  Social networks, such as Twitter, form a heterogeneous information network
(HIN) where nodes represent domain entities (e.g., user, content, advertiser,
etc.) and edges represent one of many entity interactions (e.g, a user
re-sharing content or "following" another). Interactions from multiple relation
types can encode valuable information about social network entities not fully
captured by a single relation; for instance, a user's preference for accounts
to follow may depend on both user-content engagement interactions and the other
users they follow. In this work, we investigate knowledge-graph embeddings for
entities in the Twitter HIN (TwHIN); we show that these pretrained
representations yield significant offline and online improvement for a diverse
range of downstream recommendation and classification tasks: personalized ads
rankings, account follow-recommendation, offensive content detection, and
search ranking. We discuss design choices and practical challenges of deploying
industry-scale HIN embeddings, including compressing them to reduce end-to-end
model latency and handling parameter drift across versions.
