SIOC Post profile for "AI3:::Adaptive Information"
type: Document
description: A SIOC profile describes the structure and contents of a weblog in a machine readable form. For more information please refer to http://sioc-project.org/.
primary topic: The Age of the Graph
generator agent: http://rdfs.org/sioc/wp-sioc.php

The Age of the Graph
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2012-08-13T03:13:48Z
content: The Transition from Transactions to Connections Download PDF Virtually everywhere one looks we are in the midst of a transition for how we organize and manage information, indeed even relationships. Social networks and online communities are changing how we live and interact. NoSQL and graph databases -- married to their near cousin Big Data -- are changing how we organize and store information and data. Semantic technologies, backed by their ontologies and RDF data model, are showing the way for how we can connect and interoperate disparate information in ways only dreamed about a decade ago. And all of this, of course, is being built upon the infrastructure of the Internet and the Web, a global, distributed network of devices and information that is undoubtedly one of the most important technological developments in human history. There is a shared structure across all of these developments -- the graph. Graphs are proving to be the new universal paradigm for how we organize and manage information. Graphs have an inherently expandable nature, and one which can also capture any existing structure. So, as we see all of the networks, connections, relationships and links -- both physical and informational -- grow around us, it is useful to step back a bit and contemplate the universal graph structure at the core of these developments. Understanding that we now live in the Age of the Graph means we can begin studying and using the concept of the graph itself to better analyze and manage our interconnected world. Whether we are trying to understand the physical networks of supply chains and infrastructure or the information relationships within ontologies or knowledge graphs, the various concepts underlying graphs and graph theory, themselves expressed through a rich vocabulary of terms, provide the keys for unlocking still further treasures hidden in the structure of graphs. Graphs as a Concept The use of "graph" as a mathematical concept is not much more than 100 year…
encoded: The Transition from Transactions to Connections Download PDF Virtually everywhere one looks we are in the midst of a transition for how we organize and manage information, indeed even relationships. Social networks and online communities are changing how we live and interact. NoSQL and graph databases — married to their near cousin Big Data — are changing how we organize and store information and data. Semantic technologies, backed by their ontologies and RDF data model, are showing the way for how we can connect and interoperate disparate information in ways only dreamed about a decade ago. And all of this, of course, is being built upon the infrastructure of the Internet and the Web, a global, distributed network of devices and information that is undoubtedly one of the most important technological developments in human history. There is a shared structure across all of these developments — the graph. Graphs are proving to be the new universal paradigm for how we organize and manage information. Graphs have an inherently expandable nature, and one which can also capture any existing structure. So, as we see all of the networks, connections, relationships and links — both physical and informational — grow around us, it is useful to step back a bit and contemplate the universal graph structure at the core of these developments. Understanding that we now live in the Age of the Graph means we can begin studying and using the concept of the graph itself to better analyze and manage our interconnected world. Whether we are trying to understand the physical networks of supply chains and infrastructure or the information relationships within ontologies or knowledge graphs, the various concepts underlying graphs and graph theory, themselves expressed through a rich vocabulary of terms, provide the keys for unlocking still further treasures hidden in the structure of graphs. Graphs as a Concept The use of “graph” as a mathematical concept is not much more than 100 years old…
topic: Adaptive Innovation
topic: Ontologies
topic: Peircean Principles
topic: Structured Web
has reply: comment-235967
links to: Download PDF
links to: rich vocabulary of terms
links to: Charles Sanders Peirce
links to: [1]
links to: [2]
links to: [3]
links to: our company's
links to: graph theory
links to: Seven Bridges of Königsberg
links to: Leonhard Euler
links to: Leibniz
links to: Cauchy
links to: L'Huillier
links to: topology.
links to: Cayley
links to: tree structures
links to: network science
links to: statistical mechanics
links to: data mining
links to: information visualization
links to: inferential modeling
links to: social structure
links to: four color problem
links to: traveling salesman problem
links to: six degrees of Kevin Bacon
links to: Graph Structures
links to: Data structures
links to: linked data structures
links to: arrays
links to: B-trees
links to: tables
links to: Tree structures
links to: grammatical structures
links to: binary trees
links to: evolutionary trees
links to: work breakdown structures
links to: nested sets
links to: decision trees
links to: taxonomies
links to: family trees
links to: tries
links to: tree automata
links to: abstract syntax trees
links to: phylogenetic trees
links to: List structures
links to: incidence lists
links to: adjacency lists
links to: linked lists
links to: queues
links to: Matrix structures
links to: incidence matrices
links to: adjacency matrices
links to: Laplacian matrices
links to: distance matrices
links to: lattices
links to: paths
links to: loops
links to: Eulerian paths
links to: Hamiltonian paths
links to: Networks
links to: engineered networks
links to: information networks
links to: biological networks
links to: semantic networks
links to: social networks
links to: Bayesian networks

Mike Bergman
type: User

Mike Bergman
type: Person

Graph Structures
label: Graph-structured data

Matrix structures
label: Matrix manipulations (many)

Bayesian networks
label: Computer networks.

complex networks

scale-free networks

small-world networks

Logic graphs

Ontologies
label: ontologies

Conceptual graphs

Mind maps

Random graphs

Weighted graphs

Sparse/dense graphs

graphical enumeration

subgraph isomorphism problem

clique problem

induced subgraphs

independent set problem

reconstruction conjecture

tree traversal

breadth-first search

depth-first search

four-color theorem

strong perfect graph theorem

Erdős–Faber–Lovász conjecture

total coloring conjecture

list coloring conjecture

Hadwiger conjecture

automatic theorem proving

linguistic structure

Hamiltonian path and cycle problems

minimum spanning tree

route inspection problem

shortest path problem

Steiner tree

three-cottage problem

critical path analysis

percolation

max flow min cut theorem

museum guard problem

set cover problem

vertex cover problem

degree distribution

power laws

clustering coefficients
label: clustering

assortativity

community structure

hierarchical structure

reciprocity

graph partitioning

enumerating

forbidden substructures

deciding

representations

[4]:

Telecommunications networks

Global shipping networks

Transport networks

Electrical networks

grids

[5]

Chemical reaction networks

Gene regulatory networks

Spin networks

Neural networks
label: neural networks

Ecological networks
label: Business ecosystems

Petri nets

Social network analysis

[6]

Open Graph

Social networks

Military conflicts

Value networks

Project networks

Workflows

Graph databases

RDF

OWL

Common logic

Conceptual graphs

relational logic

WordNet

VerbNet

[7]

Semantic networks

hardest

big data

map reduce

Radhanites

Internet

Facebook