Dynamic Network Analysis
Encyclopedia
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.
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.
Kathleen Carley
, of Carnegie Mellon University
, is a leading authority in this field.
Link Analysis
In network theory, link analysis is a data-analysis technique used to evaluate relationships between nodes. Relationships may be identified among various types of nodes , including organizations, people and transactions...
(LA) and multi-agent systems (MAS) within network science
Network science
Network science is a new and emerging scientific discipline that examines the interconnections among diverse physical or engineered networks, information networks, biological networks, cognitive and semantic networks, and social networks. This field of science seeks to discover common principles,...
and network theory
Network theory
Network theory is an area of computer science and network science and part of graph theory. It has application in many disciplines including statistical physics, particle physics, computer science, biology, economics, operations research, and sociology...
. 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
Uncertainty
Uncertainty is a term used in subtly different ways in a number of fields, including physics, philosophy, statistics, economics, finance, insurance, psychology, sociology, engineering, and information science...
. 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.
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
Node (networking)
In communication networks, a node is a connection point, either a redistribution point or a communication endpoint . The definition of a node depends on the network and protocol layer referred to...
(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
Static
Static has several meanings:* Static electricity, a net charge of an object** The triboelectric effect, e.g. from shoes rubbing carpet* White noise, a random signal with a flat power spectral density** Noise , a type of radio interference...
, 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. See Computer simulation and organizational studiesComputer Simulation and Organizational StudiesComputer simulation is a prominent method in organizational studies and strategic management. While there are many uses for computer simulation , most academics in the fields of strategic management and organizational studies have used computer simulation to understand how organizations or firms...
- 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
Kathleen Carley
Kathleen M. Carley is an American social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Institute for Software Research International at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the...
, of Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania, United States....
, is a 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, 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)
See also
- Network dynamicsNetwork dynamicsNetwork dynamics is the study of networks that change in time.These networks could be from the fields of biology, sociology, economics, computer science, graph theory etc.For a dynamical systems approach to network dynamics, see sequential dynamical system....
- Sequential dynamical systemSequential dynamical systemSequential dynamical systems are a class of graph dynamical systems. They are discrete dynamical systems which generalize many aspects of for example classical cellular automata, and they provide a framework for studying asynchronous processes over graphs...
- Kathleen CarleyKathleen CarleyKathleen M. Carley is an American social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Institute for Software Research International at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the...
- Keyhubs
- Network scienceNetwork scienceNetwork science is a new and emerging scientific discipline that examines the interconnections among diverse physical or engineered networks, information networks, biological networks, cognitive and semantic networks, and social networks. This field of science seeks to discover common principles,...
- INSNA