In World Wide Web (WWW) systems, networks (or graphs) serve as a fundamental tool for representing, analyzing, and understanding linked data, providing significant insights into the underlying systems. Naturally, most real-world systems have inherent temporal information, e.g., interactions in social networks occur at specific moments in time and last for a certain period. Temporal networks, i.e., network data modeling temporal information, enable novel and fundamental discoveries about the underlying systems they model, otherwise not captured by static networks that ignore such temporal information. In this tutorial, we present state-of-the-art models and algorithmic techniques for mining temporal networks that can provide precious insights into a plethora of web-related applications. We present how temporal networks can be used to extract novel information, especially in web-related network data, and highlight the challenges that arise when modeling temporal information compared to traditional static network-based approaches. We first overview different temporal network models. We then show how such powerful models can be leveraged to extract novel insights through suitable mining primitives. In particular, we present recent advances addressing most foundational problems for temporal network mining—ranging from the computation of temporal centrality measures, temporal motif counting, and temporal communities to bursty events and anomaly detection.