Dynamic (Social) Networks

DecisionPath's Dynamic Network (DynNet) solution helps organizations model social networks that drive performance and strategic risk. Applications include improving performance for innovation and competitive analysis, as well as counter-terrorism and pandemic preparedness.

Problem: Understanding and Influencing the Structure and Behavior of Critical Social Networks

Social network theory models relationships and interactions between individuals and groups. The core idea is that information, influence, and materiel flow between people or groups (nodes) via connective links such as reporting structures, communication and contact pathways, funding methods, and logistics support channels (arcs). Modeling the structure and behavior of such networks can help organizations better understand and influence their operations and performance. Applications of network analysis are diverse.

For example, businesses face continual pressures to improve performance (with fewer resources) and to avoid being blindsided by competitors and market changes. Social network analysis can help address these challenges through monitoring and optimizing the flow and synthesis of information and ideas within your organizations.

In the Government sector, Homeland Security authorities face critical challenges in countering terrorist groups, whose structure and operations are highly decentralized and difficult to track. Network models can expedite the fusion and visualization of intelligence of terrorist activities, which is critical to identifying and interdicting emerging security threats.

Public health agencies face similarly daunting problems in preparing for outbreaks of contagious diseases such as avian flu or SARS. Here, it is critical to model the expected progression of disease across highly mobile populations, and anticipate the impact of containment strategies such as quarantines and vaccinations.

Numerous software packages exist for depicting networks graphically and computing statistical metrics such as densities of arcs per node. While clearly valuable, most of these tools are limited to modeling homogenous networks, composed of a single type of node and link. They also typically model networks statically, producing snapshots at particular times. These constraints impede you from fully leveraging available information about your network and the behaviors of its elements.

Our Solution Approach: Dynamic Networks
ForeTell DynNet offers a robust and flexible heterogeneous dynamic network framework. Dynamic social networks depict complex situations as continuous movies rather than discrete photographs. Without dynamics, it is difficult to understand how and why networks change over time. More importantly, without dynamic models, you cannot project and assess the likely consequences of prospective strategies to influence your network's structure and behavior to your advantage. Example strategies include initiatives to enhance cross-functional collaboration, inhibit terrorist movements, or stockpile medicines at regional depots.

DynNet leverages ForeTell's simulation-based scenario planning capabilities. First, it helps you quickly describe your network as it exists today, including relevant environmental factors. You then define scenarios that extend this description with alternate assumptions about future conditions. If desired, you can add candidate strategies for modifying the network.

Given these scenarios, DynNet projects the likely evolution of your network over time. Your then apply ForeTell's analytic tools to explore and compare these projected outcomes. DynNet projections help you identify opportunities and threats, as well as robust intervention strategies likely to perform well across plausible alternate futures.

Model Quickly and Easily with Pre-Made Building Blocks
DynNet implements a core statistical dynamic model described by R. Albert and A.L. Barbasi, leading researchers in network theory. This behavioral model provides an invaluable bottom-up perspective, describing network change in terms of new actors joining networks; other actors leaving; and relationships forming, intensifying, or disappearing. (By way of contrast, top-down models such as macroeconomics or epidemiology study change more broadly, for entire populations, not individuals.)

DecisionPath then works with you to extend this baseline, rapidly tailoring DynNet to describe the structure and behavioral dynamics for the networks of interest to you. The result is a customized dynamic network that models relevant:

  • People, organizations, locations, and materiel (node types)
  • Lines of authority; communication, transportation, funding, and supply channels (arc types)
  • Properties, such as demographic, social, political, and cultural affiliations (node attributes)
  • Data sources, corroborating evidence, annotations and hypotheses (node and arc metadata)
  • Environmental forces and trends, disruptive events, behavior patterns of actors (dynamics)

DynNet's novel dynamic network model behavioral modeling and dynamic networks to better leverage available business intelligence to help you to understand and influence your social networks.