3,454 research outputs found
The domination number of on-line social networks and random geometric graphs
We consider the domination number for on-line social networks, both in a
stochastic network model, and for real-world, networked data. Asymptotic
sublinear bounds are rigorously derived for the domination number of graphs
generated by the memoryless geometric protean random graph model. We establish
sublinear bounds for the domination number of graphs in the Facebook 100 data
set, and these bounds are well-correlated with those predicted by the
stochastic model. In addition, we derive the asymptotic value of the domination
number in classical random geometric graphs
Collaborative Learning of Stochastic Bandits over a Social Network
We consider a collaborative online learning paradigm, wherein a group of
agents connected through a social network are engaged in playing a stochastic
multi-armed bandit game. Each time an agent takes an action, the corresponding
reward is instantaneously observed by the agent, as well as its neighbours in
the social network. We perform a regret analysis of various policies in this
collaborative learning setting. A key finding of this paper is that natural
extensions of widely-studied single agent learning policies to the network
setting need not perform well in terms of regret. In particular, we identify a
class of non-altruistic and individually consistent policies, and argue by
deriving regret lower bounds that they are liable to suffer a large regret in
the networked setting. We also show that the learning performance can be
substantially improved if the agents exploit the structure of the network, and
develop a simple learning algorithm based on dominating sets of the network.
Specifically, we first consider a star network, which is a common motif in
hierarchical social networks, and show analytically that the hub agent can be
used as an information sink to expedite learning and improve the overall
regret. We also derive networkwide regret bounds for the algorithm applied to
general networks. We conduct numerical experiments on a variety of networks to
corroborate our analytical results.Comment: 14 Pages, 6 Figure
What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks
In this paper, we analyze the quality of a large class of simple dynamic
resource allocation (DRA) strategies which we name priority planning. Their aim
is to control an undesired diffusion process by distributing resources to the
contagious nodes of the network according to a predefined priority-order. In
our analysis, we reduce the DRA problem to the linear arrangement of the nodes
of the network. Under this perspective, we shed light on the role of a
fundamental characteristic of this arrangement, the maximum cutwidth, for
assessing the quality of any priority planning strategy. Our theoretical
analysis validates the role of the maximum cutwidth by deriving bounds for the
extinction time of the diffusion process. Finally, using the results of our
analysis, we propose a novel and efficient DRA strategy, called Maximum
Cutwidth Minimization, that outperforms other competing strategies in our
simulations.Comment: 18 pages, 3 figure
Structures in supercritical scale-free percolation
Scale-free percolation is a percolation model on which can be
used to model real-world networks. We prove bounds for the graph distance in
the regime where vertices have infinite degrees. We fully characterize
transience vs. recurrence for dimension 1 and 2 and give sufficient conditions
for transience in dimension 3 and higher. Finally, we show the existence of a
hierarchical structure for parameters where vertices have degrees with infinite
variance and obtain bounds on the cluster density.Comment: Revised Definition 2.5 and an argument in Section 6, results are
unchanged. Correction of minor typos. 29 pages, 7 figure
The Iterated Local Transitivity Model for Tournaments
A key generative principle within social and other complex networks is
transitivity, where friends of friends are more likely friends. We propose a
new model for highly dense complex networks based on transitivity, called the
Iterated Local Transitivity Tournament (or ILTT) model. In ILTT and a dual
version of the model, we iteratively apply the principle of transitivity to
form new tournaments. The resulting models generate tournaments with small
average distances as observed in real-world complex networks. We explore
properties of small subtournaments or motifs in the ILTT model and study its
graph-theoretic properties, such as Hamilton cycles, spectral properties, and
domination numbers. We finish with a set of open problems and the next steps
for the ILTT model
The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches
This work deals with the so-called weighted independent domination problem, which is an -hard combinatorial optimization problem in graphs. In contrast to previous work, this paper considers the problem from a non-theoretical perspective. The first contribution consists in the development of three integer linear programming models. Second, two greedy heuristics are proposed. Finally, the last contribution is a population-based iterated greedy metaheuristic which is applied in two different ways: (1) the metaheuristic is applied directly to each problem instance, and (2) the metaheuristic is applied at each iteration of a higher-level framework---known as construct, merge, solve \& adapt---to sub-instances of the tackled problem instances. The results of the considered algorithmic approaches show that integer linear programming approaches can only compete with the developed metaheuristics in the context of graphs with up to 100 nodes. When larger graphs are concerned, the application of the populated-based iterated greedy algorithm within the higher-level framework works generally best. The experimental evaluation considers graphs of different types, sizes, densities, and ways of generating the node and edge weights
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