Learning the Internet View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002-06-25

AUTHORS

Christos Papadimitriou

ABSTRACT

The Internet is arguably the most important, complex, interesting, and intellectually challenging computational artifact of our time, and it is therefore worthy of the research community’s attention. The most novel and defining characteristic of the Internet is its nature as an artifact that was not designed by a single entity, but emerged from the complex interaction of many economic agents (network operators, service providers, users, etc.), in various and varying degrees of collaboration and competition. There is a nascent research area that combines algorithmic thinking with concepts from Game Theory, as well as the realities of the Internet, in order to better understand it. This line of research may actually bring back to the fore certain foundational aspects of Game Theory (namely, the nature of equilibria as well as their relationship to repeated play and evolution) in which the methodology of Learning Theory has much to contribute.The Internet is also an information repository of unprecedented extent, diversity, availability, and lack of structure, and it has therefore become an arena for the development of a new generation of sophisticated techniques for information retrieval and data mining (and to which Learning Theory has obviously much to offer). It has been suggested that economic considerations are crucial in formulating problems also in this environment, and can shed new light on important topics such as clustering and privacy.Because of its spontaneous emergence, the Internet is the first computational artifact that must be studied as a mysterious object (akin to matter, market, and intelligence) whose laws we must derive by observation, experiment, and the development of falsifiable theories. Recently, a plausible explanation of the skewed degree distributions one observes in the Internet topology was based on simple models of network creation, in which arriving nodes choose connections that achieve a favorable tradeoff among criteria such as last-mile distance and communication delays. These topics will be illustrated in terms of recent models and results by the speaker and co-authors, generally available at the speaker’s web page. More... »

PAGES

396-396

Book

TITLE

Computational Learning Theory

ISBN

978-3-540-43836-6
978-3-540-45435-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-45435-7_27

DOI

http://dx.doi.org/10.1007/3-540-45435-7_27

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011573230


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "UC Berkeley, USA", 
          "id": "http://www.grid.ac/institutes/grid.47840.3f", 
          "name": [
            "UC Berkeley, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Papadimitriou", 
        "givenName": "Christos", 
        "id": "sg:person.013233165465.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013233165465.63"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2002-06-25", 
    "datePublishedReg": "2002-06-25", 
    "description": "The Internet is arguably the most important, complex, interesting, and intellectually challenging computational artifact of our time, and it is therefore worthy of the research community\u2019s attention. The most novel and defining characteristic of the Internet is its nature as an artifact that was not designed by a single entity, but emerged from the complex interaction of many economic agents (network operators, service providers, users, etc.), in various and varying degrees of collaboration and competition. There is a nascent research area that combines algorithmic thinking with concepts from Game Theory, as well as the realities of the Internet, in order to better understand it. This line of research may actually bring back to the fore certain foundational aspects of Game Theory (namely, the nature of equilibria as well as their relationship to repeated play and evolution) in which the methodology of Learning Theory has much to contribute.The Internet is also an information repository of unprecedented extent, diversity, availability, and lack of structure, and it has therefore become an arena for the development of a new generation of sophisticated techniques for information retrieval and data mining (and to which Learning Theory has obviously much to offer). It has been suggested that economic considerations are crucial in formulating problems also in this environment, and can shed new light on important topics such as clustering and privacy.Because of its spontaneous emergence, the Internet is the first computational artifact that must be studied as a mysterious object (akin to matter, market, and intelligence) whose laws we must derive by observation, experiment, and the development of falsifiable theories. Recently, a plausible explanation of the skewed degree distributions one observes in the Internet topology was based on simple models of network creation, in which arriving nodes choose connections that achieve a favorable tradeoff among criteria such as last-mile distance and communication delays. These topics will be illustrated in terms of recent models and results by the speaker and co-authors, generally available at the speaker\u2019s web page.", 
    "editor": [
      {
        "familyName": "Kivinen", 
        "givenName": "Jyrki", 
        "type": "Person"
      }, 
      {
        "familyName": "Sloan", 
        "givenName": "Robert H.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/3-540-45435-7_27", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-43836-6", 
        "978-3-540-45435-9"
      ], 
      "name": "Computational Learning Theory", 
      "type": "Book"
    }, 
    "keywords": [
      "web pages", 
      "computational artifacts", 
      "game theory", 
      "information repository", 
      "information retrieval", 
      "data mining", 
      "Internet topology", 
      "Internet", 
      "research community's attention", 
      "communication delays", 
      "network creation", 
      "research area", 
      "lack of structure", 
      "favorable tradeoff", 
      "nascent research area", 
      "learning theory", 
      "important topic", 
      "degree of collaboration", 
      "sophisticated techniques", 
      "foundational aspects", 
      "artifacts", 
      "privacy", 
      "new generation", 
      "single entity", 
      "mining", 
      "repository", 
      "retrieval", 
      "community's attention", 
      "line of research", 
      "pages", 
      "clustering", 
      "nodes", 
      "tradeoff", 
      "objects", 
      "topic", 
      "topology", 
      "environment", 
      "collaboration", 
      "model", 
      "creation", 
      "reality", 
      "methodology", 
      "entities", 
      "delay", 
      "technique", 
      "concept", 
      "falsifiable theory", 
      "complex interactions", 
      "attention", 
      "distribution one", 
      "order", 
      "availability", 
      "experiments", 
      "generation", 
      "connection", 
      "development", 
      "theory", 
      "mysterious object", 
      "speakers", 
      "one", 
      "research", 
      "aspects", 
      "spontaneous emergence", 
      "terms", 
      "distance", 
      "time", 
      "simple model", 
      "results", 
      "consideration", 
      "arena", 
      "criteria", 
      "economic agents", 
      "area", 
      "emergence", 
      "problem", 
      "lack", 
      "economic considerations", 
      "characteristics", 
      "nature", 
      "agents", 
      "structure", 
      "competition", 
      "recent models", 
      "law", 
      "interaction", 
      "diversity", 
      "lines", 
      "degree", 
      "unprecedented extent", 
      "new light", 
      "observations", 
      "light", 
      "explanation", 
      "extent", 
      "plausible explanation", 
      "fore certain foundational aspects", 
      "certain foundational aspects", 
      "first computational artifact", 
      "skewed degree distributions one", 
      "degree distributions one", 
      "last-mile distance", 
      "speaker\u2019s web page"
    ], 
    "name": "Learning the Internet", 
    "pagination": "396-396", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1011573230"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/3-540-45435-7_27"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/3-540-45435-7_27", 
      "https://app.dimensions.ai/details/publication/pub.1011573230"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-12-01T20:03", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/chapter/chapter_281.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/3-540-45435-7_27"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/3-540-45435-7_27'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-45435-7_27'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-45435-7_27'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-45435-7_27'


 

This table displays all metadata directly associated to this object as RDF triples.

171 TRIPLES      23 PREDICATES      128 URIs      120 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/3-540-45435-7_27 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:0806
4 schema:author Nf2831a6e35e64714a316398d3ed491b1
5 schema:datePublished 2002-06-25
6 schema:datePublishedReg 2002-06-25
7 schema:description The Internet is arguably the most important, complex, interesting, and intellectually challenging computational artifact of our time, and it is therefore worthy of the research community’s attention. The most novel and defining characteristic of the Internet is its nature as an artifact that was not designed by a single entity, but emerged from the complex interaction of many economic agents (network operators, service providers, users, etc.), in various and varying degrees of collaboration and competition. There is a nascent research area that combines algorithmic thinking with concepts from Game Theory, as well as the realities of the Internet, in order to better understand it. This line of research may actually bring back to the fore certain foundational aspects of Game Theory (namely, the nature of equilibria as well as their relationship to repeated play and evolution) in which the methodology of Learning Theory has much to contribute.The Internet is also an information repository of unprecedented extent, diversity, availability, and lack of structure, and it has therefore become an arena for the development of a new generation of sophisticated techniques for information retrieval and data mining (and to which Learning Theory has obviously much to offer). It has been suggested that economic considerations are crucial in formulating problems also in this environment, and can shed new light on important topics such as clustering and privacy.Because of its spontaneous emergence, the Internet is the first computational artifact that must be studied as a mysterious object (akin to matter, market, and intelligence) whose laws we must derive by observation, experiment, and the development of falsifiable theories. Recently, a plausible explanation of the skewed degree distributions one observes in the Internet topology was based on simple models of network creation, in which arriving nodes choose connections that achieve a favorable tradeoff among criteria such as last-mile distance and communication delays. These topics will be illustrated in terms of recent models and results by the speaker and co-authors, generally available at the speaker’s web page.
8 schema:editor Nf4f311e3d93f498586aaf05a25fa8e9f
9 schema:genre chapter
10 schema:inLanguage en
11 schema:isAccessibleForFree false
12 schema:isPartOf N7f44d427acb940aaad9dfc340dd313d7
13 schema:keywords Internet
14 Internet topology
15 agents
16 area
17 arena
18 artifacts
19 aspects
20 attention
21 availability
22 certain foundational aspects
23 characteristics
24 clustering
25 collaboration
26 communication delays
27 community's attention
28 competition
29 complex interactions
30 computational artifacts
31 concept
32 connection
33 consideration
34 creation
35 criteria
36 data mining
37 degree
38 degree distributions one
39 degree of collaboration
40 delay
41 development
42 distance
43 distribution one
44 diversity
45 economic agents
46 economic considerations
47 emergence
48 entities
49 environment
50 experiments
51 explanation
52 extent
53 falsifiable theory
54 favorable tradeoff
55 first computational artifact
56 fore certain foundational aspects
57 foundational aspects
58 game theory
59 generation
60 important topic
61 information repository
62 information retrieval
63 interaction
64 lack
65 lack of structure
66 last-mile distance
67 law
68 learning theory
69 light
70 line of research
71 lines
72 methodology
73 mining
74 model
75 mysterious object
76 nascent research area
77 nature
78 network creation
79 new generation
80 new light
81 nodes
82 objects
83 observations
84 one
85 order
86 pages
87 plausible explanation
88 privacy
89 problem
90 reality
91 recent models
92 repository
93 research
94 research area
95 research community's attention
96 results
97 retrieval
98 simple model
99 single entity
100 skewed degree distributions one
101 sophisticated techniques
102 speakers
103 speaker’s web page
104 spontaneous emergence
105 structure
106 technique
107 terms
108 theory
109 time
110 topic
111 topology
112 tradeoff
113 unprecedented extent
114 web pages
115 schema:name Learning the Internet
116 schema:pagination 396-396
117 schema:productId N2344f4e02e494596a4e386d231c6c697
118 N6abca0d3a52542a9aed161432066051a
119 schema:publisher N1ed9813c8c914a50861e62bcf9227a1c
120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011573230
121 https://doi.org/10.1007/3-540-45435-7_27
122 schema:sdDatePublished 2021-12-01T20:03
123 schema:sdLicense https://scigraph.springernature.com/explorer/license/
124 schema:sdPublisher N98b745b14eda4c9aa9806a4c56c1cf72
125 schema:url https://doi.org/10.1007/3-540-45435-7_27
126 sgo:license sg:explorer/license/
127 sgo:sdDataset chapters
128 rdf:type schema:Chapter
129 N0e8748b8a732449d89c0e81a2bd329ed schema:familyName Sloan
130 schema:givenName Robert H.
131 rdf:type schema:Person
132 N1ed9813c8c914a50861e62bcf9227a1c schema:name Springer Nature
133 rdf:type schema:Organisation
134 N2344f4e02e494596a4e386d231c6c697 schema:name dimensions_id
135 schema:value pub.1011573230
136 rdf:type schema:PropertyValue
137 N511c98e3613043efa27cab5c0c1459bb rdf:first N0e8748b8a732449d89c0e81a2bd329ed
138 rdf:rest rdf:nil
139 N6abca0d3a52542a9aed161432066051a schema:name doi
140 schema:value 10.1007/3-540-45435-7_27
141 rdf:type schema:PropertyValue
142 N7f44d427acb940aaad9dfc340dd313d7 schema:isbn 978-3-540-43836-6
143 978-3-540-45435-9
144 schema:name Computational Learning Theory
145 rdf:type schema:Book
146 N98b745b14eda4c9aa9806a4c56c1cf72 schema:name Springer Nature - SN SciGraph project
147 rdf:type schema:Organization
148 N9f1eb02528634a379aeaff50ee615019 schema:familyName Kivinen
149 schema:givenName Jyrki
150 rdf:type schema:Person
151 Nf2831a6e35e64714a316398d3ed491b1 rdf:first sg:person.013233165465.63
152 rdf:rest rdf:nil
153 Nf4f311e3d93f498586aaf05a25fa8e9f rdf:first N9f1eb02528634a379aeaff50ee615019
154 rdf:rest N511c98e3613043efa27cab5c0c1459bb
155 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
156 schema:name Information and Computing Sciences
157 rdf:type schema:DefinedTerm
158 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
159 schema:name Artificial Intelligence and Image Processing
160 rdf:type schema:DefinedTerm
161 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
162 schema:name Information Systems
163 rdf:type schema:DefinedTerm
164 sg:person.013233165465.63 schema:affiliation grid-institutes:grid.47840.3f
165 schema:familyName Papadimitriou
166 schema:givenName Christos
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013233165465.63
168 rdf:type schema:Person
169 grid-institutes:grid.47840.3f schema:alternateName UC Berkeley, USA
170 schema:name UC Berkeley, USA
171 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...