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 $v$, the centrality maximization problem consists in creating $k$ new links incident to $v$ in such a way that the centrality of $v$ 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 $1-1/e$, unless $P=NP$, and, under the stronger gap-ETH hypothesis, the problem cannot be approximated within a factor better than $1/n^{o(1)}$, where $n$ 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 $\Omega(1/\sqrt{n})$. 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

Selecting Nodes and Buying Links to Maximize the Information Diffusion in a Network

The Independent Cascade Model (ICM) is a widely studied model that aims to capture the dynamics of the information diffusion in social networks and in general complex networks. In this model, we can distinguish between active nodes which spread the information and inactive ones. The process starts from a set of initially active nodes called seeds. Recursively, currently active nodes can activate their neighbours according to a probability distribution on the set of edges. After a certain number of these recursive cycles, a large number of nodes might become active. The process terminates when no further node gets activated. Starting from the work of Domingos and Richardson [Domingos et al. 2001], several studies have been conducted with the aim of shaping a given diffusion process so as to maximize the number of activated nodes at the end of the process. One of the most studied problems has been formalized by Kempe et al. and consists in finding a set of initial seeds that maximizes the expected number of active nodes under a budget constraint [Kempe et al. 2003]. In this paper we study a generalization of the problem of Kempe et al. in which we are allowed to spend part of the budget to create new edges incident to the seeds. That is, the budget can be spent to buy seeds or edges according to a cost function. The problem does not admin a PTAS, unless P=NP. We propose two approximation algorithms: the former one gives an approximation ratio that depends on the edge costs and increases when these costs are high; the latter algorithm gives a constant approximation guarantee which is greater than that of the first algorithm when the edge costs can be small

Imaging and therapy of ovarian cancer: Clinical application of nanoparticles and future perspectives

Despite significant advances in cancer diagnostics and treatment, ovarian cancers (OC) continue to kill more than 150,000 women every year worldwide. Due to the relatively asymptomatic nature and the advanced stage of the disease at the time of diagnosis, OC is the most lethal gynecologic malignancy. The current treatment for advanced OC relies on the synergistic effect of combining surgical cytoreduction and chemotherapy; however, beside the fact that chemotherapy resistance is a major challenge in OC management, new imaging strategies are needed to target microscopic lesions and improve both cytoreductive surgery and patient outcomes. In this context, nanostructured probes are emerging as a new class of medical tool that can simultaneously provide imaging contrast, target tumor cells, and carry a wide range of medicines resulting in better diagnosis and therapeutic precision. Herein we summarize several exemplary efforts in nanomedicine for addressing unmet clinical needs

Topology versus Link Strength for Information Dissemination in Networks

International audienceInformation can flow in a network through communication links connecting the nodes. The topology of connections and the strength of the links are two factors that effect the speed of spread of information in the network. In this paper we show that the topology can have stronger effect on the information spread than the strength of the links. In particular, we consider an iterative belief propagation process as in average consensus protocols where each node in the network has a certain belief (a real number), and with every iteration each node updates its own belief with the weighted average of its belief and the ones of it is connected to. The speed of spread of beliefs in the network is governed by the speed of convergence of the average consensus protocol. We show by simulations that a topological optimization can have a significant faster convergence than any weight selection optimization techniques. We also give a 2-hop message averaging that perform faster convergence than standard algorithms. The simulations are done on different graph topologies: static graphs (Rings, Grids), random graphs (Erdos Renyi, Random Geometric), and a real world network (Enron internal email exchange network).L'information peut circuler dans un réseau de communication par les liens reliant les nœuds. La topologie du réseau et la force des liens sont deux facteurs qui influent sur la vitesse de propagation de l'information dans le réseau. Dans cet article, nous montrons que la topologie peut avoir un rôle plus important que la force des liens pour la vitesse de propagation de l'information. En particulier, nous considérons un processus itératif de propagation de croyance comme dans les protocoles de consensus moyen où chaque nœud dans le réseau a une certaine croyance (exprimée par un nombre réel), et à chaque itération il met à jour sa croyance en calculant une moyenne pondérée de sa croyance et de celles des ses voisins. Nous montrons que l'ajout de liens peut conduire à une augmentation de la vitesse de convergence du protocole de consensus plus significative que les techniques d'optimisation des poids. Les simulations sont effectuées sur différentes topologies: anneaux, grilles, graphes aléatoires (Erdos Renyi, graphes géométriques aléatoires) et le graphe d'échange de courriels chez Enron

Greedily Improving Our Own Centrality in A Network

International audienceThe closeness and the betweenness centralities are two well knownmeasures of importance of a vertex within a given complex network.Having high closeness or betweenness centrality can have positiveimpact on the vertex itself: hence, in this paper we consider the problemof determining how much a vertex can increase its centrality by creatinga limited amount of new edges incident to it. We first prove that thisproblem does not admit a polynomial-time approximation scheme (unlessP = NP), and we then propose a simple greedy approximation algorithm(with an almost tight approximation ratio), whose performance is thentested on synthetic graphs and real-world networks

Secondary Autochthonous Outbreak of Chikungunya, Southern Italy, 2017

In 2017, a chikungunya outbreak in central Italy later evolved into a secondary cluster in southern Italy, providing evidence of disease emergence in new areas. Officials have taken action to raise awareness among clinicians and the general population, increase timely case detection, reduce mosquito breeding sites, and promote mosquito bite prevention