The Algorithmic Network Science Group (ANSG) at West Point’s Network Science Center is studying disease by running simulations to identify “spreaders” in complex networks through the use of  the Susceptible-Infected-Recovered (SIR) model. This has the potential for creating novel algorithms to apply not only to biology, but to counter-insurgency, intelligence analysis, cyber security and social networking as well.

 To understand the force of infection for each susceptible unit in an infectious population, the researchers run simulations with different nodes as the initiator in order to identify the “best spreaders of disease.” The results differ depending on measurable variables – for example, with nodes connected to very few other nodes, the disease may die out quickly. ANSG argues that the insight gained from applying the SIR model holds implications for how researchers can measure the “best spreaders of information” operating in complex networks.

 Examining how information is optimally spread requires the study of the degree of influence one node has in relation to other nodes, or as ANSG might say, how influential units in a network act in a changing environment. Running simulations on a small network seems fairly easy, but “simulations become computationally expensive when the size of the network becomes very large.”

 Using an inferential statistics framework is a powerful way to enable researchers to scale analysis with massive data sets. For example, behavioral intelligence tools allow analysts to ingest millions of complex electronic communications within an organization, aggregate and standardize the data, and identify patterns of normal behavior. By analyzing communication patterns, analysts can isolate anomalies and provide perspective on inter- and intra-communication behaviors across networks.

 In particular, behavioral intelligence can identify the level of influence of each actor in relation to others. Through the process of analyzing digital trails to uncover trends in communication between nodes, users are able to investigate, monitor, and detect relevant activities in large amounts of data, or as ANSG might say – identify “spreaders” in the population who carry the most influence.

 Post by: Dan Potocki, who directs the Initiatives Group at Praescient Analytics, a collective of subject matter experts and thought leaders committed to solving the biggest problems with advanced analysis technologies.