When Graph Traversal Meets Structured Preferences: Unified Framework and Complexity Results
Guozhen Rong, Xin Li, Yongjie Yang

TL;DR
This paper introduces a unified framework linking preference restrictions in social choice to classical graph search paradigms, analyzing the computational complexity of related support recognition problems.
Contribution
It models preference profiles as graph traversals under six paradigms and establishes NP-hardness results for support recognition with structural restrictions.
Findings
NP-hardness for support recognition with at most k edges
NP-hardness for maximum degree k restrictions
Polynomial-time recognition for tree supports in DFS
Abstract
Preference restrictions have played a significant role in computational social choice. This paper studies a framework that connects preference restrictions with classical graph search paradigms. We model candidates as vertices of a graph and interpret the preference ordering of each voter as the outcome of traversing the graph according to a graph search. We focus on six fundamental paradigms: breadth-first search (BFS), depth-first search (DFS), breadth-first search (LexBFS), lexicographic depth-first (LexDFS), maximum cardinality search (MCS), and maximal neighborhood search (MNS). Within this framework, we study the problem of determining whether a given preference profile admits a graph support subject to structural restrictions, that is, whether there exists a graph such that each preference ordering can be generated by traversing the graph under the chosen paradigm. For all…
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.
