8,528 research outputs found
Convex Graph Invariant Relaxations For Graph Edit Distance
The edit distance between two graphs is a widely used measure of similarity
that evaluates the smallest number of vertex and edge deletions/insertions
required to transform one graph to another. It is NP-hard to compute in
general, and a large number of heuristics have been proposed for approximating
this quantity. With few exceptions, these methods generally provide upper
bounds on the edit distance between two graphs. In this paper, we propose a new
family of computationally tractable convex relaxations for obtaining lower
bounds on graph edit distance. These relaxations can be tailored to the
structural properties of the particular graphs via convex graph invariants.
Specific examples that we highlight in this paper include constraints on the
graph spectrum as well as (tractable approximations of) the stability number
and the maximum-cut values of graphs. We prove under suitable conditions that
our relaxations are tight (i.e., exactly compute the graph edit distance) when
one of the graphs consists of few eigenvalues. We also validate the utility of
our framework on synthetic problems as well as real applications involving
molecular structure comparison problems in chemistry.Comment: 27 pages, 7 figure
An algebraic analysis of the graph modularity
One of the most relevant tasks in network analysis is the detection of
community structures, or clustering. Most popular techniques for community
detection are based on the maximization of a quality function called
modularity, which in turn is based upon particular quadratic forms associated
to a real symmetric modularity matrix , defined in terms of the adjacency
matrix and a rank one null model matrix. That matrix could be posed inside the
set of relevant matrices involved in graph theory, alongside adjacency,
incidence and Laplacian matrices. This is the reason we propose a graph
analysis based on the algebraic and spectral properties of such matrix. In
particular, we propose a nodal domain theorem for the eigenvectors of ; we
point out several relations occurring between graph's communities and
nonnegative eigenvalues of ; and we derive a Cheeger-type inequality for the
graph optimal modularity
A Characterization of Uniquely Representable Graphs
The betweenness structure of a finite metric space is a pair
where is the so-called betweenness
relation of that consists of point triplets such that . The underlying graph of a betweenness structure
is the simple graph where
the edges are pairs of distinct points with no third point between them. A
connected graph is uniquely representable if there exists a unique metric
betweenness structure with underlying graph . It was implied by previous
works that trees are uniquely representable. In this paper, we give a
characterization of uniquely representable graphs by showing that they are
exactly the block graphs. Further, we prove that two related classes of graphs
coincide with the class of block graphs and the class of distance-hereditary
graphs, respectively. We show that our results hold not only for metric but
also for almost-metric betweenness structures.Comment: 16 pages (without references); 3 figures; major changes: simplified
proofs, improved notations and namings, short overview of metric graph theor
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