5 research outputs found
Coverage Centrality Maximization in Undirected Networks
Centrality metrics are among the main tools in social network analysis. Being
central for a user of a network leads to several benefits to the user: central
users are highly influential and play key roles within the network. Therefore,
the optimization problem of increasing the centrality of a network user
recently received considerable attention. Given a network and a target user
, the centrality maximization problem consists in creating new links
incident to in such a way that the centrality of is maximized,
according to some centrality metric. Most of the algorithms proposed in the
literature are based on showing that a given centrality metric is monotone and
submodular with respect to link addition. However, this property does not hold
for several shortest-path based centrality metrics if the links are undirected.
In this paper we study the centrality maximization problem in undirected
networks for one of the most important shortest-path based centrality measures,
the coverage centrality. We provide several hardness and approximation results.
We first show that the problem cannot be approximated within a factor greater
than , unless , and, under the stronger gap-ETH hypothesis, the
problem cannot be approximated within a factor better than , where
is the number of users. We then propose two greedy approximation
algorithms, and show that, by suitably combining them, we can guarantee an
approximation factor of . We experimentally compare the
solutions provided by our approximation algorithm with optimal solutions
computed by means of an exact IP formulation. We show that our algorithm
produces solutions that are very close to the optimum.Comment: Accepted to AAAI 201
Brand Network Booster: A New System for Improving Brand Connectivity
This paper presents a new decision support system offered for an in-depth
analysis of semantic networks, which can provide insights for a better
exploration of a brand's image and the improvement of its connectivity. In
terms of network analysis, we show that this goal is achieved by solving an
extended version of the Maximum Betweenness Improvement problem, which includes
the possibility of considering adversarial nodes, constrained budgets, and
weighted networks - where connectivity improvement can be obtained by adding
links or increasing the weight of existing connections. We present this new
system together with two case studies, also discussing its performance. Our
tool and approach are useful both for network scholars and for supporting the
strategic decision-making processes of marketing and communication managers