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<h1 class="jumbotron-heading text-capitalize" id="Teaching">Advances in Human Event Modeling: <br/>From Graph Neural Networks to Language Models</h1>
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<br>
Tutorial in 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)
<hr>
<div class="mt-3">
<ul>
<li><b>Presenters</b>: <a href="https://songgaojundeng.github.io/">Songgaojun Deng</a> and <a href="https://yue-ning.github.io/">Yue Ning</a></li>
<li><b>Other contributor</b>: <a href="https://staff.fnwi.uva.nl/m.derijke/">Maarten de Rijke</a></li>
<li><b>ID</b>: LS-3</li>
<li class="text-danger"><b>Date</b>: Sunday, August 25 2024</li>
<li class="text-danger"><b>Time</b>: 10:00 AM – 1:00 PM</li>
<li><b>Place</b>: Centre de Convencions Internacional de Barcelona, Barcelona, Spain</li>
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<div class="mt-3">
<h4>Abstract</h4>
Human events are events that directly involve individuals, communities, societies, or humanity as a whole. Human events are often influenced by factors such as the economy, public policies, and civil unrest. Recently, machine learning methods including graph neural networks as well as (large) language models have been developed to forecast and interpret temporal human events from online data sources such as social networks, news articles, and personal blogs. Advancing AI in this subject can enhance decision-making in social science, public health care, and governance. Workshops for predictive analytics have been organized to educate participants regarding various challenges in human event modeling across diverse sectors involving business, finance, healthcare, and government. Human event modeling focuses on forecasting that estimates future events based on historical data. Interpretation of events seeks to identify explainable factors for the predictions to understand the underlying mechanisms of events.
<br>
<br>
In this tutorial, we present recent and ongoing advances in human event modeling. We start by introducing how events are formulated into graphs with auxiliary features including text and time series. Next, we discuss graph learning models, a prominent research area in deep learning focusing on modeling data with structural information. Additionally, we explore the insights gained from using language models in human event modeling. Finally, we discuss challenges and identify future opportunities in the field.
<br>
<br>
We want to emphasize the timeliness and significance of this tutorial. Human event modeling has a long history, dating back to the 19th century. However, the recent rise of large language models has changed the space of the problem. We anticipate significant advancements in the near future, making this tutorial timely and relevant.
</div>
<div class="mt-3">
<h4>Outline (tentative)</h4>
<ul>
<li>Introduction and motivation [40]</li>
<ul>
<li>Societal event prediction</li>
<li>Challenges in human event prediction</li>
<li>Explainable event prediction</li>
<li>Early approaches</li>
</ul>
<li>Methodology</li>
<ul>
<li>Part 1: Graph Neural Network (GNN)-based methods [55]</li>
<ul>
<li>Vanilla graph learning</li>
<li class="text-success">[Break - 15]</li>
<li>Graph learning with contextual information</li>
<li>Graph learning with causal reasoning</li>
</ul>
<li class="text-success">[Break - 15]</li>
<li>Part 2: Large Language Model (LLM)-based methods [50]</li>
<ul>
<li>LLMs for event prediction</li>
<li>LLMs for event data construction</li>
<li>Evaluating LLMs for event forecasting</li>
</ul>
</ul>
<li>Summary and future work [5]</li>
</ul>
<!-- Introduction [20]
Societal Event Prediction
Main Challenges in Explainable Event Prediction
Temporal Event Forecasting
Part 1: Early approaches [20]
Nested Multi-instance learning
Multi-task learning
[Break - 30]
Part 2: Event Graphs for Interpretable Event Forecasting [60]
Vanilla graph learning
Graph learning with contextual information
Graph learning with causal reasoning
Part 3: Large language models for Societal Event Forecasting [60]
LLMs as valuable tools
Part 4: Data resources [10]
Summary and Future Work [5] -->
</div>
<div class="mt-3">
<h4>Resources</h4>
<ul>
<li><a href="https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/deng-2024-advances.pdf">Survey paper</a></li>
<li><a href="KDD24-Tutorial-Slides-reduced.pdf">Slides</a></li>
</ul>
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