My GNN Project: 5 Secrets for Next Activity Prediction 2025

It’s 2025, and the world of AI is moving faster than ever. If you're working in machine learning, you know that predicting human behavior is one of the ultimate holy grails. Specifically, next activity prediction—forecasting what a person will do next based on their recent actions—is a problem with huge implications for everything from smart home assistants to personalized healthcare.

For the past few months, I’ve been deep in the trenches with a Graph Neural Network (GNN) project aimed at cracking this very problem. And let me tell you, the standard textbook approach only gets you so far. After countless experiments, late-night debugging sessions, and a few eureka moments, I've uncovered what I believe are the key ingredients for building a state-of-the-art activity prediction model today.

Forget just throwing a GAT or GCN layer at your data. To truly excel, you need to think differently about the problem. Here are the five secrets that took my GNN project from “meh” to “magic.”

Secret 1: Go Way Beyond One-Hot Node Features

When we start a project like this, it's tempting to represent each activity (like 'Work', 'Eat', 'Sleep') as a simple node with a one-hot encoded feature vector. It’s easy, but it’s a trap. You're leaving a massive amount of predictive power on the table.

Human activities don't happen in a vacuum. The context is everything.

Why Context is King

The likelihood of you going to the 'Gym' after 'Work' is very different on a Tuesday evening versus a Saturday morning. The activity 'Make Coffee' means something different at 7 AM than it does at 10 PM. Your model needs this context.

How to Implement Richer Features

Instead of a simple ID, enrich your node features for each activity instance. Each node in your graph should represent not just an activity, but an event. Include features like:

• Temporal Features: Time of day (cyclically encoded with sin/cos), day of the week, is_weekend.
• Duration: How long did the previous activity last? A 30-minute 'Work' session is different from an 8-hour one.
• Location Context: If you have location data, add features like `at_home`, `at_work`, or even cluster locations into semantic categories.

By feeding the GNN this rich, contextual information, you give it the clues it needs to learn the nuanced patterns of daily life. This was the single biggest performance booster in my project.

Secret 2: Your Edges Crave Temporal Information

In a typical GNN, an edge simply means “activity B happened after activity A.” All transitions are treated equally. But in reality, the time between activities is a powerful signal.

Think about it: the transition from 'Finish Dinner' to 'Watch TV' is very different if it happens in 5 minutes versus 3 hours. The first implies a routine; the second suggests something else happened in between that you may not have data for.

The Power of Time Deltas

The secret here is to encode the time delta as an edge attribute. When creating an edge from node A to node B, calculate the time difference between them (e.g., in minutes or hours) and attach it to the edge.

But don't just use the raw number. It's better to use a more sophisticated encoding, similar to how positional encodings work in Transformers. You can use a set of sinusoidal functions to represent the time delta. This helps the model understand the scale and periodicity of time gaps without being thrown off by large outliers.

This tells your model not just *what* happened next, but *when* and with what rhythm. It helps the GNN distinguish between immediate causal actions and loosely correlated events.

Secret 3: Stop Thinking Statically. Your Graph is Alive.

Most GNN tutorials will have you build a single, static graph from your entire dataset. Every activity sequence is added to one giant graph, and the model learns from there. This is a flawed representation of human behavior.

Habits evolve. Routines change. A pattern you had six months ago might be irrelevant today. A static graph mixes old, stale patterns with new, relevant ones, confusing the model.

Enter Temporal Graph Networks (TGNs)

The cutting-edge solution for 2025 is to use a dynamic graph approach. Frameworks like Temporal Graph Networks (TGNs) are designed for this. A TGN processes events one by one, updating a memory or state for each node as it appears in a new event. The graph structure itself evolves over time.

Here’s a simple way to think about it:

• Instead of one big graph, the model processes a stream of interactions (edges).
• Each time a node is involved in an interaction, its internal “memory” (a state vector) is updated.
• When predicting the next activity for a user, the model uses the *current* state of that user's node memory, which reflects their most recent behavior.

This approach allows the model to naturally adapt to changing routines and forget outdated patterns, making it far more robust for real-world, long-term prediction.

Secret 4: Your Loss Function is More Than an Afterthought

We all default to Cross-Entropy Loss for classification tasks. It’s the standard, and it works. But for next activity prediction, your data is likely to be heavily imbalanced. Users spend most of their time in a few common states ('Sleep', 'Work', 'Relax'), while other activities ('Go to Doctor', 'Exercise') are rare.

A standard Cross-Entropy Loss will lead to a model that gets great accuracy by simply always predicting the most common activities. It will be terrible at predicting the rare, but often more important, events.

Focal Loss to the Rescue

The secret is to use a loss function designed for class imbalance. My weapon of choice was Focal Loss. In essence, Focal Loss modifies the standard cross-entropy loss to down-weight the loss assigned to well-classified examples. This forces the model to focus its efforts on learning the hard, rare examples.

Implementing it is straightforward with most modern frameworks, and it can be the difference between a model that just predicts the obvious and one that uncovers genuinely surprising and useful predictions.

Secret 5: Make Your GNN Explain Itself (XAI)

You have a model with 90% accuracy. Great. But *why* did it predict 'Go for a Run' right now? If you can't answer that, you have a black box. For any real-world application, that’s not good enough.

Building trust and debugging your model requires explainability. The final secret is to integrate GNN explainability methods into your workflow from the start.

Using GNNExplainer

Tools like GNNExplainer can analyze a trained GNN's prediction for a specific instance. It identifies the most influential subgraph and node features that led to that decision. Instead of just getting a prediction, you get a story:

Prediction: 'Prepare Dinner'
Reason: The model's decision was most influenced by the sequence [Activity: 'Leave Work' (High Importance)] -> [Time: '6:30 PM' (Medium Importance)] -> [Day: 'Wednesday' (Low Importance)].

This isn't just for showing off. It’s an incredible debugging tool. If the model makes a strange prediction, the explanation will often point directly to a data quality issue or a pattern you hadn't considered. It turns your model from a black box into a collaborative partner in understanding the data.

Conclusion: Tying It All Together

Building a powerful next activity prediction model in 2025 isn't about finding one magic GNN architecture. It's about a holistic approach that respects the nature of human behavior.

By combining these five secrets—rich contextual features, temporal edges, dynamic graph structures, a smart loss function, and built-in explainability—you create a system that is more than the sum of its parts. You build a model that doesn't just see a sequence of labels; it understands the rhythm, context, and evolving nature of a person's life.

So, as you embark on your next GNN project, I challenge you to look beyond the standard tutorials. The real breakthroughs are waiting in these thoughtful, nuanced details.