ATMoN: Adapting the “Temporality” in Large-Scale Dynamic Networks

Abstract

With the widespread adoption of dynamic networks modeled as temporal graphs to study fast evolving interactions, attention is needed to provide graph metrics in time and at scale. In this paper, we introduce ATMoN, an open-source library developed to computationally offload graph processing engines and ease the communication overhead in monitored networks over an unprecedented wealth of data. This is achieved, by efficiently adapting, in place and inexpensively, the temporal granularity at which graph metrics are computed based on runtime knowledge captured by a low-cost probabilistic learning model capable of approximating both the metric stream evolution and the runtime volatility of the graph topology structure. After a thorough evaluation study with real-world data from mobile, face-to-face and vehicular networks, results show that ATMoN is able to reduce the computation overhead by at least 76%, data volume by 60%, overall cloud costs by at least 54%, while always maintaining accuracy above 88%.