Geographic Patterns in I2P Peer Selection: An Empirical Network Topology Analysis

TL;DR

This study analyzes I2P's peer selection to determine if geographic location influences routing topology, finding a highly heterogeneous and geographically random network that balances performance and anonymity.

Contribution

It provides the first empirical analysis showing that I2P's peer selection results in random geographic mixing, supporting its design for anonymity.

Findings

No significant geographic homophily detected

Same-country connections are near random expectation

Community detection reveals high modularity with moderate geographic alignment

Abstract

The Invisible Internet Project (I2P) routes data via encrypted, decentralized tunnels. Peer selection can significantly affect security and performance. This empirical study examines whether geographic location systematically influences I2P's routing topology. Consistent with I2P's design principles, which include avoiding multiple peers from the same /16 IP subnet to maximize anonymity, we conducted assortativity analysis, community detection, and permutation testing on data from 327 routers and 254 connections (SWARM-I2P). We found a network-level absence of significant geographic homophily. The assortativity coefficient was r = 0.017 (p = 0.222). Same-country connections (11.1%) are statistically near random expectation (10.91%). Community detection found 110 highly modular groups (Q = 0.972) only moderately aligned geographically (NMI = 0.521). We conclude that aggregate peer selection in I2P leads to a highly heterogeneous, random geographical mixing, providing a foundation for understanding the performance-anonymity tradeoff.