Kairos: Enabling Prompt Monitoring of Information Diffusion Over Temporal Networks

Analyses of temporal graphs provide valuable insights into temporal data through the use of two analytical approaches: temporal evolution and temporal information diffusion. The former shows how a network evolves over time; the latter explains how information spreads throughout a network over time. Systems have been mainly proposed to efficiently handle graph snapshots, which are suitable for temporal evolution but inappropriate for temporal information diffusion. For analyses of temporal information diffusion, temporal graph traversal platforms have recently been proposed; however, it is still infeasible to handle infinitely evolving temporal data, especially for monitoring applications. In this paper, we propose an incremental approach and its graph processing engine, Kairos, to enable prompt monitoring of temporal information diffusion. This approach makes it possible to immediately process diffusion results for sources of interest by traversing a part of the whole network, which avoids full traversals influenced by a small change in the network, thus making monitoring applications feasible. The recipes for implementing incremental versions of existing temporal graph traversal algorithms and metrics will make it easier for users to build their ad-hoc programs.