Dynamic network analysis

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.

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.

Meta-network

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

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 studies 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 Studying random walks on temporal networks Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes Assessment of covert activity and dark networks Citational analysis Social media analysis Assessment of public health systems Analysis of hospital safety outcomes Assessment of the structure of ethnic violence from news data Assessment of terror groups Online social decay of social interactions Modelling of classroom interactions in schools

References

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) 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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