Dynamic network analysis

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Dynamic network analysis

Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA) and multi-agent systems (MAS) within network science and network theory. 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 DNA taken the domain of time into account. One of the most notable and earliest case of 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.[1]

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). 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. 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.

An example of a multi-entity, multi-network, dynamic network diagram

Illustrative problems that people in the DNA area work on

• Developing metrics and statistics to assess and identify change within and across networks.
• 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 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

Kathleen Carley, of Carnegie Mellon University, is a leading authority in this field.

• 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)
• 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)

References

1. ^ Identity and control: a structural theory of social action By Harrison C. White

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