The role of geography and traffic in the structure of complex networks

We report a study of the correlations among topological, weighted and spatial properties of large infrastructure networks. We review the empirical results obtained for the air transportation infrastructure that motivates a network modeling approach which integrates the various attributes of this network. In particular we describe a class of models which include a weight-topology coupling and the introduction of geographical attributes during the network evolution. The inclusion of spatial features is able to capture the appearance of non-trivial correlations between the traffic flows, the connectivity pattern and the actual distances of vertices. The anomalous fluctuations in the betweenness-degree correlation function observed in empirical studies are also recovered in the model. The presented results suggest that the interplay between topology, weights and geographical constraints is a key ingredient in order to understand the structure and evolution of many real-world networks

Why Do Cascade Sizes Follow a Power-Law?

We introduce random directed acyclic graph and use it to model the information diffusion network. Subsequently, we analyze the cascade generation model (CGM) introduced by Leskovec et al. [19]. Until now only empirical studies of this model were done. In this paper, we present the first theoretical proof that the sizes of cascades generated by the CGM follow the power-law distribution, which is consistent with multiple empirical analysis of the large social networks. We compared the assumptions of our model with the Twitter social network and tested the goodness of approximation.Comment: 8 pages, 7 figures, accepted to WWW 201

Resistance distance, information centrality, node vulnerability and vibrations in complex networks

We discuss three seemingly unrelated quantities that have been introduced in different fields of science for complex networks. The three quantities are the resistance distance, the information centrality and the node displacement. We first prove various relations among them. Then we focus on the node displacement, showing its usefulness as an index of node vulnerability.We argue that the node displacement has a better resolution as a measure of node vulnerability than the degree and the information centrality

Information and Dynamics in Urban Traffic Networks

The study of complex systems has intensified in recent years. Researchers from many different disciplines have realised that the study of systems possessing a large number of degrees of freedom interacting in a non-linear way can offer insights into problems in engineering, biology, economics and many other fields besides. Among the themes in complexity, we focus here the issues of congestion and congestion emergence in the context of urban networks, with particular reference to the effects of dissemination of information about the system’s status. This topic is of great relevance today, due to the increasing availability of real-time information about traffic conditions and the large diffusion of personal devices that allow travellers to access such information. Through the analysis of a few simple models of information propagation in urban environment, we uncover that, contrarily to the naïve expectation, complete information is often detrimental to the global performance of the urban traffic network. Indeed, global or long-range dissemination induces correlations in the systems that become a source for spatial disorder, making the system more prone to the emergence of congested states and pushing it away from its Wardrop equilibrium. The models we study range from simple flow models on network to complete agent-based simulations on real-world networks with interacting agents and dynamical information. We then analyse real data, coming from London’s network of traffic detectors. We confirm that the heterogeneity in the distribution of traffic flow and occupancies across the network reduces its performances, consistently with the results obtained for the information propagation models. In addition, we find a rich phenomenology strikingly similar to the one found in critical self-organised systems. Indeed, we measure power-law correlation functions and 1/f power spectra, hinting to long spatial and temporal effects in the traffic flow, and confirm this result through the community detection analysis of the detectors’ correlation network, which showing that the whole urban area behaves as a single large chunk. We conclude discussing the origin of these features and how they can be used to improve the network performances

THE ROLE OF GEOGRAPHY AND TRAFFIC IN THE STRUCTURE OF COMPLEX NETWORKS

We report a study of the correlations among topological, weighted and spatial properties of large infrastructure networks. We review the empirical results obtained for the air-transportation infrastructure that motivates a network modeling approach which integrates the various attributes of this network. In particular, we describe a class of models which include a weight-topology coupling and the introduction of geographical attributes during the network evolution. The inclusion of spatial features is able to capture the appearance of non-trivial correlations between the traffic flows, the connectivity pattern and the actual distances of vertices. The anomalous fluctuations in the betweenness-degree correlation function observed in empirical studies are also recovered in the model. The presented results suggest that the interplay between topology, weights and geographical constraints is a key ingredient in order to understand the structure and evolution of many real-world networks.Networks, traffic, transportation systems