The Temporal Graph Learning (TGL) workshop, now in its third edition at KDD 2025, offers an interdisciplinary platform for researchers to explore the evolving applications of temporal networks in various domains, including recommender systems, social network analysis, traffic analytics, and epidemiological data analysis.The workshop aims to facilitate the exchange of ideas across disciplines, highlight successes and challenges in TGL, and outline future research directions.The workshop welcomes diverse contributions, offers keynote talks from academic and industry experts, and is complemented by a panel discussion on emerging aspects of TGL.

Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely designed for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs. TGX provides access to eleven built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as well as any novel datasets in the .csv format. Beyond data loading, TGX facilitates data processing functionalities such as discretization of temporal graphs and node sub-sampling to accelerate working with larger datasets. For comprehensive investigation, TGX offers network analysis by providing a diverse set of measures, including average node degree and the evolving number of nodes and edges per timestamp. Additionally, the package consolidates meaningful visualization plots indicating the evolution of temporal patterns, such as Temporal Edge Appearance (TEA) and Temporal Edge Traffic (TET) plots. The TGX package is a robust tool for examining the features of temporal graphs and can be used in various areas like studying social networks, citation networks, and tracking user interactions. We plan to continuously support and update TGX based on community feedback. TGX is publicly available on: https://github.com/ComplexData-MILA/TGX.

Many real-world complex systems can be modelled by temporal networks. Representation learning on these networks often captures their dynamic evolution and is a first step for performing further analysis, e.g. node classification. Node classification is a fundamental task for graph analysis in general and in the context of temporal graph, is often employed to categories nodes based on their activity patterns. Analysis of existing real world networks from different high-stake domains reveals that the rate of the malicious activities is on uptick, resulting in catastrophic social or economic consequences. This strongly motivates designing accurate node classification methods for temporal graphs. In this paper, we propose TGbase, for node classification on weighted temporal networks. TGbase efficiently extracts key features to consider the structural characteristics of each node and its neighborhood as well as the intensity and timestamp of the interactions among node pairs. These features accurately differentiate different classes of nodes, as shown on eight real-world benchmark datasets, outperforming multiple state-of-the-art (SOTA) deep/complex models. Our strong yet simple model is also generic, whereas the SOTA contenders are designed often for their specific (class of) datasets.