DualCast: Friendship-Preference Co-evolution Forecasting for Attributed Networks

If a person changes their interests and opinions, how will that change and affect their friendships? Conversely, when a person changes who they are friends with, how will that affect their interests and opinions. Clearly, the person's relationships and interests are related. For example, if several of a person's friends are smokers, but they themselves are not, they are likely to either start smoking as well or to reduce their relationships with the smokers and make new, non-smoking friends. We propose DualCast, a method for predicting the evolution of friendship edges between nodes, as well as the attribute values (which represent opinions and preferences), of nodes in an attributed network. One of the main contributions of the present study is the ability to assume and estimate two scores for each node: the influence (its power to influence neighbors) and its susceptibility (how easily it can be influenced).

Our DualCast has the following novel benefits: (A) Expressive: it can capture when links between nodes are dropped, as well as polarization that occurs with changes in interests, (B) Scalable: its performance is linear with input size, (C) Accurate: it is up to 8% more accurate in forecasting links between nodes, and up to 20% more accurate for attribute-values, when tested on publicly available, real datasets with 100K nodes.