  We propose a decentralised "local2global" approach to graph representation
learning, that one can a-priori use to scale any embedding technique. Our
local2global approach proceeds by first dividing the input graph into
overlapping subgraphs (or "patches") and training local representations for
each patch independently. In a second step, we combine the local
representations into a globally consistent representation by estimating the set
of rigid motions that best align the local representations using information
from the patch overlaps, via group synchronization. A key distinguishing
feature of local2global relative to existing work is that patches are trained
independently without the need for the often costly parameter synchronisation
during distributed training. This allows local2global to scale to large-scale
industrial applications, where the input graph may not even fit into memory and
may be stored in a distributed manner. Preliminary results on medium-scale data
sets (up to $\sim$7K nodes and $\sim$200K edges) are promising, with a graph
reconstruction performance for local2global that is comparable to that of
globally trained embeddings. A thorough evaluation of local2global on large
scale data and applications to downstream tasks, such as node classification
and link prediction, constitutes ongoing work.
