The traditional Dialogue State Tracking (DST) problem aims to track user...
Email platforms need to generate personalized rankings of emails that sa...
Motifs, which have been established as building blocks for network struc...
Node representation learning in a network is an important machine learni...
We investigate graph representation learning approaches that enable mode...
Federated graph representation learning (FedGRL) brings the benefits of
...
Most work in graph-based recommender systems considers a static setting
...
Graph neural networks (GNNs) have achieved tremendous success on multipl...
Self-supervised learning of graph neural networks (GNN) is in great need...
Analyzing the readability of articles has been an important sociolinguis...
The problem of knowledge graph (KG) reasoning has been widely explored b...
Graph Neural Networks (GNNs) have recently been used for node and graph
...
Social Reinforcement Learning methods, which model agents in large netwo...
The goal of lifetime clustering is to develop an inductive model that ma...
Clustering and community detection with multiple graphs have typically
f...
In this work, we consider hypothesis testing and anomaly detection on
da...
Research in statistical relational learning has produced a number of met...
Gradient boosting of regression trees is a competitive procedure for lea...
From social science to biology, numerous applications often rely on grap...
From social networks to Internet applications, a wide variety of electro...
Network sampling is integral to the analysis of social, information, and...
The majority of real-world networks are dynamic and extremely large (e.g...
Relational data representations have become an increasingly important to...
To understand the structural dynamics of a large-scale social, biologica...
Temporal networks are ubiquitous and evolve over time by the addition,
d...