The Diffusion of Networking Technologies

TL;DR

This paper presents a novel approximation algorithm for identifying the minimal initial set of nodes that can trigger a cascade of protocol upgrades across the Internet modeled as a graph, considering influence thresholds.

Contribution

It introduces the first provably guaranteed approximation algorithm for the cascade initiation problem in network upgrades, incorporating influence thresholds and highlighting differences from social network models.

Findings

Algorithm based on linear programming with performance guarantees

Computational hardness results established for the problem

Demonstrates the substantial difference from social network cascade models

Abstract

There has been significant interest in the networking community on the impact of cascade effects on the diffusion of networking technology upgrades in the Internet. Thinking of the global Internet as a graph, where each node represents an economically-motivated Internet Service Provider (ISP), a key problem is to determine the smallest set of nodes that can trigger a cascade that causes every other node in the graph to adopt the protocol. We design the first approximation algorithm with a provable performance guarantee for this problem, in a model that captures the following key issue: a node's decision to upgrade should be influenced by the decisions of the remote nodes it wishes to communicate with. Given an internetwork G(V,E) and threshold function \theta, we assume that node $u$ activates (upgrades to the new technology) when it is adjacent to a connected component of active nodes in G of size exceeding node $u$'s threshold \theta(u). Our objective is to choose the smallest set of nodes that can cause the rest of the graph to activate. Our main contribution is an approximation algorithm based on linear programming, which we complement with computational hardness results and a near-optimum integrality gap. Our algorithm, which does not rely on submodular optimization techniques, also highlights the substantial algorithmic difference between our problem and similar questions studied in the context of social networks.