Scalable preference aggregation in social networks

In social choice theory, preference aggregation refers to com-puting an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenar-ios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the ag-gregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using pref-erences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human sub-jects, and exploits network structure and homophily of re-lationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call ex-pected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this prop-erty. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodol-ogy attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is supe-rior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is accept-able in the case of social metrics