Control of the flow of information in large-scale non-deterministic social networks is a complex problem requiring both a search for the optimal connection of a control system to the network, and a means of determining the required control signals. This paper formalizes the Network Control Problem (NCP) as a means of relating the field of diverse social network control subproblems. Additionally, this paper defines a novel NCP subproblem, the θ-Consensus Avoidance Problem (θ-CAP), as a next step towards solving the general NCP. Benchmark results for the θ-CAP using Artificial Neural Networks, Evolutionary Neural Networks, and heuristic methods are presented, and interesting areas of the problem space are identified.

Additional Metadata
Keywords Control, Influence, Optimization, Social networks
Persistent URL dx.doi.org/10.1007/s13278-015-0248-2
Journal Social Network Analysis and Mining
Citation
Runka, A. (Andrew), & White, A. (2015). Towards intelligent control of influence diffusion in social networks. Social Network Analysis and Mining, 5(1), 1–15. doi:10.1007/s13278-015-0248-2