ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Using Node Centrality and Optimal Control to Maximize Information Diffusion in Social Networks

Kandhway, Kundan and Kuri, Joy (2017) Using Node Centrality and Optimal Control to Maximize Information Diffusion in Social Networks. In: IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 47 (7). pp. 1099-1110.

[img] PDF
IEEE_Tra_Sys_Man_Cyb_Sys_47-7_1099_2017.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: http://dx.doi.org/10.1109/TSMC.2016.2531690


We model information dissemination as a susceptible-infected epidemic process and formulate a problem to jointly optimize seeds for the epidemic and time varying resource allocation over the period of a fixed duration campaign running on a social network with a given adjacency matrix. Individuals in the network are grouped according to their centrality measure and each group is influenced by an external control function-implemented through advertisements-during the campaign duration. The aim is to maximize an objective function which is a linear combination of the reward due to the fraction of informed individuals at the deadline, and the aggregated cost of applying controls (advertising) over the campaign duration. We also study a problem variant with a fixed budget constraint. We set up the optimality system using Pontryagin's maximum principle from optimal control theory and solve it numerically using the forward-backward sweep technique. Our formulation allows us to compare the performance of various centrality measures (pagerank, degree, closeness, and betweenness) in maximizing the spread of a message in the optimal control framework. We find that degree-a simple and local measure-performs well on the three social networks used to demonstrate results: 1) scientific collaboration; 2) Slashdot; and 3) Facebook. The optimal strategy targets central nodes when the resource is scarce, but noncentral nodes are targeted when the resource is in abundance. Our framework is general and can be used in similar studies for other disease or information spread models-that can be modeled using a system of ordinary differential equations-for a network with a known adjacency matrix.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 21 Jul 2017 09:48
Last Modified: 21 Jul 2017 09:48
URI: http://eprints.iisc.ac.in/id/eprint/57447

Actions (login required)

View Item View Item