Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …

Networks are finite metric spaces, with distances defined by the shortest paths between
nodes. However, this is not the only form of network geometry: two others are the geometry …

In general, graph representation learning methods assume that the train and test data come
from the same distribution. In this work we consider an underexplored area of an otherwise …

We consider a simple and overarching representation for permutation-invariant functions of
sequences (or multiset functions). Our approach, which we call Janossy pooling, expresses …

Treating neural network inputs and outputs as random variables, we characterize the
structure of neural networks that can be used to model data that are invariant or equivariant …

We use deep learning to model interactions across two or more sets of objects, such as user
{–} movie ratings or protein {–} drug bindings. The canonical representation of such …

Community detection, the decomposition of a graph into essential building blocks, has been
a core research topic in network science over the past years. Since a precise notion of what …

Statistical network modelling has focused on representing the graph as a discrete structure,
namely the adjacency matrix. When assuming exchangeability of this array—which can aid …

Social network data are often prohibitively expensive to collect, limiting empirical network
research. We propose an inexpensive and feasible strategy for network elicitation using …

Visual knowledge bases such as Visual Genome power numerous applications in computer
vision, including visual question answering and captioning, but suffer from sparse …