Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

Link prediction is a critical task within the realm of graph machine learning. While recent
advancements mainly emphasize node representation learning, the rich information …

Recent years have witnessed the remarkable success of applying Graph machine learning
(GML) to node/graph classification and link prediction. However, edge classification task that …

Graph representation learning (GRL) is to encode graph elements into informative vector
representations, which can be used in downstream tasks for analyzing graph-structured data …

Benchmark datasets and task definitions in temporal graph learning are limited to dynamic
node classification and future link prediction. In this paper, we consider the task of edge …

Deep Learning (DL) has demonstrated remarkable success in various Remote Sensing
Image (RSI) analysis applications. However, due to disparities in data distributions, DL …

Abstract The emergence of Graph Neural Networks (GNNs) has led to the development of
several GNN-based anomaly detection models that detect anomalies in attributed graphs …

The performance of Temporal Graph Learning (TGL) methods are typically evaluated on the
future link prediction task, ie, whether two nodes will get connected and dynamic node …

This thesis comprises three interconnected projects addressing challenges in multi-label
classification and temporal graph learning. In the first project, we tackle the challenge of …

Given a network G, edge centrality is a metric used to evaluate the importance of edges in G,
which is a key concept in analyzing networks and finds vast applications involving edge …