economic MachineMarko Rodriguez The document discusses the evolving landscape of open-source software and its economic implications, particularly highlighting the challenges faced by independent and corporate open-source developers in the face of large companies like Amazon. It proposes a royalty-based model for compensating developers to ensure sustainability and innovation within the industry. Additionally, it addresses the potential future risks to open-source ecosystems due to corporate exploitation and dominance.
mm-ADT: A Multi-Model Abstract Data Type
mm-ADT: A Multi-Model Abstract Data TypeMarko Rodriguez The document describes mm-ADT, a proposed multi-model abstract datatype that aims to provide a universal data structure, processing model, and instruction set that can support various database models like graph, document, relational etc. in a common framework. Key goals are to release a stable mm-ADT specification, compiler and virtual machine, as well as a basic reference implementation by early 2020. The presentation focuses on the universal data structure component, describing how mm-ADT would define custom datatypes, instances, access paths and more using a bytecode language.
Open Problems in the Universal Graph Theory
Open Problems in the Universal Graph TheoryMarko Rodriguez The document discusses the idea of the universal graph - that everything can be modeled as a graph of vertices and edges. It proposes some open problems regarding how to model processes and qualia as part of the universal graph structure, and how one might manipulate the laws of physics by altering the processes that govern the evolution of the physical world graph.
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Gremlin 101.3 On Your FM DialMarko Rodriguez The document outlines Gremlin, a graph traversal language, and provides examples of how to navigate and query a graph database using specific commands. It discusses various traversal steps, including filtering, mapping, and reducing data within the graph context. Additionally, it cites references and offers insight into the functionality and capabilities of Gremlin in graph database operations.
Quantum Processes in Graph Computing
Quantum Processes in Graph ComputingMarko Rodriguez The document discusses quantum processes in graph computing, particularly focusing on the concept of energy diffusion within graph structures and its representation using Gremlin sacks. It explains how energy behaves like a wave and can undergo constructive and destructive interference when traversers split and merge. Furthermore, it relates this to quantum mechanics and the Copenhagen interpretation, illustrating how particles take multiple paths simultaneously and the behavior of wave functions.
ACM DBPL Keynote: The Graph Traversal Machine and Language
ACM DBPL Keynote: The Graph Traversal Machine and LanguageMarko Rodriguez The document summarizes the components of the Gremlin traversal machine:
- The graph is stored in memory and represents the data as vertices, edges, and properties.
- Traversers represent computational threads that traverse the graph. They track their location, path history, and program counter in the traversal program.
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The Gremlin Graph Traversal Language
The Gremlin Graph Traversal LanguageMarko Rodriguez The document discusses the Gremlin graph traversal language and its application to the MovieLens dataset, which consists of user ratings for movies. It outlines how to create and manipulate a TinkerGraph, including loading the dataset, counting vertices and edges, and performing various queries to analyze user and movie data. Key insights include the average rating for movies and the categorization of films by genre.
The Path Forward
The Path ForwardMarko Rodriguez This document discusses graph-based computing and traversal using Gremlin and Titan. It provides examples of querying a graph about relationships between characters in Greek mythology like Hercules. Traversal operations are demonstrated to find other characters Hercules may know or which actor played him in a movie. The value of graph analysis for insights and recommendations is also discussed.
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Solving Problems with Graphs
Solving Problems with GraphsMarko Rodriguez The document discusses the use of graphs to solve problems, particularly in organizing and analyzing large data sets. It highlights the importance of interconnections within data, suggesting that effective resolutions can be achieved through proper linkages. Additionally, it presents various solutions and technologies developed for handling graph data and emphasizes the role of algorithms in optimizing these connections.
Titan: The Rise of Big Graph Data
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Graph Databases: Trends in the Web of Data
Graph Databases: Trends in the Web of DataMarko Rodriguez Aanswers · Aanswers ◦ n(I) π
etc...15
15
I is the identity matrix.
The Multi-Relational Path Algebra:
- Allows single-relational algorithms to be applied to multi-relational graphs
- Provides a universal framework for defining paths through a multi-relational
graph
- Enables the computation of multiple primary eigenvectors, each
corresponding to a different path definition
- In effect, provides multiple definitions of centrality for a multi-relational
network
- Is Turing complete—any computable path can be expressed
- Is a general framework—applies to any multi-relational data model
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- 1. The Network Data Structure in Computing Marko A. Rodriguez Los Alamos National Laboratory Vrije Universiteit Brussel [email_address] http://cnls.lanl.gov/~marko
- 2. 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. Researcher at the Los Alamos National Laboratory since 2005.
- 3. 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 ) C D M S : Collective Decision Making Systems. ( http://cdms.lanl.gov )
- 4. 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. Social : friendship, trust, communication, collaboration. Technological : web-pages, communication, software dependencies, circuits. Scholarly : journals, authors, articles, institutions. Natural : protein interaction, neural, food web.
- 5. The undirected network. There is the undirected network of common knowledge. Sometimes called an undirected single-relational network. e.g. vertex i and vertex j are “related”. The semantic of the edge denotes the network type . e.g. friendship network, collaboration network, etc. i j
- 6. Example undirected network. Herbert Marko Aric Ed Zhiwu Alberto Jen Johan Luda Stephan Whenzong
- 7. The directed network. Then there is the directed network of common knowledge. Sometimes called a directed single-relational network. For example, vertex i is related to vertex j , but j is not related to i . i j
- 8. Example directed network. Muskrat Bear Fish Fox Meerkat Lion Human Wolf Deer Beetle Hyena
- 9. The semantic network. Finally, there is the semantic network Sometimes called a directed multi-relational network. 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
- 10. Example semantic network. SantaFe Marko NewMexico Ryan California UnitedStates LANL livesIn worksWith cityOf originallyFrom stateOf stateOf locatedIn hasLab Cells Atoms madeOf madeOf researches Oregon southOf hasResident Arnold governerOf northOf
- 11. Google’s PageRank. PageRank Used to rank web-pages that are connected by citation (hyper-link). Note: this image was stolen off the web from somewhere.
- 12. The components to calculate a stationary 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 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. Let this algorithm run for an “infinite” amount of time. Normalize the “times visited” counters. That is your centrality vector. a 1 0.0123
- 13. Random walker example. a c b d 0 0 0 0
- 14. Random walker example. a c b d 1 0 0 0
- 15. Random walker example. a c b d 1 0 1 0
- 16. Random walker example. a c b d 1 0 1 1
- 17. Random walker example. a c b d 1 1 1 1
- 18. Random walker example. a c b d 1 1 2 1
- 19. Random walker example. a c b d 1 2 2 1
- 20. Random walker example. a c b d 2 2 2 1
- 21. Random walker example. a c b d 2 2 3 1
- 22. Random walker example. a c b d 2 2 3 2
- 23. Random walker example. a c b d 2 3 3 2
- 24. Random walker example. a c b d 2 3 4 2
- 25. Random walker example. a c b d 66785 133310 133321 66784
- 26. Random walker example. a c b d 0.167 0.332 0.332 0.167
- 27. Breather.
- 28. Example semantic network. SantaFe Marko NewMexico Ryan California UnitedStates LANL livesIn worksWith cityOf originallyFrom stateOf stateOf locatedIn hasLab Cells Atoms madeOf madeOf researches Oregon southOf hasResident Arnold governerOf northOf
- 29. What is the Semantic Web? The figurehead of the Semantic Web initiative, Tim Berners-Lee, describes the Semantic Web as “ ... 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 “ a distributed, standardized semantic network data model--a URG (Uniform Resource Graph). It’s a uniform way of graphing resources. ”
- 30. What is a resource? Resource = Anything. Anything that can be identified. The Uniform Resource Identifier (URI): <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 “ first identify it, then relate it! ”
- 31. 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. <lanl:marko> <lanl:worksWith> <lanl:jhw> <lanl:jhw> <lanl:wrote> <lanl:LAUR-07-2028> <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. “ Anything of rdf:type lanl:Human can lanl:drive anything of rdf:type lanl:Car .”
- 32. RDF and RDFS. lanl:marko lanl:cookie lanl:Human lanl:Food lanl:isEating rdf:type rdf:type lanl:isEating rdfs:domain rdfs:range ontology instance RDF is not a syntax. It’s a data model. Various syntaxes exist to encode RDF including RDF/XML, N-TRIPLE, TRiX, N3, etc.
- 33. 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 ? ?
- 34. Components of a grammar-based walker. A walker . Discrete element. A grammar . 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.
- 35. 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 of rdf:type lanl:Article . Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human . Increment only lanl:Human s. Make sure that the lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.”
- 36. 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 of rdf:type lanl:Article . Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human . Increment only lanl:Human s. Make sure that the lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.” 1 0 0
- 37. 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 of rdf:type lanl:Article . Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human . Increment only lanl:Human s. Make sure that the lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.”
- 38. 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 of rdf:type lanl:Article . Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human . Increment only lanl:Human s. Make sure that the lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.”
- 39. 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 of rdf:type lanl:Article . Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human . Increment only lanl:Human s. Make sure that the lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.”
- 40. 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 of rdf:type lanl:Article . Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human . Increment only lanl:Human s. Make sure that the lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.”
- 41. 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 of rdf:type lanl:Article . Then take a lanl:wrote in-edge to a resource of rdf:type lanl:Human . Increment only lanl:Human s. Make sure that the lanl:Human seen before is not the same lanl:Human currently. Repeat infinitely.”
- 42. 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
- 43. Conclusions. Many systems can be represented as a network. The semantic network is a more expressive, though less studied data model. The grammar technique can be used to port many of the common network analysis algorithms to the semantic network domain.
- 44. 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 ”, Knowledge-Based Systems, Elsevier, LA-UR-06-7791, in press, 2007.