Modeling Spread of Preferences in Social Networks for Sampling-based Preference Aggregation
Swapnil Dhamal, Rohith D. Vallam, Y. Narahari

TL;DR
This paper explores how social network data can be used to efficiently sample and aggregate individual preferences, proposing algorithms for selecting representative nodes that improve preference aggregation accuracy.
Contribution
It introduces models of preference distribution in social networks and develops algorithms with performance guarantees for selecting representative nodes for preference aggregation.
Findings
Network-based sampling outperforms random polling for personal preferences.
Algorithms perform well across various voting rules.
Preference aggregation effectiveness varies with topic type.
Abstract
Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population. We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes. We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network. We hence propose an appropriate objective function for the problem of selecting best representative nodes. We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice. We…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
