Rami Puzis

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Network planning and traffic flow optimization require the acquisition and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements and so on. Data acquisition is an expensive process that involves household surveys and automatic as well as semiautomatic measurements performed all over the network. For example, in order to accurately estimate the effect of a certain network change on the total emissions produced by vehicles in the network, assessment of the vehicle fleet composition for each origin–destination pair is required. As a result, problems that optimize nonlocal merit functions become highly difficult to solve. One such problem is finding the optimal deployment of traffic monitoring units. In this article we suggest a new traffic assignment model that is based on the concept of shortest path betweenness centrality measure, borrowed from the domain of complex network analysis. We show how betweenness can be augmented in order to solve the traffic assignment problem given an arbitrary travel cost definition. The proposed traffic assignment model is evaluated using a high-resolution Israeli transportation data set derived from the analysis of cellular phones data. The group variant of the augmented betweenness centrality is then used to optimize the locations of traffic monitoring units, hence reducing the cost and increasing the effectiveness of traffic monitoring.

Heuristics for Speeding Up Betweenness Centrality Computation

We propose and evaluate two complementary heuristics to speed up exact computation of the shortest-path between ness centrality. Both heuristics are relatively simple adaptations of the standard algorithm for between ness centrality. Consequently, they generalize the computation of edge between ness and most other variants, and can be used to further speed up between ness estimation algorithms, as well. In the first heuristic, structurally equivalent vertices are contracted based on the observation that they have the same centrality and also contribute equally to the centrality of others. In the second heuristic, we first apply a linear-time between ness algorithm on the block-cut point tree and then compute the remaining contributions separately in each biconnected component. Experiments on a variety of large graphs illustrate the efficiency and complementarity of our heuristics.

Link Prediction in Highly Fractional Data Sets

In recent years, online social networks have grown in scale and variability and offer individuals with similar interests the possibility of exchanging ideas and networking. On the one hand, social networks create new opportunities to develop friendships, share ideas, and conduct business. On the other hand, they are also an effective media tool for plotting crime and organizing extremists groups around the world. Online social networks, such as Facebook, Google+, and Twitter are hard to track due to their massive scale and increased awareness of privacy. Criminals and terrorists strive to hide their relationships, especially those that can associate them with a executed terror act.

Betweenness computation in the single graph representation of hypergraphs

Many real-world social networks are hypergraphs because they either explicitly support membership in groups or implicitly include communities. We present the HyperBC algorithm that exactly computes betweenness centrality (or BC) in hypergraphs. The forward phase of HyperBC and the backpropagation phase are specifically tailored for BC computation on hypergraphs. In addition, we present an efficient method for pruning networks through the notion of “non-bridging” vertices. We experimentally evaluate our algorithm on a variety of real and artificial networks and show that it significantly speeds up the computation of BC on both real and artificial hypergraphs, while at the same time, being very memory efficient.