  Graphs are a key form of Big Data, and performing scalable analytics over
them is invaluable to many domains. As our ability to collect data grows, there
is an emerging class of inter-connected data which accumulates or varies over
time, and on which novel analytics - both over the network structure and across
the time-variant attribute values - is necessary. We introduce the notion of
time-series graph analytics and propose Gopher, a scalable programming
abstraction to develop algorithms and analytics on such datasets. Our
abstraction leverages a sub-graph centric programming model and extends it to
the temporal dimension using an iterative BSP (Bulk Synchronous Parallel)
approach. Gopher is co-designed with GoFS, a distributed storage specialized
for time-series graphs, as part of the GoFFish distributed analytics platform.
We examine storage optimizations for GoFS, design patterns in Gopher to
leverage the distributed data layout, and evaluate the GoFFish platform using
time-series graph data and applications on a commodity cluster.
