Leveraging Node Attributes for Incomplete Relational Data

Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.Comment: Appearing in ICML 201

JNET: Learning User Representations via Joint Network Embedding and Topic Embedding

User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow

Relational learning and fairness

This thesis will focus on relational learning in the modeling of text and user roles in networks, and the relative treatment of individuals as related to algorithmic fairness. With the exponential growth in social network data, the need for models of user interaction data is growing. This work presents a model which agglomerates users into archetypes based on topical modeling of the contents of their interactions. It further proposes models and a fairness metric for the creation of classifiers for individuals which control for the relative treatment of individualsStatistic