The performance of Italian airports

The aim of this work is the analysis of the operational efficiencies of the major Italian airports. The study is based on a cross-sectional, time series dataset of 14 Italian airports for the period 2000-2004. In the sample there are the two international airport systems of Rome and Milan, each composed by two airports, and ten regional airports. The analysis of the industry characteristics have pointed out that both structural and institutional factors cause an high degrees of dissimilarities among the 14 Italians airports. In this framework comparing the efficiency could be troublesome and misleading at the same time. Thus the methodological goal of the paper has been the application of two multivariate techniques, factorial and cluster analysis, in order to reduces dissimilarities and improve the results of the efficiency measures. The two multivariate techniques help in determining variables which mostly affect the airports operational efficiencies. The operational efficiencies have been estimated using non parametric models. In particular, the modified Torqvinst index has been employed to measure both Total Factor Productivity (TFP) and Variable Factor Productivity (VFP) indexes. Moreover, in order to remove the effects of the variables beyond the managerial control the residual Total Factor Productivity (RTFP) and Variable Factor Productivity (RVFP) indexes have been computed.Factorial analysis; Cluster analysis; Torqvinst index; Productivity

Computing Top-k Closeness Centrality Faster in Unweighted Graphs. (Technical Report)

Centrality indices are widely used analytic measures for the importance of nodes in a network. Closeness centrality is very popular among these measures. For a single node v, it takes the sum of the distances of v to all other nodes into account. The currently best algorithms in practical applications for computing the closeness for all nodes exactly in unweighted graphs are based on breadth-first search (BFS) from every node. Thus, even for sparse graphs, these algorithms require quadratic running time in the worst case, which is prohibitive for large networks. In many relevant applications, however, it is unnecessary to compute closeness values for all nodes. Instead, one requires only the k nodes with the highest closeness values in descending order. Thus, we present a new algorithm for computing this top-k ranking in unweighted graphs. Following the rationale of previous work, our algorithm significantly reduces the number of traversed edges. It does so by computing upper bounds on the closeness and stopping the current BFS search when k nodes already have higher closeness than the bounds computed for the other nodes. In our experiments with real-world and synthetic instances of various types, one of these new bounds is good for small-world graphs with low diameter (such as social networks), while the other one excels for graphs with high diameter (such as road networks). Combining them yields an algorithm that is faster than the state of the art for top-k computations for all test instances, by a wide margin for high-diameter graphs

Scaling up Group Closeness Maximization

Closeness is a widely-used centrality measure in social network analysis. For a node it indicates the inverse average shortest-path distance to the other nodes of the network. While the identification of the k nodes with highest closeness received significant attention, many applications are actually interested in finding a group of nodes that is central as a whole. For this problem, only recently a greedy algorithm with approximation ratio (1−1/e) has been proposed [Chen et al., ADC 2016]. Since this algorithm’s running time is still expensive for large networks, a heuristic without approximation guarantee has also been proposed in the same paper. In the present paper we develop new techniques to speed up the greedy algorithm without losing its theoretical guarantee. Compared to a straightforward implementation, our approach is orders of magnitude faster and, compared to the heuristic proposed by Chen et al., we always find a solution with better quality in a comparable running time in our experiments. Our method Greedy++ allows us to approximate the group with maximum closeness on networks with up to hundreds of millions of edges in minutes or at most a few hours. To have the same theoretical guarantee, the greedy approach by [Chen et al., ADC 2016] would take several days already on networks with hundreds of thousands of edges. In a comparison with the optimum, our experiments show that the solution found by Greedy++ is actually much better than the theoretical guarantee. Over all tested networks, the empirical approximation ratio is never lower than 0.97. Finally, we study for the first time the correlation between the top-k nodes with highest closeness and an approximation of the most central group in large complex networks and show that the overlap between the two is relatively small

Faster Betweenness Centrality Updates in Evolving Networks

Finding central nodes is a fundamental problem in network analysis. Betweenness centrality is a well-known measure which quantifies the importance of a node based on the fraction of shortest paths going though it. Due to the dynamic nature of many today's networks, algorithms that quickly update centrality scores have become a necessity. For betweenness, several dynamic algorithms have been proposed over the years, targeting different update types (incremental- and decremental-only, fully-dynamic). In this paper we introduce a new dynamic algorithm for updating betweenness centrality after an edge insertion or an edge weight decrease. Our method is a combination of two independent contributions: a faster algorithm for updating pairwise distances as well as number of shortest paths, and a faster algorithm for updating dependencies. Whereas the worst-case running time of our algorithm is the same as recomputation, our techniques considerably reduce the number of operations performed by existing dynamic betweenness algorithms.Comment: Accepted at the 16th International Symposium on Experimental Algorithms (SEA 2017

The performance of Italian airports

The aim of this work is the analysis of the operational efficiencies of the major Italian airports. The study is based on a cross-sectional, time series dataset of 14 Italian airports for the period 2000-2004. In the sample there are the two international airport systems of Rome and Milan, each composed by two airports, and ten regional airports. The analysis of the industry characteristics have pointed out that both structural and institutional factors cause an high degrees of dissimilarities among the 14 Italians airports. In this framework comparing the efficiency could be troublesome and misleading at the same time. Thus the methodological goal of the paper has been the application of two multivariate techniques, factorial and cluster analysis, in order to reduces dissimilarities and improve the results of the efficiency measures. The two multivariate techniques help in determining variables which mostly affect the airports operational efficiencies. The operational efficiencies have been estimated using non parametric models. In particular, the modified Torqvinst index has been employed to measure both Total Factor Productivity (TFP) and Variable Factor Productivity (VFP) indexes. Moreover, in order to remove the effects of the variables beyond the managerial control the residual Total Factor Productivity (RTFP) and Variable Factor Productivity (RVFP) indexes have been computed