A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
Kevin Bello, Chuyang Ke, Jean Honorio

TL;DR
This paper investigates the effectiveness of the degree-4 sum-of-squares hierarchy for exact graph inference, revealing its connection to algebraic connectivity and deriving a new Cheeger-type bound.
Contribution
It provides a graph-theoretic analysis of the degree-4 SoS relaxation, linking dual solutions to Johnson and Kneser graphs and introducing a novel algebraic connectivity bound.
Findings
Degree-4 SoS relaxation improves exact recoverability.
Dual solutions relate to Johnson and Kneser graph edge weights.
New Cheeger-type bound for algebraic connectivity of signed graphs.
Abstract
Performing inference in graphs is a common task within several machine learning problems, e.g., image segmentation, community detection, among others. For a given undirected connected graph, we tackle the statistical problem of exactly recovering an unknown ground-truth binary labeling of the nodes from a single corrupted observation of each edge. Such problem can be formulated as a quadratic combinatorial optimization problem over the boolean hypercube, where it has been shown before that one can (with high probability and in polynomial time) exactly recover the ground-truth labeling of graphs that have an isoperimetric number that grows with respect to the number of nodes (e.g., complete graphs, regular expanders). In this work, we apply a powerful hierarchy of relaxations, known as the sum-of-squares (SoS) hierarchy, to the combinatorial problem. Motivated by empirical evidence on…
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Taxonomy
TopicsTopological and Geometric Data Analysis · Graph theory and applications · Computational Drug Discovery Methods
