  Pairwise temporal interactions between entities can be represented as
temporal networks, which code the propagation of processes such as epidemic
spreading or information cascades, evolving on top of them. The largest outcome
of these processes is directly linked to the structure of the underlying
network. Indeed, a node of a network at given time cannot affect more nodes in
the future than it can reach via time-respecting paths. This set of nodes
reachable from a source defines an out-component, which identification is
costly. In this paper, we propose an efficient matrix algorithm to tackle this
issue and show that it outperforms other state-of-the-art methods. Secondly, we
propose a hashing framework to coarsen large temporal networks into smaller
proxies on which out-components are easier to estimate, and then recombined to
obtain the initial components. Our graph hashing solution has implications in
privacy respecting representation of temporal networks.
