There has been significant interest in integrating sentiment analysis with graph neural networks (GNNs) for stock prediction tasks. This article thoroughly reviews the application of GNNs in conjunction with sentiment analysis for stock prediction. This study introduces the fundamental concepts...

We study binary action network games with strategic complementarities. An agent acts if the aggregate social influence of her friends exceeds a transfer levied on the agent by a principal. The principal seeks to maximize her revenue while inducing everyone to act in a unique equilibrium. We...

Sustainable urban development involves many fields with complex data types and rich semantic relationships, such as the economic, societal, and ecological fields. Knowledge graphs provide a new means for sustainable urban development research by leveraging their strengths in the construction of...

In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and...

Community detection can help uncover and understand complex networks' underlying patterns and structures. It involves identifying cohesive groups with similar entities while being separated from other groups. Social networks are a prime example of an area where community detection is...

We consider the price-optimal earliest arrival problem in public transit (POEAP) in which we aim to calculate the Pareto-set of journeys with respect to ticket price and arrival time in a public transportation network. Public transit fare structures are often a combination of various fare...