Autonomous Systems (AS) exist and co-exist in two parallel dimensions. In one dimension they are physical networks, whose interconnections are necessary to ensure global Internet reachabilty. In the other dimension, ASes are large well-known companies competing in the same industry. In this paper we bridge together these dimensions by investigating synchronous cross correlations of stock market data and AS-level topological properties. We find that geographically close companies offering similar services are driven by common economic factors. We also provide evidence on the existence and nature of factors governing AS global as well as local topological properties.

The DNS is one of the core protocols on which the Internet is built upon. Hidden behind higher-level protocols such as email and web, it carries valuable information that can be exploited for understanding trends and preferences of the Internet community. In this paper we propose novel methodologies for modelling DNS traffic that allow Internet domains, DNS resolvers and their interactions to be represented effectively by means of graphs. DNS traffic collected at “.it” ccTLD DNS domain servers has been used to validate this work on a large scale. We found highly-skewed, fat-tailed domain and resolver degree frequencies, obeying power laws at least in their tails. These findings shed light on the the scale-free nature of the DNS ecosystem, where a few domains and a few resolvers are responsible for most of the DNS activity.

The analysis of real-world complex networks has been the focus of recent research. Detecting communities helps in uncovering their structural and functional organization. Valuable insight can be obtained by analyzing the dense, overlapping, and highly interwoven k-clique communities. However, their detection is challenging due to extensive memory requirements and execution time. In this paper, we present a novel, parallel k-clique community detection method, based on an innovative technique which enables connected components of a network to be obtained from those of its subnetworks. The novel method has an unbounded, user-configurable, and input-independent maximum degree of parallelism, and hence is able to make full use of computational resources. Theoretical tight upper bounds on its worst case time and space complexities are given as well. Experiments on real-world networks such as the Internet and the World Wide Web confirmed the almost optimal use of parallelism (i.e., a linear speedup). Comparisons with other state-of-the-art k-clique community detection methods show dramatic reductions in execution time and memory footprint. An open-source implementation of the method is also made publicly available.