# Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks Source: [Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:00:33.232Z Authors: Amirhossein Nouranizadeh, Fatemeh Tabatabaei Far, Mohammad Rahmati Published: 2024-08-22T22:50:46.000Z ## Abstract Evolving networks are complex data structures that emerge in a wide range of systems in science and engineering. Learning expressive representations for such networks that encode their structural connectivity and temporal evolution is essential for downstream data analytics and machine learning applications. In this study, we introduce a self-supervised method for learning representations of temporal networks and employ these representations in the dynamic link prediction task. While temporal networks are typically characterized as a sequence of interactions over the continuous time domain, our study focuses on their discrete-time versions. This enables us to balance the trade-off between computational complexity and precise modeling of the interactions. We propose a recurrent message-passing neural network architecture for modeling the information flow over time-respecting paths of temporal networks. The key feature of our method is the contrastive training objective of the model, which is a combination of three loss functions: link prediction, graph reconstruction, and contrastive predictive coding losses. The contrastive predictive coding objective is implemented using infoNCE losses at both local and global scales of the input graphs. We empirically show that the additional self-supervised losses enhance the training and improve the model's performance in the dynamic link prediction task. The proposed method is tested on Enron, COLAB, and Facebook datasets and exhibits superior results compared to existing models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Link Prediction on Enron (last three snapshots)](): Link Prediction; Enron (last three snapshots); AUC: 93.54. - [Link Prediction on COLAB (last three snapshots)](): Link Prediction; COLAB (last three snapshots); AUC: 0.8825. - [Link Prediction on Facebook (last three snapshots)](): Link Prediction; Facebook (last three snapshots); AUC: 91.03. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()