HeteroGraphNet: A Ligand–Receptor Informed, Heterophily-Adapted Graph Neural Network for Cell Type Prediction in scRNA-Seq Data

Graph Neural Networks (GNNs) have emerged as powerful tools for modeling complex relational data, yet most existing architectures assume homophily-where connected nodes share similar features-an assumption that does not hold in many biological systems. In single-cell RNA sequencing (scRNA-seq) data, intercellular communication networks often exhibit heterophily, with meaningful interactions occurring between dissimilar cell types. Moreover, conventional graph construction in this domain frequently relies on arbitrary similarity thresholds, overlooking biologically validated interaction pathways. We address these limitations with HeteroGraphNet, a heterophily-adapted GNN that incorporates ligand-receptor ($\mathbf{L}-\mathbf{R}$) interactions inferred from scRNA-seq data using LIANA to construct biologically grounded cell-cell graphs. Our model combines a bi-kernel aggregation mechanism-capable of capturing both homophilic and heterophilic signals-with cosine similarity-guided adaptive random walks that dynamically update neighborhoods during training. We further mitigate class imbalance through weighted loss functions, ensuring robust performance across underrepresented cell types. Across six scRNA-seq datasets, HeteroGraphNet consistently outperforms a multi-layer perceptron (MLP), four standard GNNs (GCN, GAT, GraphSAGE, MixHop), and two heterophily-specific GNNs (H2GCN, GBK-GNN), with especially strong gains on low-homophily graphs. These results demonstrate that incorporating ligand-receptor-informed connectivity with adaptive neighborhood exploration enables more accurate and biologically meaningful cell type prediction in heterogeneous single-cell interaction networks, offering a scalable framework for broader biological network analysis.

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