No abstract

Over the past two decades, a desire to reduce transit cost, improve control over routing and performance, and enhance the quality of experience for users, has yielded a more densely connected, flat network with fewer hops between sources and destinations. The shortening of paths in terms of the number of hops or links has also meant, for what is at the end an infrastructure-bound network, the lengthening of many of these links. In this paper, we focus on an important aspect of the evolving logical connectivity of the Internet that has received little attention to date: intercontinental long-haul links. We develop a methodology and associated processing system for identifying long haul links in traceroute measurements. We apply this system to a large corpus of traceroute data and report on multiple aspects of long haul connectivity including country-level prevalence, routers as international gateways, preferred long-haul destinations, and the evolution of these characteristics over a 7 year period. We identify over 9K layer 3 links that satisfy our definition for intercontinental long haul with many of them terminating in a relatively small number of nodes. An analysis of connected components shows a clearly dominant one with a relative size that remains stable despite a significant growth of the long-haul infrastructure.

We present a network software suite that can model contagions or opinion manipulation in social networks, that combines features from the standard packages and extends them to allow complex, interacting, dynamic topologies, and dynamic heterogeneous agent types, with individual interaction policies. The framework allows for the easy implementation of new agent types, and provides flexible visualization tools to elucidate network behavior over time.

We present a novel software suite for social network modeling and opinion diffusion processes. Much research on social network science has assumed networks with static topologies. More recently, attention has been turned to networks that evolve. Although software for modeling both the topological evolution of networks and diffusion processes are constantly improving, very little attention has been paid to agent modeling. Our software is designed to be robust, modular, and extensible, providing the ability to model dynamic social network topologies and multidimensional diffusion processes, different styles of agent including non-homophilic paradigms, as well as a testing environment for multi-agent reinforcement learning (MARL) experiments with diverse sets of agent types. We also illustrate the value of diverse agent modeling, and environments that allow for strategic unfriending. Our work shows that polarization and consensus dynamics, as well as topological clustering effects, may rely more than previously known on individuals' goals for the composition of their neighborhood's opinions.1 This software allows for arbitrary attributes to be associated with individual nodes (e.g., gender, height, opinion, etc.) Users may also program their own network change mechanism, by which agents update their attribute values based on a user-defined rule. However, there is no built-in functionality to, e.g., embed nodes in an opinion space.