FDphormer: Beyond Homophily with Feature-Difference Position Encoding

What is it about?

In many real-world networks—like social networks, citation graphs, or biological systems—connected items (called "nodes") often have very different characteristics. This is known as heterophily. Traditional graph learning models, including popular Graph Neural Networks (GNNs) and graph Transformers, usually assume that connected nodes are similar (a property called homophily). This assumption doesn't hold in heterophilic scenarios, leading to poor performance. To solve this, we introduce FDphormer, a new kind of graph Transformer. Unlike existing methods, FDphormer doesn't just look at how nodes are connected—it also examines the differences between the features of connected nodes. We designed a new encoding method called DiSP, which captures these differences and helps the model better understand relationships in heterophilic graphs. We also explored the math behind how models generalize to new data and found that while adding this feature-difference information increases the model's power, it also risks overfitting. To address this, we add a regularization strategy to keep the model stable. Extensive experiments show that FDphormer performs very well on various types of graphs, especially those with heterophily, while remaining competitive on more traditional (homophilic) data.

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Photo by Logan Voss on Unsplash

Photo by Logan Voss on Unsplash

Why is it important?

In recent years, graph Transformers have become popular for understanding complex network data, such as social networks, molecules, or knowledge graphs. However, most of these models are still built around the assumption that connected nodes are similar. This works well for some datasets, but breaks down in real-world cases where connected nodes are actually very different—a common situation known as heterophily. What makes our work unique is that we directly address this challenge. We propose a new position encoding method called DiSP, which measures not just the structure of the graph, but the feature differences between connected nodes. This allows the model to better distinguish between meaningful and misleading relationships—something previous methods largely ignored. Our work is also timely, as interest in applying Transformers to graph-structured data is growing rapidly. But many current models still struggle in heterophilic settings like protein interaction networks, social media, or recommendation systems. FDphormer bridges this gap, providing a simple and efficient way to boost performance where it’s needed most. By combining theoretical analysis with practical improvements, our model is not just a new tool—it could reshape how graph learning models are designed for diverse and challenging datasets.

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