DS-Span: Single-Phase Discriminative Subgraph Mining for Efficient Graph Embeddings

TL;DR

DS-Span is a unified, single-phase framework for discriminative subgraph mining that enhances efficiency and interpretability in graph embedding and classification tasks.

Contribution

It introduces a novel single-phase approach with dynamic exploration limits and information-gain scoring, improving over multi-stage methods in efficiency and discriminative power.

Findings

Produces more compact, discriminative subgraph features

Achieves higher or comparable accuracy to prior methods

Reduces runtime significantly

Abstract

Graph representation learning seeks to transform complex, high-dimensional graph structures into compact vector spaces that preserve both topology and semantics. Among the various strategies, subgraph-based methods provide an interpretable bridge between symbolic pattern discovery and continuous embedding learning. Yet, existing frequent or discriminative subgraph mining approaches often suffer from redundant multi-phase pipelines, high computational cost, and weak coupling between mined structures and their discriminative relevance. We propose DS-Span, a single-phase discriminative subgraph mining framework that unifies pattern growth, pruning, and supervision-driven scoring within one traversal of the search space. DS-Span introduces a coverage-capped eligibility mechanism that dynamically limits exploration once a graph is sufficiently represented, and an information-gain-guided selection that promotes subgraphs with strong class-separating ability while minimizing redundancy. The resulting subgraph set serves as an efficient, interpretable basis for downstream graph embedding and classification. Extensive experiments across benchmarks demonstrate that DS-Span generates more compact and discriminative subgraph features than prior multi-stage methods, achieving higher or comparable accuracy with significantly reduced runtime. These results highlight the potential of unified, single-phase discriminative mining as a foundation for scalable and interpretable graph representation learning.