TouchUp-G: Improving Feature Representation through Graph-Centric
  Finetuning
Jing Zhu Xiang Song Vassilis N. Ioannidis Danai Koutra Christos Faloutsos
How can we enhance the node features acquired from Pretrained Models (PMs) to
better suit downstream graph learning tasks? Graph Neural Networks (GNNs) have
become the state-of-the-art approach for many high-impact, real-world graph
applications. For feature-rich graphs, a prevalent practice involves utilizing
a PM directly to generate features, without incorporating any domain adaptation
techniques. Nevertheless, this practice is suboptimal because the node features
extracted from PM are graph-agnostic and prevent GNNs from fully utilizing the
potential correlations between the graph structure and node features, leading
to a decline in GNNs performance. In this work, we seek to improve the node
features obtained from a PM for downstream graph tasks and introduce TOUCHUP-G,
which has several advantages. It is (a) General: applicable to any downstream
graph task, including link prediction which is often employed in recommender
systems; (b) Multi-modal: able to improve raw features of any modality (e.g.
images, texts, audio); (c) Principled: it is closely related to a novel metric,
feature homophily, which we propose to quantify the potential correlations
between the graph structure and node features and we show that TOUCHUP-G can
effectively shrink the discrepancy between the graph structure and node
features; (d) Effective: achieving state-of-the-art results on four real-world
datasets spanning different tasks and modalities.