Routing in NDN networks must scale in terms of forwarding table size and routing protocol overhead. Hyperbolic routing (HR) presents a potential solution to address the routing scalability problem, because it does not use traditional forwarding tables or exchange routing updates upon changes in network topologies. Although HR has the drawbacks of producing sub-optimal routes or local minima for some destinations, these issues can be mitigated by NDN's intelligent data forwarding plane. However, HR's viability still depends on both the quality of the routes HR provides and the overhead incurred at the forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's sub-optimal path selection. This paper describes our experimental investigation into the packet delivery delay and overhead under HR as compared with Named-Data Link State Routing (NLSR), which calculates shortest paths. We run emulation experiments using various topologies with different failure scenarios, probing intervals, and maximum number of next hops for a name prefix. Our results show that HR's delay stretch has a median close to 1 and a 95th-percentile around or below 2, which does not grow with the network size. HR's message overhead in dynamic topologies is nearly independent of the network size, while NLSR's overhead grows polynomially at least. These results suggest that HR offers a more scalable routing solution with little impact on the optimality of routing paths.

SUMMARY: Detecting communities and densely connected groups may contribute to unravel the underlying relationships among the units present in diverse biological networks (e.g. interactomes, coexpression networks, ecological networks). We recently showed that communities can be precisely characterized by maximizing Surprise, a global network parameter. Here, we present SurpriseMe, a tool that integrates the outputs of seven of the best algorithms available to estimate the maximum Surprise value. SurpriseMe also generates distance matrices that allow visualizing the relationships among the solutions generated by the algorithms. We show that the communities present in small- and medium-sized networks, with up to 10 000 nodes, can be easily characterized: on standard PC computers, these analyses take less than an hour. Also, four of the algorithms may rapidly analyze networks with up to 100 000 nodes, given enough memory resources. Because of its performance and simplicity, SurpriseMe is a reference tool for community structure characterization. AVAILABILITY AND IMPLEMENTATION: SurpriseMe is implemented in Perl and C/C++. It compiles and runs on any UNIX-based operating system, including Linux and Mac OS/X, using standard libraries. The source code is freely and publicly available under the GPL 3.0 license at http://github.com/raldecoa/SurpriseMe/releases.