Discovering Evolution Chains in Dynamic Networks

Most of the works on learning from networked data assume that the network is static. In this paper we consider a different scenario, where the network is dynamic, i.e. nodes/relationships can be added or removed and relationships can change in their type over time. We assume that the “core” of the network is more stable than the “marginal” part of the network, nevertheless it can change with time. These changes are of interest for this work, since they reflect a crucial step in the network evolution. Indeed, we tackle the problem of discovering evolution chains, which express the temporal evolution of the “core” of the network. To describe the “core” of the network, we follow a frequent pattern-mining approach, with the critical difference that the frequency of a pattern is computed along a time-period and not on a static dataset. The proposed method proceeds in two steps: 1) identification of changes through the discovery of emerging patterns; 2) composition of evolution chains by joining emerging patterns. We test the effectiveness of the method on both real and synthetic data.