  Recent advances in Graph Neural Networks (GNN) have led to a considerable
growth in graph data modeling for multi-modal data which contains various types
of nodes and edges. Although some integrative prediction solutions have been
developed recently for network-structured data, these methods have some
restrictions. For a node classification task involving multi-modal data,
certain data modalities may perform better when predicting one class, while
others might excel in predicting a different class. Thus, to obtain a better
learning representation, advanced computational methodologies are required for
the integrative analysis of multi-modal data. Moreover, existing integrative
tools lack a comprehensive and cohesive understanding of the rationale behind
their specific predictions, making them unsuitable for enhancing model
interpretability. Addressing these restrictions, we introduce a novel
integrative neural network approach for multi-modal data networks, named
Integrative Graph Convolutional Networks (IGCN). IGCN learns node embeddings
from multiple topologies and fuses the multiple node embeddings into a weighted
form by assigning attention coefficients to the node embeddings. Our proposed
attention mechanism helps identify which types of data receive more emphasis
for each sample to predict a certain class. Therefore, IGCN has the potential
to unravel previously unknown characteristics within different node
classification tasks. We benchmarked IGCN on several datasets from different
domains, including a multi-omics dataset to predict cancer subtypes and a
multi-modal clinical dataset to predict the progression of Alzheimer's disease.
Experimental results show that IGCN outperforms or is on par with the
state-of-the-art and baseline methods.
