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). 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 are larger dynamic multi-mode, multi-plex networks, and may contain varying levels of uncertainty.

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.

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 validating simulations to study network change, evolution, adaptation, decay...

• Developing and validating formal models of network generation and evolution

• Developing and testing theory of network change, evolution, adaptation, decay...

• 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 the leading authority in this field.

Further reading

  • 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, 2005-forthcoming, Toward an Interoperable Dynamic Network Analysis Toolkit, DSS Special Issue on Cyberinfrastructure for Homeland Security: Advances in Information Sharing, Data Mining, and Collaboration Systems.

External links

social network is a social structure made of nodes (which are generally individuals or organizations) that are tied by one or more specific types of interdependency, such as values, visions, idea, financial exchange, friends, kinship, dislike, conflict, trade, web links, sexual
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Network analysis is the analysis of networks through network theory (or more generally graph theory). The networks may be social, transportation, biological, Internet networks, etc.
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Agent Based Model is a specific individual based computational model for computer simulation extensively related to the theme in complex systems, emergence, Monte Carlo Method, computational sociology, multi agent systems, and evolutionary programming.
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Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities.
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Uncertainty is a term used in subtly different ways in a number of fields, including philosophy, statistics, economics, finance, insurance, psychology, engineering and science.
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A node is a device that is connected as part of a computer network. For example, a node may be a computer, personal digital assistant, cell phone, router, switch, or hub.
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Static has several meanings:
  • Static electricity, a net charge of an object
  • The triboelectric effect, e.g. from shoes rubbing carpet

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Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania, United States. It began as the Carnegie Technical Schools, founded by Andrew Carnegie in 1900. In 1912, the school became Carnegie Institute of Technology and began granting four-year degrees.
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