Information Epidemics and Social Networking

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Communicable disease models have been studied using classical mathematical differential equations for a long time now. It is important to study the communicable disease models, so that one can come up with a good response system to contain the spread of viruses. Social networks are susceptible to the rapid spread of malicious information, commonly referred to as rumors. Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. This chapter describes a method for identifying highly connected nodes in a social network and using these nodes to build immunity against such malicious information. To describe this method, this chapter draws inspiration from two well-established topics in the area of biology: one is the spread of communicable diseases in human population and second is how human body builds immunity against diseases as described in Chap. 5. In case of communicable diseases, it would be very simplistic if we only consider that an infected node can transmit its disease to its nearest neighbors. More realistically speaking, it is possible that an infected node can develop random links with other nodes in the system. The spread of communicable diseases is controlled by both these factors. An infected node with capability to have several random links is capable of spreading the disease through the network faster. We can postulate that certain nodes in a social network exhibit similar behavior and can be defined as highly connected nodes in the network. Once such nodes are identified, the concept of weighting functions is introduced that can be attached to messages passing through such nodes. This chapter describes how the spread of malicious information can be controlled by a community of such highly connected nodes, using the concept of weighted functions.