A data-centric lens for representation learning in graphs via semantic and structural enhancement

Abstract

While progress in graph representation learning has often been model-centric: focusing on the design of more powerful architectures, this thesis presents a data-centric lens: a framework viewing the graph not as a static input, but as an object to be actively and intelligently enhanced. We argue and then show that superior performance can be unlocked through a deliberate progression, beginning with specialised node-level semantic enhancement and culminating in generic graph-level structural enhancement. Our investigation begins with the specialised task of citation recommendation in scholarly networks, where we first explore semantic enhancement. Our formative framework, SymTax, enriches node data by modelling human exploratory behaviour via symbiotic relationship and by fusing taxonomies into hyperbolic space. Building upon this, we address critical issues of scalability and real-world applicability by introducing Profiler, a highly efficient module that creates a rich Public Profile for each paper by statically aggregating signals from its inward citations, which is then carefully integrated with our designed DAVINCI reranker. This advancement is validated under our proposed Inductive evaluation protocol, establishing a more rigorous, robust and realistic standard for the field. Having established the power of semantic enrichment, we then widen the horizon of our data-centric lens to structural enhancement for the generalised challenge of learning on heterophilic graphs. We propose PathLens, a model-agnostic technique that fundamentally augments the graph’s topology. By strategically adding supernodes derived from spectral clustering, PathLens creates new information pathways, effectively reducing the average shortest path length between similar nodes thereby increasing the graph’s homophily. This directly addresses the core impediment of heterophily, significantly boosting the performance of a wide range of standard GNNs as well as heterophilic GNNs by providing them with a more learnable graph structure. Collectively, this body of work validates the efficacy of the data-centric lens. By first enriching the semantic content of nodes and then augmenting the structural fabric of the graph, we demonstrate a powerful and generalisable paradigm for building high- performance systems for learning on graphs. This thesis contributes not only a suite of novel techniques but also a guiding philosophy that prioritises the enhancement of the data itself as a primary driver of success for representation learning in graphs.