On Studying Information Dissemination in Social-Physical Interdependent Networks

Most existing studies for information dissemination in the online social network are based on variations of the classical epidemic model. In such a model, nodes recursively infect, or share information to, their neighboring nodes with a certain probability. The higher degree a node has, the more likely it gets infected by its neighbors. Although widely accepted, we found there are certain discrepancies between existing epidemic models and social interactions in reality. Firstly, the real-world social network is actually a dual-layered network, where a person shares information online to her online friends, and also offline to her real-life friends. More importantly, since a computer do not automatically share information, a computer exposed to information will not effectively receive it (i.e., getting infected and starting to infect others) unless its user receives it. Secondly, contrary to the epidemic model, the more friends a person has, the less likely she is going to effectively receive a certain piece of message (just imagine how easily a message can be flushed and ignored by a human user because of overwhelming newer information). In other words, in social networks, the infection rate of a node may not be positively correlated with its degree. Based on these observations, we develop the social-physical interdependent (SPI) model to capture and analyze the unique characters of social networks. Our study provides new observations, and sheds light on a new direction for the study of information dissemination in social networks.