Simplifying Complex Networks for Practitioners 

About: Simplifying Complex Networks for Practitioners is an academic conference. The conference publishes majorly in the area(s): Network science & Complex network. Over the lifetime, 9 publications have been published by the conference receiving 103 citations.

Distributed assessment of the closeness centrality ranking in complex networks

Abstract: We propose a method for the Distributed Assessment of the Closeness CEntrality Ranking (DACCER) in complex networks. DACCER computes centrality based only on localized information restricted to a given neighborhood around each node, thus not requiring full knowledge of the network topology. We show that the node centrality ranking computed by DACCER is highly correlated with the node ranking based on the traditional closeness centrality, which requires high computational costs and full knowledge of the network topology. This outcome is quite useful given the vast potential applicability of closeness centrality, which is seldom applied to large-scale networks due to its high computational costs. Results indicate that DACCER is simple, yet efficient, in assessing node centrality while allowing a distributed implementation that contributes to its performance. This also contributes to the practical applicability of DACCER in the analysis of large-scale complex networks, as we show using in our experimental evaluation both synthetically generated networks and traces of real-world networks of different kinds and scales.

Topological trends of internet content providers

Abstract: The Internet is constantly changing, and its hierarchy was recently shown to become flatter. Recent studies of inter-domain traffic showed that large content providers drive this change by bypassing tier-1 networks and reaching closer to their users, enabling them to save transit costs and reduce reliance of transit networks as new services are being deployed, and traffic shaping is becoming increasingly popular.In this paper we take a first look at the evolving connectivity of large content provider networks, from a topological point of view of the autonomous systems (AS) graph. We perform a 5-year longitudinal study of the topological trends of large content providers, by analyzing several large content providers and comparing these trends to those observed for large tier-1 networks. We study trends in the connectivity of the networks, neighbor diversity and geographical spread, their hierarchy, the adoption of IXPs as a convenient method for peering, and their centrality. Our observations indicate that content providers gradually increase and diversify their connectivity, enabling them to improve their centrality in the Internet, while tier-1 networks lose dominance over time.

Publish-subscribe systems via gossip: a study based on complex networks

Abstract: This paper analyzes the adoption of unstructured P2P overlay networks to build publish-subscribe systems. We consider a very simple distributed communication protocol, based on gossip and on the local knowledge each node has about subscriptions made by its neighbours. A mathematical analysis is provided to estimate the number of nodes receiving the event. These outcomes are compared to those obtained via simulation. Results show even when the amount of subscribers represents a very small (yet non-negligible) portion of network nodes, by tuning the gossip probability the event can percolate through the overlay.

Predicting human contacts in mobile social networks using supervised learning

Abstract: Having access to human contact traces has allowed researchers to study and understand how people contact each other in different social settings. However, most of the existing human contact traces are limited in the number of deployed Bluetooth sensors. In most experiments, there are two types of participants, the ordinary ones who carry cellphones and a specially selected group who additionally carry sensors. Although the contacts between any pair of participants are known when at least one of them carry a sensor, the contacts between any pair of participants are "hidden" when both of them carry their cellphones. In this paper, we employ two well-known supervised classifiers for predicting hidden contacts among participants who carry their cellphones. The performance results of our supervised classifiers show the applicability of using machine learning algorithms for contact prediction task. The results also show that a small subset of features such as number of common neighbors and total overlap time play essential roles in forming human contacts. Finally, we show that contacts of nodes with high centralities are more predictable than nodes with low centralities.

The dynamic network notation: harnessing network effects in PaaS-ecosystems

Abstract: Web applications complement the Platform-as-a-Service (PaaS) value by satisfying widespread and rapidly changing consumer requirements within limited time and budget. Successful PaaS providers excel in governing their market performance by leveraging complex network effects, which implicitly control PaaS-ecosystems. There is currently no methodically sound and easy to use tool available to business analysts and software engineers of PaaS-offerings that addresses challenges and opportunities in launching and governing such highly dynamic networks. In this paper, we capture network behavior through elements of complex system and control theory. Our dynamic network notation (DYNO) builds upon these theories. In more detail, DYNO models PaaS offerings with a focus on identifying and shaping network effects towards a sufficient user-base and an optimized portfolio of Web applications, all while maintaining a high quality of service.