  Heterogeneous networks, which connect informative nodes containing text with
different edge types, are routinely used to store and process information in
various real-world applications. Graph Neural Networks (GNNs) and their
hyperbolic variants provide a promising approach to encode such networks in a
low-dimensional latent space through neighborhood aggregation and hierarchical
feature extraction, respectively. However, these approaches typically ignore
metapath structures and the available semantic information. Furthermore, these
approaches are sensitive to the noise present in the training data. To tackle
these limitations, in this paper, we propose Text Enriched Sparse Hyperbolic
Graph Convolution Network (TESH-GCN) to capture the graph's metapath structures
using semantic signals and further improve prediction in large heterogeneous
graphs. In TESH-GCN, we extract semantic node information, which successively
acts as a connection signal to extract relevant nodes' local neighborhood and
graph-level metapath features from the sparse adjacency tensor in a
reformulated hyperbolic graph convolution layer. These extracted features in
conjunction with semantic features from the language model (for robustness) are
used for the final downstream task. Experiments on various heterogeneous graph
datasets show that our model outperforms the current state-of-the-art
approaches by a large margin on the task of link prediction. We also report a
reduction in both the training time and model parameters compared to the
existing hyperbolic approaches through a reformulated hyperbolic graph
convolution. Furthermore, we illustrate the robustness of our model by
experimenting with different levels of simulated noise in both the graph
structure and text, and also, present a mechanism to explain TESH-GCN's
prediction by analyzing the extracted metapaths.
