The Complexity of Minimum-Envy House Allocation Over Graphs
Palash Dey, Anubhav Dhar, Ashlesha Hota, Sudeshna Kolay
TL;DR
This work extends the classical House Allocation problem by embedding agents in a graph and restricting envy to neighbouring agents, formalizing two objectives: minimize envy and, among minimum-envy allocations, maximize happiness. It delivers polynomial-time algorithms for the case where each agent has a single preferred house, and establishes NP-hardness when preference lists grow to size two or when the graph is structured with small vertex covers, highlighting a sharp boundary from tractable to intractable regimes. The paper also contributes exact exponential algorithms that exploit sparsity, balanced separators, or small vertex covers, broadening the toolkit for structured graphs. Overall, the results reveal a rich complexity landscape markedly different from the classical setting and provide practical algorithms for graph-structured allocations.
Abstract
In this paper, we study a generalization of the House Allocation problem. In our problem, agents are represented by vertices of a graph $\GG_{\mathcal{A}} = (Å, E_Å)$, and each agent $a \in Å$ is associated with a set of preferred houses $\PP_a \subseteq \HH$, where $Å$ is the set of agents and $\HH$ is the set of houses. A house allocation is an injective function $φ: Å\rightarrow \HH$, and an agent $a$ envies a neighbour $a' \in N_{\GG_Å}(a)$ under $φ$ if $φ(a) \notin \PP_a$ and $φ(a') \in \PP_a$. We study two natural objectives: the first problem called \ohaa, aims to compute an allocation that minimizes the number of envious agents; the second problem called \ohaah aims to maximize, among all minimum-envy allocations, the number of agents who are assigned a house they prefer. These two objectives capture complementary notions of fairness and individual satisfaction. We design polynomial time algorithms for both problems for the variant when each agent prefers exactly one house. On the other hand, when the list of preferred houses for each agent has size at most $2$ then we show that both problems are \NP-hard even when the agent graph $\GG_Å$ is a complete bipartite graph. We also show that both problems are \NP-hard even when the number $|\mathcal H|$ of houses is equal to the number $|\mathcal A|$ of agents. This is in contrast to the classical {\sc House Allocation} problem, where the problem is polynomial time solvable when $|\mathcal H| = |\mathcal A|$. The two problems are also \NP-hard when the agent graph has a small vertex cover. On the positive side, we design exact algorithms that exploit certain structural properties of $\GG_Å$ such as sparsity, existence of balanced separators or existence of small-sized vertex covers, and perform better than the naive brute-force algorithm.