Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Most existing collaborative filtering techniques have focused on modeling the binary relation
of users to items by extracting from user ratings. Aside from users' ratings, their affiliated …

There is mounting concern that social media sites contribute to political polarization by
creating “echo chambers” that insulate people from opposing views about current events …

In this paper, we introduce a large scale multi-objective ranking system for recommending
what video to watch next on an industrial video sharing platform. The system faces many …

Analyzing large graphs provides valuable insights for social networking and web companies
in content ranking and recommendations. While numerous graph processing systems have …

We predict that people with different political orientations will exhibit systematically different
levels of political homophily, the tendency to associate with others similar to oneself in …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

Link prediction attempts to predict whether an unseen edge exists based on only a portion of
the graph. A flurry of methods has been created in recent years that attempt to make use of …

Network analysis is a powerful tool that provides us a fruitful framework to describe
phenomena related to social, technological, and many other real-world complex systems. In …

In this paper, we provide a characterization of the topological features of the Twitter follow
graph, analyzing properties such as degree distributions, connected components, shortest …