  Spam is a serious problem plaguing web-scale digital platforms which
facilitate user content creation and distribution. It compromises platform's
integrity, performance of services like recommendation and search, and overall
business. Spammers engage in a variety of abusive and evasive behavior which
are distinct from non-spammers. Users' complex behavior can be well represented
by a heterogeneous graph rich with node and edge attributes. Learning to
identify spammers in such a graph for a web-scale platform is challenging
because of its structural complexity and size. In this paper, we propose SEINE
(Spam DEtection using Interaction NEtworks), a spam detection model over a
novel graph framework. Our graph simultaneously captures rich users' details
and behavior and enables learning on a billion-scale graph. Our model considers
neighborhood along with edge types and attributes, allowing it to capture a
wide range of spammers. SEINE, trained on a real dataset of tens of millions of
nodes and billions of edges, achieves a high performance of 80% recall with 1%
false positive rate. SEINE achieves comparable performance to the
state-of-the-art techniques on a public dataset while being pragmatic to be
used in a large-scale production system.
