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<div class="log-event-page">
    <div class="log-header">
        <img src="/figs/events/LoG_2026/log-logo-light_new.png" alt="Learning on Graphs Conference Tromsø Meetup"
            class="log-logo log-logo-light" width="60%" />
        <img src="/figs/events/LoG_2026/log-logo-dark_new.png" alt="Learning on Graphs Conference Tromsø Meetup"
            class="log-logo log-logo-dark" width="60%" />
        <p class="subtitle">Tromsø, Norway 🇳🇴</p>
        <p class="subtitle">Date: 17-18 February 2026 | Location: UiT - the Arctic University of Norway</p>
    </div>

    <div class="log-section">
        <h2>About the Event</h2>
        <p>
            Join us for the 2025 Learning on Graphs meetup in Tromsø! This event brings together researchers,
            students, and practitioners interested in graph machine learning, graph neural networks, and related topics.
        </p>
        <p>
            Whether you're new to the field or an experienced researcher, this meetup offers great opportunities
            for networking, learning, and discussing the latest advances in graph-based machine learning.
        </p>
    </div>

    <div class="highlight">
        <strong><s>📢 Register <a href="https://forms.gle/8F6MKErJ6RpuhtL59">here</a> before Wednesday, February
                11th 2026.</s></strong>
    </div>

    <div class="log-section">
        <h2>Event Details</h2>
        <div class="info-box">
            <h3>📅 When and 📍 Where</h3>
            <ul>
                <li><strong>Date:</strong> 17-18 February 2026</li>
                <li><strong>Venue:</strong> UiT - the Arctic University of Norway</li>
                <li><strong>Rooms:</strong> Teorifagbygget hus 4, room 4.262 on February 17th / Naturfagbygget, room
                    2.108 (Vulkanen) on February 18th</li>
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                        day 1</a>
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                </div>
            </ul>
        </div>
    </div>

    <div class="log-section">
        <h2>Tentative Schedule</h2>
        <!-- <p><em>Detailed schedule will be announced soon. Here's a general outline:</em></p> -->

        <div class="schedule-item">
            <span class="schedule-time">Day 1</span>
            <ul>
                <li>08:45 Registration 🤗. Morning talk + PhD Kitchen 📚 </li>
                <li class="keynote-item">
                    <div class="keynote-header" onclick="toggleKeynote('keynote1')">
                        <span class="keynote-toggle" id="keynote1-toggle">▼</span>
                        <span>09:00 Keynote 1 🎤: Sabrina Gaito — <a
                                href="/assets/slides/log2026/Talk_Gaito_LoG_2026.pdf" target="_blank">slides
                                (PDF)</a></span>
                    </div>
                    <div class="keynote-abstract expanded" id="keynote1">
                        <div
                            style="float: right; width: 20%; max-width: 400px; margin: 0 0 10px 20px; text-align: center;">
                            <img src="/figs/news/gaito.png" style="width: 100%; height: auto;" />
                        </div>
                        <p>Abstract: <strong>Reading temporal networks through graph evolution rules</strong> <br><br>
                            Temporal networks encode not only when interactions occur, but also how recurrent mesoscopic
                            mechanisms progressively reshape connectivity. Yet, most approaches either extend static
                            descriptors to time-stamped data or adopt growth models that pre-impose a small set of
                            mechanisms. In this talk I present a framework to read temporal networks through Graph
                            Evolution Rules (GERs): probabilistic patterns, inspired by association rules, in which a
                            subgraph observed at a given time tends to evolve into one or more subgraphs at a
                            subsequent with measurable likelihood. GERs provide an explicit, mechanism-oriented
                            representation of network dynamics and yield a compact, interpretable Network Evolution
                            Profile—a distribution over statistically validated rules that captures the evolution
                            signature of a system and supports rigorous comparison across graphs, communities, and
                            ego-networks. This perspective opens the way to mechanism-preserving GNN architectures,
                            improved explainability grounded in explicit evolutionary transformations, and practical
                            applications such as anomaly detection and distributional shifts framed as changes (drift or
                            change points) in the evolution profile.</p>
                    </div>
                </li>
                <li>10:00 Coffee Break ☕️</li>
                <li class="keynote-item">
                    <div class="keynote-header" onclick="toggleKeynote('keynote3')">
                        <span class="keynote-toggle" id="keynote3-toggle">▼</span>
                        <span>10:15 Coding session 1 👩‍💻: Veronica Lachi</span>
                    </div>
                    <div class="keynote-abstract expanded" id="keynote3">
                        <div
                            style="float: right; width: 15%; max-width: 400px; margin: 0 0 10px 20px; text-align: center;">
                            <img src="/figs/people/vl.png" style="width: 100%; height: auto;" />
                        </div>
                        <p>Title: <strong>Introduction to PyTorch Geometric 📐</strong> <br><br>

                            This coding session will cover the basics of PyTorch Geometric, including data handling,
                            model building, and training of graph neural networks. Students will get hands-on experience
                            with key concepts and tools.</p>
                    </div>
                </li>
                <li>11:30 Coffee Break ☕️</li>
                <li class="keynote-item">
                    <div class="keynote-header" onclick="toggleKeynote('keynote4')">
                        <span class="keynote-toggle" id="keynote4-toggle">▼</span>
                        <span>11:45 Coding session 2 👨‍💻: Carlo Abate</span>
                    </div>
                    <div class="keynote-abstract expanded" id="keynote4">
                        <div
                            style="float: right; width: 15%; max-width: 400px; margin: 0 0 10px 20px; text-align: center;">
                            <img src="/figs/people/ca.jpeg" style="width: 100%; height: auto;" />
                        </div>
                        <p>Title: <strong>Advanced PyTorch Geometric 📐 with Torch Geometric Pool 🎱</strong> <br><br>

                            This coding session will cover advanced topics in PyTorch Geometric with the Torch Geometric
                            Pool library, including pooling techniques and graph coarsening. Participants will learn how
                            to implement and utilize these methods effectively to improve graph neural network
                            performance.</p>
                    </div>
                </li>
                <li>13:00 Lunch break 🍱 </li>
                <li>14:00 Afternoon talk 🎙️ + poster session 🗺️</li>
                <li class="keynote-item">
                    <div class="keynote-header" onclick="toggleKeynote('keynote2')">
                        <span class="keynote-toggle" id="keynote2-toggle">▼</span>
                        <span>14:15 Keynote 2 🎤: Davide Bacciu</span>
                    </div>
                    <div class="keynote-abstract expanded" id="keynote2">
                        <div
                            style="float: right; width: 20%; max-width: 400px; margin: 0 0 10px 20px; text-align: center;">
                            <img src="/figs/news/bacciu.jpg" style="width: 100%; height: auto;" />
                        </div>
                        <p>Abstract: <strong>Shaping GNNs with dynamical systems</strong> <br><br>

                            The dynamics of information diffusion in local message passing is a key issue that heavily
                            influences graph representation learning, especially when long-range propagation is needed.
                            We vouch for principled approaches that control and regulate the degree of propagation,
                            conservation and dissipation of information throughout the neural flow. In the talk we
                            explore some approaches stemming from a dynamical systems' view of neural networks for
                            static and temporal graphs, with particular focus on wide applicability of the concepts and
                            on the theoretical guarantees on information conservation.</p>
                    </div>
                </li>


                <li>15:15 Break 🍎</li>
                <li>15:30 Poster session + Pizza! 🍕</li>
                <li>17:00 End of day 1 💤</li>
                <li>20:00 Meetup social event in the city center 🍻🌃</li>
            </ul>
        </div>

        <div class="schedule-item">
            <span class="schedule-time">Day 2</span>
            <ul>
                <li>08:45 Welcome back! ☀️</li>
                <li class="keynote-item">
                    <div class="keynote-header" onclick="toggleKeynote('keynote5')">
                        <span class="keynote-toggle" id="keynote5-toggle">▼</span>
                        <span>09:00 Tutorial (part 1) 👨‍🏫: Andrea Cini — <a
                                href="/assets/slides/log2026/tutorial_log_tromsø_handout.pdf" target="_blank">handout
                                (PDF)</a></span>
                    </div>
                    <div class="keynote-abstract expanded" id="keynote5">
                        <div
                            style="float: right; width: 15%; max-width: 400px; margin: 0 0 10px 20px; text-align: center;">
                            <img src="/figs/people/ac.png" style="width: 100%; height: auto;" />
                        </div>
                        <p>Title: <strong>Graph Deep Learning for Time Series Forecasting</strong> <br><br>

                            Graph deep learning methods have become popular tools to process collections of correlated
                            time series. Unlike traditional multivariate forecasting methods, graph-based predictors
                            leverage pairwise relationships by conditioning forecasts on graphs spanning the time series
                            collection. This tutorial will explore these methods in depth, providing practical examples
                            and insights.</p>
                    </div>
                </li>
                <li>10:00 Coffee Break ☕️</li>
                <li>10:15 Tutorial (part 2) 👨‍🏫: Andrea Cini</li>
                <li>11:15 Coffee Break ☕️</li>
                <li>11:30 Tutorial (part 3) 👨‍🏫: Andrea Cini</li>
                <li>12:30 End of Meetup 🎉</li>
            </ul>
        </div>
    </div>

    <div class="log-section">
        <h2>Topics of Interest (non-exhaustive list)</h2>
        <ul>
            <li>Graph Neural Networks (GNNs)</li>
            <li>Graph representation learning</li>
            <li>Graph pooling and coarsening</li>
            <li>Applications of GNNs in various domains</li>
            <li>Theoretical aspects of graph learning</li>
            <li>Graph signal processing</li>
            <li>Network science and analysis</li>
            <li>Geometric deep learning</li>
        </ul>
    </div>

    <div class="log-section">
        <h2>Call for Contributions</h2>
        <p>
            We welcome contributions in the form of posters! If you're interested in presenting
            your work, please reach out to us at <a href="mailto:roberto.neglia@uit.no">roberto.neglia@uit.no</a> with
            an abstract of your poster by February 11th, 2026.
        </p>
    </div>

    <!-- <div class="log-section">
        <h2>Register Your Interest</h2>
        <p>Registration details will be available soon. In the meantime, feel free to reach out if you have questions!
        </p>
        Uncomment when registration is ready:
        <a href="#" class="cta-button">Register Now</a>
        <div style="text-align: center; margin: 40px 0;">
            <a href="mailto:roberto.neglia@uit.no" class="cta-button">Contact Us</a>
        </div>
    </div> -->

    <div class="log-section">
        <h2>Contact & Information</h2>
        <p>
            For questions or more information about the event, please contact us at
            <a href="mailto:roberto.neglia@uit.no">roberto.neglia@uit.no</a>
        </p>
        <p>
            Follow us for updates:
            <a href="https://github.com/ngmlgroup" target="_blank">GitHub</a>
        </p>
    </div>

    <div class="log-section">
        <h2>Sponsored by</h2>
        <p>
            This event is sponsored by <a href="https://www.integreat.no/" target="_blank">Integreat</a> — the Norwegian
            Centre for Knowledge-driven Machine Learning.
        </p>
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