Hybrid neural network system for processing graph-structured and sequential data

A hybrid neural network system for processing graph-structured and sequential data, comprising:

• a Graph Convolutional Network (GCN) module configured to process graph-structured data, wherein the GCN module includes multiple graph convolution layers and ReLU activation;
• a Recurrent Neural Network (RNN) module configured to process sequential data, wherein the RNN module includes at least one RNN layer and a fully connected layer;
• a weighted system configured to combine outputs from the GCN module and the RNN module, wherein the weighted system includes a learnable parameter “a” for dynamically adjusting the contribution of the GCN and RNN modules;
• an input component configured to incorporate pre-trained language model (LLM) embeddings or outputs alongside the GCN and RNN outputs;
• wherein the hybrid neural network system is configured to generate a combined representation from the GCN module, the RNN module, and the LLM input component for producing a final output.