
New heuristics for burning graphs
The concept of graph burning and burning number (bn(G)) of a graph G was...
read it

Approximation Algorithms for Graph Burning
Numerous approaches study the vulnerability of networks against social c...
read it

The FastMap Algorithm for Shortest Path Computations
We present a new preprocessing algorithm for embedding the nodes of a gi...
read it

An Atypical Survey of TypicalCase Heuristic Algorithms
Heuristic approaches often do so well that they seem to pretty much alwa...
read it

Computing Consensus Curves
We consider the problem of extracting accurate average ant trajectories ...
read it

Multiple Node Immunisation for Preventing Epidemics on Networks by Exact Multiobjective Optimisation of Cost and ShieldValue
The general problem in this paper is vertex (node) subset selection with...
read it

Sequence stacking using dual encoder Seq2Seq recurrent networks
A widely studied nonpolynomial (NP) hard problem lies in finding a rout...
read it
Faster Heuristics for Graph Burning
Graph burning is a process of information spreading through the network by an agent in discrete steps. The problem is to find an optimal sequence of nodes which have to be given information so that the network is covered in least number of steps. Graph burning problem is NPHard for which two approximation algorithms and a few heuristics have been proposed in the literature. In this work, we propose three heuristics, namely, Backbone Based Greedy Heuristic (BBGH), Improved Cutting Corners Heuristic (ICCH) and Component Based Recursive Heuristic (CBRH). These are mainly based on Eigenvector centrality measure. BBGH finds a backbone of the network and picks vertex to be burned greedily from the vertices of the backbone. ICCH is a shortest path based heuristic and picks vertex to burn greedily from best central nodes. The burning number problem on disconnected graphs is harder than on the connected graphs. For example, burning number problem is easy on a path where as it is NPHard on disjoint paths. In practice, large networks are generally disconnected and moreover even if the input graph is connected, during the burning process the graph among the unburned vertices may be disconnected. For disconnected graphs, ordering of the components is crucial. Our CBRH works well on disconnected graphs as it prioritizes the components. All the heuristics have been implemented and tested on several benchmark networks including large networks of size more than 50K nodes. The experimentation also includes comparison to the approximation algorithms. The advantages of our algorithms are that they are much simpler to implement and also several orders faster than the heuristics proposed in the literature.
READ FULL TEXT
Comments
There are no comments yet.