Abstract
In this paper the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. Having the network structure represented in an explicit manner within the extended network enhances expressiveness and enables to model adaptation of the base network by dynamics within the reified network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated by a number of known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.
| Original language | English |
|---|---|
| Title of host publication | Theory and Practice of Natural Computing |
| Subtitle of host publication | 7th International Conference, TPNC 2018, Proceedings |
| Editors | Carlos Martín-Vide, Miguel A. Vega-Rodríguez, David Fagan, Michael O’Neill |
| Publisher | Springer - Verlag |
| Pages | 344-358 |
| Number of pages | 15 |
| ISBN (Electronic) | 9783030040703 |
| ISBN (Print) | 9783030040697 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018 - Dublin, Ireland Duration: 12 Dec 2018 → 14 Dec 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11324 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018 |
|---|---|
| Country | Ireland |
| City | Dublin |
| Period | 12/12/18 → 14/12/18 |
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Keywords
- Adaptation principle
- Network reification
Cite this
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Network reification as a unified approach to represent network adaptation principles within a network. / Treur, Jan.
Theory and Practice of Natural Computing: 7th International Conference, TPNC 2018, Proceedings. ed. / Carlos Martín-Vide; Miguel A. Vega-Rodríguez; David Fagan; Michael O’Neill. Springer - Verlag, 2018. p. 344-358 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11324 LNCS).Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Network reification as a unified approach to represent network adaptation principles within a network
AU - Treur, Jan
PY - 2018
Y1 - 2018
N2 - In this paper the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. Having the network structure represented in an explicit manner within the extended network enhances expressiveness and enables to model adaptation of the base network by dynamics within the reified network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated by a number of known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.
AB - In this paper the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. Having the network structure represented in an explicit manner within the extended network enhances expressiveness and enables to model adaptation of the base network by dynamics within the reified network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated by a number of known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.
KW - Adaptation principle
KW - Network reification
UR - http://www.scopus.com/inward/record.url?scp=85058546929&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058546929&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04070-3_27
DO - 10.1007/978-3-030-04070-3_27
M3 - Conference contribution
SN - 9783030040697
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 344
EP - 358
BT - Theory and Practice of Natural Computing
A2 - Martín-Vide, Carlos
A2 - Vega-Rodríguez, Miguel A.
A2 - Fagan, David
A2 - O’Neill, Michael
PB - Springer - Verlag
ER -