2,698 research outputs found
Statistical Mechanics of the Hyper Vertex Cover Problem
We introduce and study a new optimization problem called Hyper Vertex Cover.
This problem is a generalization of the standard vertex cover to hypergraphs:
one seeks a configuration of particles with minimal density such that every
hyperedge of the hypergraph contains at least one particle. It can also be used
in important practical tasks, such as the Group Testing procedures where one
wants to detect defective items in a large group by pool testing. Using a
Statistical Mechanics approach based on the cavity method, we study the phase
diagram of the HVC problem, in the case of random regualr hypergraphs.
Depending on the values of the variables and tests degrees different situations
can occur: The HVC problem can be either in a replica symmetric phase, or in a
one-step replica symmetry breaking one. In these two cases, we give explicit
results on the minimal density of particles, and the structure of the phase
space. These problems are thus in some sense simpler than the original vertex
cover problem, where the need for a full replica symmetry breaking has
prevented the derivation of exact results so far. Finally, we show that
decimation procedures based on the belief propagation and the survey
propagation algorithms provide very efficient strategies to solve large
individual instances of the hyper vertex cover problem.Comment: Submitted to PR
Solving the Canonical Representation and Star System Problems for Proper Circular-Arc Graphs in Log-Space
We present a logspace algorithm that constructs a canonical intersection
model for a given proper circular-arc graph, where `canonical' means that
models of isomorphic graphs are equal. This implies that the recognition and
the isomorphism problems for this class of graphs are solvable in logspace. For
a broader class of concave-round graphs, that still possess (not necessarily
proper) circular-arc models, we show that those can also be constructed
canonically in logspace. As a building block for these results, we show how to
compute canonical models of circular-arc hypergraphs in logspace, which are
also known as matrices with the circular-ones property. Finally, we consider
the search version of the Star System Problem that consists in reconstructing a
graph from its closed neighborhood hypergraph. We solve it in logspace for the
classes of proper circular-arc, concave-round, and co-convex graphs.Comment: 19 pages, 3 figures, major revisio
Predicting Multi-actor collaborations using Hypergraphs
Social networks are now ubiquitous and most of them contain interactions
involving multiple actors (groups) like author collaborations, teams or emails
in an organizations, etc. Hypergraphs are natural structures to effectively
capture multi-actor interactions which conventional dyadic graphs fail to
capture. In this work the problem of predicting collaborations is addressed
while modeling the collaboration network as a hypergraph network. The problem
of predicting future multi-actor collaboration is mapped to hyperedge
prediction problem. Given that the higher order edge prediction is an
inherently hard problem, in this work we restrict to the task of predicting
edges (collaborations) that have already been observed in past. In this work,
we propose a novel use of hyperincidence temporal tensors to capture time
varying hypergraphs and provides a tensor decomposition based prediction
algorithm. We quantitatively compare the performance of the hypergraphs based
approach with the conventional dyadic graph based approach. Our hypothesis that
hypergraphs preserve the information that simple graphs destroy is corroborated
by experiments using author collaboration network from the DBLP dataset. Our
results demonstrate the strength of hypergraph based approach to predict higher
order collaborations (size>4) which is very difficult using dyadic graph based
approach. Moreover, while predicting collaborations of size>2 hypergraphs in
most cases provide better results with an average increase of approx. 45% in
F-Score for different sizes = {3,4,5,6,7}
- …