Collective Classification (CC) is the process of simultaneously inferring the class labels of a
set of inter-linked nodes, such as the topic of publications in a citation graph. Recently …

Regression inference in network data is a challenging task in machine learning and data
mining. Network data describe entities represented by nodes, which may be connected with …

The present notes are derived from a course taught at the University of Southern California.
The focus of the course is on the mathematical and algorithmic theory underpinning the …

Graph convolution operator of the GCN model is originally motivated from a localized first-
order approximation of spectral graph convolutions. This work stands on a different view; …

Research on link prediction for social networks has been actively pursued. In link prediction
for a given social network obtained from time-windowed observation, new link formation in …

Data often comes in the form of a graph. When it does not, it often makes sense to represent
it as a graph for learning tasks that rely on the similarities or relationships between data …

Graph Neural Networks (GNNs) have emerged as a powerful tool in machine learning for
the modeling, analysis, and prediction of complex interaction networks. Our research is …

Link prediction on graphs has achieved great success with the rise of deep graph learning.
However, the potential robustness under the edge noise is less investigated. We reveal that …

In the early years of data mining and knowledge discovery in databases, method
development focused on rigidly and plainly structured data. Most often efforts were even …