A challenge in social network analysis, is understanding the position, or stance, of people on a large set of topics. While past work has modeled (dis)agreement in social networks using signed graphs, these approaches have not modeled agreement patterns across a range of correlated topics. For instance, disagreement on one topic may make disagreement (or agreement) more likely for related topics. Recognizing topics influence agreement and disagreement, we propose the Stance Embeddings Model (SEM), which jointly learns embeddings for each user and topic in signed social graphs with distinct edge types for each topic. By jointly learning user and topic embeddings, SEM can perform cold-start topic stance detection, predicting the stance of a user on topics for which we have not observed their engagement. We demonstrate the effectiveness of SEM using two large-scale Twitter signed graph datasets that we open-source. One dataset, TwitterSG, labels (dis)agreements using engagements between users via tweets to derive topic-informed, signed edges. The other, BirdwatchSG, leverages community reports on misinformation and misleading content. On TwitterSG and BirdwatchSG, SEM shows a 39% and 26% error reduction respectively against strong topic-agnostic baselines.

Recommender systems are fundamental building blocks of modern consumer web applications that seek to predict user preferences to better serve relevant items. As such, high-quality user and item representations as inputs to recommender systems are crucial for personalized recommendation. To construct these user and item representations, self-supervised graph embedding has emerged as a principled approach to embed relational data such as user social graphs, user membership graphs, user-item engagements, and other heterogeneous graphs. In this tutorial we discuss different families of approaches to self-supervised graph embedding. Within each family, we outline a variety of techniques, their merits and disadvantages, and expound on latest works. Finally, we demonstrate how to effectively utilize the resultant large embedding tables to improve candidate retrieval and ranking in modern industry-scale deep-learning recommender systems.

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.

Entity matching -- the task of clustering duplicated database records to underlying entities -- has become an increasingly critical component in modern data integration management. Amperity provides a platform for businesses to manage customer data that utilizes a machine-learning approach to entity matching, resolving billions of customer records on a daily basis. We face several challenges in deploying entity matching to industrial applications at scale, and they are less prominent in the literature. These challenges include: (1) Providing not just a single entity clustering, but supporting clusterings at multiple confidence levels to enable downstream applications with varying precision/recall trade-off needs. (2) Many customer record attributes may be systematically missing from different sources of data, creating many pairs of records in a cluster that appear to not match due to incomplete, rather than conflicting information. Allowing these records to connect transitively without introducing conflicts is invaluable to businesses because they can acquire a more comprehensive profile of their customers without incorrect entity merges. (3) How to cluster records over time and assign persistent cluster IDs that can be used for downstream use cases such as A/B tests or predictive model training; this is made more challenging by the fact that we receive new customer data every day and clusters naturally evolving over time still require persistent IDs that refer to the same entity. In this work, we describe Amperity's entity matching framework, Fusion, and how its design provides solutions to these challenges. In particular, we describe our pairwise matching model based on ordinal regression that permits a well-defined way to produce entity clusterings at different confidence levels, a novel clustering algorithm that separates conflicting record pairs in clusters while allowing for pairs that may appear dissimilar due to missing data, and a persistent ID generation algorithm which balances stability of the identifier with ever-evolving entities.