Abstract In Socially Aware Networks (SANs), nodes exhibit some degree of individual
selfishness and social selfishness because of their limited resources and the strength of …

Influence maximization is an important research area in social network analysis where
researchers are concerned with detecting influential nodes. The detection of influential …

Abstract Graph Neural Networks (GNNs) have achieved great success in representing data
with dependencies by recursively propagating and aggregating messages along the edges …

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …

Large language models (LLMs) have achieved impressive performance on many natural
language processing tasks. However, their capabilities on graph-structured data remain …

Graph embedding is an advantageous technique for reducing computational costs and
effectively using graph information in machine learning tasks like classification, clustering …

Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train
GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed …

The mouse is a pervasive input device used for a wide range of interactive applications.
However, computational modelling of mouse behaviour typically requires time-consuming …

Influence maximization (IM) has been extensively researched in the information propagation
field and applied in various domains. However, existing studies on the IM have primarily …

Compressive Learning (CL) has proven to be highly successful in executing joint signal
sampling and inference for intricate vision tasks through resource-limited Internet of Things …