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Dynamic network analysis

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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 of time (modeled as a subset of the real numbers) to a set of graphs; for each time point there is a graph. This is akin to the definition of 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 vertices.[1]

Overview

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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.[2]

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 nodes (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as meta-networks or high-dimensional networks. In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.

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)[3] and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009).[4] 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 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.

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

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Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space.[5] 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.[6]

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

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A meta-network is a multi-mode, multi-link, multi-level network. Multi-mode means that there are many types of nodes; e.g., nodes people and locations. Multi-link means that there are many types of links; e.g., friendship and advice. Multi-level means that some nodes may be members of other nodes, such as a network composed of people and organizations and one of the links is who is a member of which organization.

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.[7] A more detailed formulation considers people, tasks, resources, knowledge, and organizations.[8] The ORA tool was developed to support meta-network analysis.[9]

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 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[10]
  • 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 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 control processes for networks over time
  • Developing algorithms to change distributions of links in networks over time
  • Developing algorithms to track groups in networks over time
  • Developing tools to extract or locate networks from various data sources such as texts
  • Developing statistically valid measurements on networks over time
  • Examining the robustness of network metrics under various types of missing data
  • Empirical studies of multi-mode multi-link multi-time period networks
  • Examining networks as probabilistic time-variant phenomena
  • Forecasting change in existing networks
  • Identifying trails through time given a sequence of networks
  • Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks
  • Studying random walks on temporal networks[11]
  • Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes[12]
  • Assessment of covert activity[13] and dark networks[14]
  • Citational analysis[15]
  • Social media analysis[16]
  • Assessment of public health systems[17]
  • Analysis of hospital safety outcomes[18]
  • Assessment of the structure of ethnic violence from news data[19]
  • Assessment of terror groups[20]
  • Online social decay of social interactions[21]
  • Modelling of classroom interactions in schools[22]

See also

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References

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  1. ^ Lotker, Z. (2021). Introduction to Evolving Social Networks. In Analyzing Narratives in Social Networks (pp. 167-185). Springer, Cham.
  2. ^ Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.
  3. ^ Purnamrita Sarkar and Andrew W. Moore. 2005. Dynamic social network analysis using latent space models. SIGKDD Explor. Newsl. 7, 2 (December 2005), 31-40.
  4. ^ Kathleen M. Carley, Michael K. Martin and Brian Hirshman, 2009, "The Etiology of Social Change," Topics in Cognitive Science, 1.4:621-650
  5. ^ Cao, Shaosheng; Lu, Wei; Xu, Qiongkai (2015-10-17). "GraRep: Learning Graph Representations with Global Structural Information". Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. CIKM '15. New York, NY, USA: Association for Computing Machinery. pp. 891–900. doi:10.1145/2806416.2806512. ISBN 978-1-4503-3794-6. S2CID 17341970.
  6. ^ Gürsoy, Furkan; Haddad, Mounir; Bothorel, Cécile (2023-10-07). "Alignment and stability of embeddings: Measurement and inference improvement". Neurocomputing. 553: 126517. arXiv:2101.07251. doi:10.1016/j.neucom.2023.126517. ISSN 0925-2312. S2CID 231632462.
  7. ^ David Krackhardt and Kathleen M. Carley, 1998, "A PCANS Model of Structure in Organization," In proceedings of the 1998 International Symposium on Command and Control Research and Technology, Monterey, CA, June 1998, Evidence Based Research, Vienna, VA, Pp. 113-119.
  8. ^ Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future," The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone (Eds.), Thousand Oaks, CA, Sage, Ch. 12: 206-220.
  9. ^ Kathleen M. Carley. 2014. "ORA: A Toolkit for Dynamic Network Analysis and Visualization," In Reda Alhajj and Jon Rokne (Eds.) Encyclopedia of Social Network Analysis and Mining, Springer.
  10. ^ Majdandzic, A.; et al. (2013). "Spontaneous recovery in dynamical networks". Nature Physics. 10: 34–38. doi:10.1038/nphys2819.
  11. ^ Michele Starnini, Andrea Baronchelli, Alain Barrat, 2012, Random walks on temporal networks. Phys. Rev. E 85, 056115, http://link.aps.org/doi/10.1103/PhysRevE.85.056115
  12. ^ René Pfitzner, Ingo Scholtes, Antonios Garas, Claudio Juan Tessone, Frank Schweitzer, 2012, "Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks", Physical Review Letters, Vol. 110, May 10, 2013.
  13. ^ Carley, Kathleen M., Michael K., Martin and John P. Hancock, 2009, "Dynamic Network Analysis Applied to Experiments from the Decision Architectures Research Environment," Advanced Decision Architectures for the Warfigher: Foundation and Technology, Ch. 4.
  14. ^ Everton, Sean, 2012, Disrupting Dark Networks, Cambridge University Press, New York, NY
  15. ^ Kas, Miray, Kathleen M. Carley and L. Richard Carley, 2012, "Who was Where, When? Spatiotemporal Analysis of Researcher Mobility in Nuclear Science," In proceedings of the International Workshop on Spatio Temporal data Integration and Retrieval (STIR 2012), held in conjunction with ICDE 2012, April 1, 2012, Washington D.C.
  16. ^ Carley, Kathleen. M., Jürgen Pfeffer, Huan Liu, Fred Morstatter, Rebecca Goolsby, 2013, Near Real Time Assessment of Social Media Using Geo-Temporal Network Analytics, In Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 25–28, 2013, Niagara Falls, Canada.
  17. ^ Merrill, Jacqueline, Mark G. Orr, Christie Y. Jeon, Rosalind V. Wilson, Jonathan Storrick and Kathleen M. Carley, 2012, "Topology of Local Health Officials’ Advice Networks: Mind the Gaps," Journal of Public Health Management Practice, 18(6): 602–608
  18. ^ Effken, Judith A., Sheila Gephart and Kathleen M. Carley, 2013, "Using ORA to Assess the Relationship of Handoffs to Quality and Safety Outcomes," Computers, Informatics, Nursing. 31(1): 36-44.
  19. ^ Van Holt, Tracy, Jeffrey C. Johnson, Jamie Brinkley, Kathleen M. Carley and Janna Caspersen, 2012, "Structure of ethnic violence in Sudan: an automated content, meta-network and geospatial analytical approach," Computational and Mathematical Organization Theory, 18:340-355.
  20. ^ Kenney, Michael J., John Horgan, Cale Horne, Peter Vining, Kathleen M. Carley, Michael Bigrigg, Mia Bloom, Kurt Braddock, 2012, Organizational adaptation in an activist network: Social networks, leadership, and change in al-Muhajiroun, Applied Ergonomics, 44(5):739-747.
  21. ^ M. Abufouda, K. A. Zweig ."A Theoretical Model for Understanding the Dynamics of Online Social Networks Decay". arXiv preprint arXiv:1610.01538.
  22. ^ Christian Bokhove, 2016, "Exploring classroom interaction with dynamic social network analysis", International Journal of Research & Method in Education, doi:10.1080/1743727X.2016.1192116.

Further reading

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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, 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. Decision Support Systems 43(4):1324-1347 (article 20[dead link‍])
  • 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
  • C. Aggarwal, K. Subbian, 2014, "Evolutionary Network Analysis: A Survey". ACM Computing Surveys, 47(1). (pdf)
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