Dynamic network analysis - Revision history

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	*Kathleen M. Carley, 2003, "Dynamic Network Analysis" in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison, (Eds.) Committee on Human Factors, National Research Council, National Research Council. Pp. 133–145, Washington, DC.		*Kathleen M. Carley, 2003, "Dynamic Network Analysis" in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison, (Eds.) Committee on Human Factors, National Research Council, National Research Council. Pp. 133–145, Washington, DC.
	*Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future" The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone, Ch. 12, pp. 206–220, Thousand Oaks, CA, Sage.		*Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future" The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone, Ch. 12, pp. 206–220, Thousand Oaks, CA, Sage.
	*Kathleen M. Carley, Jana Diesner, Jeffrey Reminga, Maksim Tsvetovat, 2008, Toward an Interoperable Dynamic Network Analysis Toolkit, DSS Special Issue on Cyberinfrastructure for Homeland Security: Advances in Information Sharing, Data Mining, and Collaboration Systems. [http://www.sciencedirect.com/science/journal/01679236 Decision Support Systems] 43(4):1324-1347 ([http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8S-4KGG5P7-1&_user=4422&_coverDate=08%2F31%2F2007&_rdoc=20&_fmt=high&_orig=browse&_srch=doc-info(%23toc%235878%232007%23999569995%23665759%23FLA%23display%23Volume)&_cdi=5878&_sort=d&_docanchor=&_ct=52&_acct=C000059600&_version=1&_urlVersion=0&_userid=4422&md5=9459e84d7a8863039c7abd5065266250 article 20]{{dead link\|date=~~August~~ ~~2020~~\|bot=medic}}{{cbignore\|bot=medic}})		*Kathleen M. Carley, Jana Diesner, Jeffrey Reminga, Maksim Tsvetovat, 2008, Toward an Interoperable Dynamic Network Analysis Toolkit, DSS Special Issue on Cyberinfrastructure for Homeland Security: Advances in Information Sharing, Data Mining, and Collaboration Systems. [http://www.sciencedirect.com/science/journal/01679236 Decision Support Systems] 43(4):1324-1347 ([http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8S-4KGG5P7-1&_user=4422&_coverDate=08%2F31%2F2007&_rdoc=20&_fmt=high&_orig=browse&_srch=doc-info(%23toc%235878%232007%23999569995%23665759%23FLA%23display%23Volume)&_cdi=5878&_sort=d&_docanchor=&_ct=52&_acct=C000059600&_version=1&_urlVersion=0&_userid=4422&md5=9459e84d7a8863039c7abd5065266250 article 20]{{dead link\|date=January 2025\|bot=medic}}{{cbignore\|bot=medic}})
	*Terrill L. Frantz, Kathleen M. Carley. 2009, Toward A Confidence Estimate For The Most-Central-Actor Finding. Academy of Management Annual Conference, Chicago, IL, USA, 7–11 August. (Awarded the Sage Publications/RM Division Best Student Paper Award)		*Terrill L. Frantz, Kathleen M. Carley. 2009, Toward A Confidence Estimate For The Most-Central-Actor Finding. Academy of Management Annual Conference, Chicago, IL, USA, 7–11 August. (Awarded the Sage Publications/RM Division Best Student Paper Award)
	*Petter Holme, Jari Saramäki, 2011, "Temporal networks". https://arxiv.org/abs/1108.1780		*Petter Holme, Jari Saramäki, 2011, "Temporal networks". https://arxiv.org/abs/1108.1780

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← Previous revision		Revision as of 02:46, 10 February 2024
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	== Dynamic Representation Learning ==		== Dynamic Representation Learning ==
	Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space.<ref>{{Cite ~~journal~~ \|~~last~~=Cao \|~~first~~=Shaosheng \|last2=Lu \|first2=Wei \|last3=Xu \|first3=Qiongkai \|~~date=2015-10-17 \|title~~=GraRep: Learning Graph Representations with Global Structural Information \|~~url~~=~~https://doi.org/~~10~~.1145/2806416.2806512~~ \|~~journal~~=Proceedings of the 24th ACM International on Conference on Information and Knowledge Management \|series=CIKM '15 \|location=New York, NY, USA \|publisher=Association for Computing Machinery \|pages=891–900 \|doi=10.1145/2806416.2806512 \|isbn=978-1-4503-3794-6}}</ref> Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings.<ref>{{Cite journal \|~~last~~=Gürsoy \|~~first~~=Furkan \|last2=Haddad \|first2=Mounir \|last3=Bothorel \|first3=Cécile \|date=2023-10-07 \|title=Alignment and stability of embeddings: Measurement and inference improvement \|url=https://www.sciencedirect.com/science/article/pii/S0925231223006409 \|journal=Neurocomputing \|volume=553 \|pages=126517 \|doi=10.1016/j.neucom.2023.126517 \|issn=0925-2312\|arxiv=2101.07251 }}</ref>		Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space.<ref>{{Cite book \|last1=Cao \|first1=Shaosheng \|last2=Lu \|first2=Wei \|last3=Xu \|first3=Qiongkai \|chapter=GraRep: Learning Graph Representations with Global Structural Information \|date=2015-10-17 \|title=Proceedings of the 24th ACM International on Conference on Information and Knowledge Management \|chapter-url=https://doi.org/10.1145/2806416.2806512 \|series=CIKM '15 \|location=New York, NY, USA \|publisher=Association for Computing Machinery \|pages=891–900 \|doi=10.1145/2806416.2806512 \|isbn=978-1-4503-3794-6\|s2cid=17341970 }}</ref> Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings.<ref>{{Cite journal \|last1=Gürsoy \|first1=Furkan \|last2=Haddad \|first2=Mounir \|last3=Bothorel \|first3=Cécile \|date=2023-10-07 \|title=Alignment and stability of embeddings: Measurement and inference improvement \|url=https://www.sciencedirect.com/science/article/pii/S0925231223006409 \|journal=Neurocomputing \|volume=553 \|pages=126517 \|doi=10.1016/j.neucom.2023.126517 \|issn=0925-2312\|arxiv=2101.07251 \|s2cid=231632462 }}</ref>

	In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.		In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.

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← Previous revision		Revision as of 04:21, 28 December 2023
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	== Dynamic Representation Learning ==		== Dynamic Representation Learning ==
	Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space<ref>{{Cite journal \|last=Cao \|first=Shaosheng \|last2=Lu \|first2=Wei \|last3=Xu \|first3=Qiongkai \|date=2015-10-17 \|title=GraRep: Learning Graph Representations with Global Structural Information \|url=https://doi.org/10.1145/2806416.2806512 \|journal=Proceedings of the 24th ACM International on Conference on Information and Knowledge Management \|series=CIKM '15 \|location=New York, NY, USA \|publisher=Association for Computing Machinery \|pages=891–900 \|doi=10.1145/2806416.2806512 \|isbn=978-1-4503-3794-6}}</ref>. Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings<ref>{{Cite journal \|last=Gürsoy \|first=Furkan \|last2=Haddad \|first2=Mounir \|last3=Bothorel \|first3=Cécile \|date=2023-10-07 \|title=Alignment and stability of embeddings: Measurement and inference improvement \|url=https://www.sciencedirect.com/science/article/pii/S0925231223006409 \|journal=Neurocomputing \|volume=553 \|pages=126517 \|doi=10.1016/j.neucom.2023.126517 \|issn=0925-2312\|arxiv=2101.07251 }}</ref>.		Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space.<ref>{{Cite journal \|last=Cao \|first=Shaosheng \|last2=Lu \|first2=Wei \|last3=Xu \|first3=Qiongkai \|date=2015-10-17 \|title=GraRep: Learning Graph Representations with Global Structural Information \|url=https://doi.org/10.1145/2806416.2806512 \|journal=Proceedings of the 24th ACM International on Conference on Information and Knowledge Management \|series=CIKM '15 \|location=New York, NY, USA \|publisher=Association for Computing Machinery \|pages=891–900 \|doi=10.1145/2806416.2806512 \|isbn=978-1-4503-3794-6}}</ref> Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings.<ref>{{Cite journal \|last=Gürsoy \|first=Furkan \|last2=Haddad \|first2=Mounir \|last3=Bothorel \|first3=Cécile \|date=2023-10-07 \|title=Alignment and stability of embeddings: Measurement and inference improvement \|url=https://www.sciencedirect.com/science/article/pii/S0925231223006409 \|journal=Neurocomputing \|volume=553 \|pages=126517 \|doi=10.1016/j.neucom.2023.126517 \|issn=0925-2312\|arxiv=2101.07251 }}</ref>

	In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.		In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.

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← Previous revision		Revision as of 06:25, 25 December 2023
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	== Dynamic Representation Learning ==		== Dynamic Representation Learning ==
	Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space<ref>{{Cite journal \|last=Cao \|first=Shaosheng \|last2=Lu \|first2=Wei \|last3=Xu \|first3=Qiongkai \|date=2015-10-17 \|title=GraRep: Learning Graph Representations with Global Structural Information \|url=https://doi.org/10.1145/2806416.2806512 \|journal=Proceedings of the 24th ACM International on Conference on Information and Knowledge Management \|series=CIKM '15 \|location=New York, NY, USA \|publisher=Association for Computing Machinery \|pages=891–900 \|doi=10.1145/2806416.2806512 \|isbn=978-1-4503-3794-6}}</ref>. Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings<ref>{{Cite journal \|last=Gürsoy \|first=Furkan \|last2=Haddad \|first2=Mounir \|last3=Bothorel \|first3=Cécile \|date=2023-10-07 \|title=Alignment and stability of embeddings: Measurement and inference improvement \|url=https://www.sciencedirect.com/science/article/pii/S0925231223006409 \|journal=Neurocomputing \|volume=553 \|pages=126517 \|doi=10.1016/j.neucom.2023.126517 \|issn=0925-2312}}</ref>.		Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space<ref>{{Cite journal \|last=Cao \|first=Shaosheng \|last2=Lu \|first2=Wei \|last3=Xu \|first3=Qiongkai \|date=2015-10-17 \|title=GraRep: Learning Graph Representations with Global Structural Information \|url=https://doi.org/10.1145/2806416.2806512 \|journal=Proceedings of the 24th ACM International on Conference on Information and Knowledge Management \|series=CIKM '15 \|location=New York, NY, USA \|publisher=Association for Computing Machinery \|pages=891–900 \|doi=10.1145/2806416.2806512 \|isbn=978-1-4503-3794-6}}</ref>. Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings<ref>{{Cite journal \|last=Gürsoy \|first=Furkan \|last2=Haddad \|first2=Mounir \|last3=Bothorel \|first3=Cécile \|date=2023-10-07 \|title=Alignment and stability of embeddings: Measurement and inference improvement \|url=https://www.sciencedirect.com/science/article/pii/S0925231223006409 \|journal=Neurocomputing \|volume=553 \|pages=126517 \|doi=10.1016/j.neucom.2023.126517 \|issn=0925-2312\|arxiv=2101.07251 }}</ref>.

	In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.		In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.

165.225.200.187: Added section on dynamic latent representations for networks

2023-12-22T20:49:25Z

Added section on dynamic latent representations for networks

← Previous revision		Revision as of 20:49, 22 December 2023
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	== Overview ==		== Overview ==
	There are two aspects of this field. The first is the [[statistical analysis]] of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of [[uncertainty]]. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network.<ref>Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.</ref>		[[Image:DynamicNetworkAnalysisExample.jpg\|right\|340px\|thumb\|An example of a multi-entity, multi-network, dynamic network diagram]]There are two aspects of this field. The first is the [[statistical analysis]] of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of [[uncertainty]]. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network.<ref>Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.</ref>

	DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of [[Node (networking)\|nodes]] (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as		DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of [[Node (networking)\|nodes]] (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as
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	DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005)<ref name="social"/> and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009).<ref name="Etiology"/> From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are [[wikt:static\|static]], nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.		DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005)<ref name="social"/> and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009).<ref name="Etiology"/> From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are [[wikt:static\|static]], nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.
	[[Image:DynamicNetworkAnalysisExample.jpg\|right\|340px\|thumb\|An example of a multi-entity, multi-network, dynamic network diagram]]

	There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.		There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.

			== Dynamic Representation Learning ==
			Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space<ref>{{Cite journal \|last=Cao \|first=Shaosheng \|last2=Lu \|first2=Wei \|last3=Xu \|first3=Qiongkai \|date=2015-10-17 \|title=GraRep: Learning Graph Representations with Global Structural Information \|url=https://doi.org/10.1145/2806416.2806512 \|journal=Proceedings of the 24th ACM International on Conference on Information and Knowledge Management \|series=CIKM '15 \|location=New York, NY, USA \|publisher=Association for Computing Machinery \|pages=891–900 \|doi=10.1145/2806416.2806512 \|isbn=978-1-4503-3794-6}}</ref>. Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings<ref>{{Cite journal \|last=Gürsoy \|first=Furkan \|last2=Haddad \|first2=Mounir \|last3=Bothorel \|first3=Cécile \|date=2023-10-07 \|title=Alignment and stability of embeddings: Measurement and inference improvement \|url=https://www.sciencedirect.com/science/article/pii/S0925231223006409 \|journal=Neurocomputing \|volume=553 \|pages=126517 \|doi=10.1016/j.neucom.2023.126517 \|issn=0925-2312}}</ref>.

			In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.

	==Meta-network==		==Meta-network==

GayaPapaya: Added to the beginning definition for clarity.

2022-03-21T08:07:46Z

Added to the beginning definition for clarity.

← Previous revision		Revision as of 08:07, 21 March 2022
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	{{More citations needed\|date=April 2009}}		{{More citations needed\|date=April 2009}}
	'''Dynamic network analysis''' ('''DNA''') is an emergent scientific field that brings together traditional [[social network analysis]] (SNA), [[link analysis]] (LA), [[social simulation]] and [[multi-agent systems]] (MAS) within [[network science]] and [[network theory]].		'''Dynamic network analysis''' ('''DNA''') is an emergent scientific field that brings together traditional [[social network analysis]] (SNA), [[link analysis]] (LA), [[social simulation]] and [[multi-agent systems]] (MAS) within [[network science]] and [[network theory]]. Dynamic networks are a [[Function (mathematics)\|function]] of [[time]] (modeled as a [[subset]] of the [[Real number\|real numbers]]) to a set of [[Graph theory\|graphs]]; for each time point there is a graph. This is akin to the definition of '''[[Dynamical system\|dynamical systems]]''', in which the function is from time to an ambient space, where instead of ambient space time is translated to relationships between pairs of [[Vertex (graph theory)\|vertices]].<ref>Lotker, Z. (2021). Introduction to Evolving Social Networks. In ''Analyzing Narratives in Social Networks'' (pp. 167-185). Springer, Cham.</ref>

	==Overview==		== Overview ==
	There are two aspects of this field. The first is the [[statistical analysis]] of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of [[uncertainty]]. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network.<ref>Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.</ref>		There are two aspects of this field. The first is the [[statistical analysis]] of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of [[uncertainty]]. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network.<ref>Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.</ref>

MrOllie: rv havlin COI / citespam IP

2022-02-01T18:18:23Z

rv havlin COI / citespam IP

← Previous revision		Revision as of 18:18, 1 February 2022
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	There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.		There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.

	==Connectedness==
	The connectedness of temporal networks can be studied by a mapping to the directed percolation problem<ref name=Dynamic_networks>{{cite journal \|last1=R. Parshani, M. Dickison, R. Cohen, H.E. Stanley, S. Havlin \|title=Dynamic networks and directed percolation \|journal=Europhys. Lett. \|date=2010 \|volume=90 \|issue=3 \|page=38004\|doi=10.1209/0295-5075/90/38004 \|arxiv=0901.4563 \|bibcode=2010EL.....9038004P \|s2cid=7990127 }}</ref>
	. The probability of temporal links has a critical value below which the connectedness is zero while above almost all nodes can be connected. This can be regarded as a phase transition.<ref name=Dynamic_networks></ref>

	==Predictability==
	An entropy framework, based on combined topological and temporal regularities of links, has been developed for quantifying the predictability of temporal networks.<ref>{{cite journal \|last1=D Tang, W Du, L Shekhtman, Y Wang, S Havlin, X Cao, G Yan \|title=Predictability of real temporal networks \|journal=National Science Review \|year=2020 \|volume=7 \|issue=5 \|page=929-937\|doi=10.1093/nsr/nwaa015 \| pmc=8288877\|arxiv=2007.04828 \|doi-access=free }}</ref>

	==Meta-network==		==Meta-network==
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	While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources.<ref name="pcans"/> A more detailed formulation considers people, tasks, resources, knowledge, and organizations.<ref name="smartAgents"/> The ORA tool was developed to support meta-network analysis.<ref name="toolkit" />		While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources.<ref name="pcans"/> A more detailed formulation considers people, tasks, resources, knowledge, and organizations.<ref name="smartAgents"/> The ORA tool was developed to support meta-network analysis.<ref name="toolkit" />

	==Phase diagram==
	The phase diagram of a model of a dynamic networks which include stability, non stability and metastabily regimes has been found in Majdandzik et al.<ref name="Spontaneous_recovery in_dynamical_networks">{{cite journal\|author=Majdandzic, A.\|title=Spontaneous recovery in dynamical networks\|journal=Nature Physics\|volume=10\|pages=34–38\|year=2013\|doi=10.1038/nphys2819\|display-authors=etal\|doi-access=free}}</ref>

	==Illustrative problems that people in the DNA area work on==		==Illustrative problems that people in the DNA area work on==
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	* Developing metrics and statistics to assess and identify change within and across networks.		* Developing metrics and statistics to assess and identify change within and across networks.
	* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]		* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
	* Developing and testing theory of network change, evolution, adaptation, decay<ref ~~name~~=~~"Spontaneous_recovery~~ ~~in_dynamical_networks">~~</ref>		* Developing and testing theory of network change, evolution, adaptation, decay<ref>{{cite journal\|author=Majdandzic, A.\|title=Spontaneous recovery in dynamical networks\|journal=Nature Physics\|volume=10\|pages=34–38\|year=2013\|doi=10.1038/nphys2819\|display-authors=etal\|doi-access=free}}</ref>
			* Developing and validating formal models of network generation and evolution
	* Developing techniques to visualize network change overall or at the node or group level		* Developing techniques to visualize network change overall or at the node or group level
	* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes		* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes

213.137.64.97: /* Illustrative problems that people in the DNA area work on */

2022-01-31T20:47:35Z

Illustrative problems that people in the DNA area work on

← Previous revision		Revision as of 20:47, 31 January 2022
Line 32:		Line 32:
	* Developing metrics and statistics to assess and identify change within and across networks.		* Developing metrics and statistics to assess and identify change within and across networks.
	* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]		* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
	* Developing and testing theory of network change, evolution, adaptation, decay<ref name=""></ref>		* Developing and testing theory of network change, evolution, adaptation, decay<ref name="Spontaneous_recovery in_dynamical_networks"></ref>
	* Developing techniques to visualize network change overall or at the node or group level		* Developing techniques to visualize network change overall or at the node or group level
	* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes		* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes

213.137.64.97: /* Illustrative problems that people in the DNA area work on */

2022-01-31T20:46:32Z

Illustrative problems that people in the DNA area work on

← Previous revision		Revision as of 20:46, 31 January 2022
Line 32:		Line 32:
	* Developing metrics and statistics to assess and identify change within and across networks.		* Developing metrics and statistics to assess and identify change within and across networks.
	* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]		* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
	* Developing and testing theory of network change, evolution, adaptation, decay		* Developing and testing theory of network change, evolution, adaptation, decay<ref name=""></ref>
	* Developing techniques to visualize network change overall or at the node or group level		* Developing techniques to visualize network change overall or at the node or group level
	* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes		* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes

213.137.64.97: /* Illustrative problems that people in the DNA area work on */

2022-01-31T20:44:29Z

Illustrative problems that people in the DNA area work on

← Previous revision		Revision as of 20:44, 31 January 2022
Line 32:		Line 32:
	* Developing metrics and statistics to assess and identify change within and across networks.		* Developing metrics and statistics to assess and identify change within and across networks.
	* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]		* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
	* Developing and testing theory of network change, evolution, adaptation, decay~~<ref name="Spontaneous_recovery in_dynamical_networks"></ref>~~		* Developing and testing theory of network change, evolution, adaptation, decay
	* Developing and validating formal models of network generation and evolution
	* Developing techniques to visualize network change overall or at the node or group level		* Developing techniques to visualize network change overall or at the node or group level
	* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes		* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes