<p>Lead-lag relationships and effects among financial assets are fundamental for understanding market dynamics and predicting price movements. However, accurately detecting these evolving temporal dependencies remains a complex challenge. Traditional approaches predominantly rely on statistical methods based on price evidence, while machine learning and deep learning techniques remain largely unexplored in this context. The lead-lag relationships and effects can be naturally represented using a dynamic graph structure, although this direction is still uninvestigated in the literature. Indeed, existing studies rarely leverage graph-based representations, and when they do, they typically consider static rather than dynamic structures, limiting their ability to capture temporal evolution. To overcome these limitations, this study proposes a novel framework that: (i) formulates lead-lag relationships and effects detection as a temporal link prediction task on dynamic graphs; (ii) introduces a novel real-world benchmark task for the evaluation and comparison of Temporal Graph Neural Networks (TGNNs); (iii) adapts, extends, and defines nine deep learning models ranging from simple LSTMs to State-of-the-Art TGNNs; (iv) explicitly evaluates two scenarios: lead-lag relationships that are both positive and negative, as well as those that are only positive; (v) performs an ablation study to assess the impact of the key components of the considered approaches. The experiments were conducted on a custom-gathered dataset of financial assets enriched with temporal, structural, and sentiment features. The findings demonstrate that temporal graph learning effectively models complex lead-lag relationships, opening new avenues for data-driven financial market analysis.</p>