A system for building immunity in social networks

Social networks are susceptible to 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 paper 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 paper draws inspiration from two well established topics in the area of biology; spread of communicable diseases in human population and how human body builds immunity against diseases. 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 postulate that certain nodes in a social network exhibit similar behavior and can be defined as highly connected nodes in the network. We present analytical tools based on our network simulation, to correctly identify such nodes. Once such nodes are identified, we introduce the concept of weighting functions that can be attached to messages passing through such nodes. This paper describes how the spread of malicious information can be controlled by a community of such highly connected nodes, using the concept of weighted functions.