  Since its inception, Facebook has become an integral part of the online
social community. People rely on Facebook to make connections with others and
build communities. As a result, it is paramount to protect the integrity of
such a rapidly growing network in a fast and scalable manner. In this paper, we
present our efforts to protect various social media entities at Facebook from
people who try to abuse our platform. We present a novel Temporal Interaction
EmbeddingS (TIES) model that is designed to capture rogue social interactions
and flag them for further suitable actions. TIES is a supervised, deep
learning, production ready model at Facebook-scale networks. Prior works on
integrity problems are mostly focused on capturing either only static or
certain dynamic features of social entities. In contrast, TIES can capture both
these variant behaviors in a unified model owing to the recent strides made in
the domains of graph embedding and deep sequential pattern learning. To show
the real-world impact of TIES, we present a few applications especially for
preventing spread of misinformation, fake account detection, and reducing ads
payment risks in order to enhance the platform's integrity.
