TAS-EGNN: Task-Aware Spectral Ego-Graphs for Efficient GNNs-Based Classification

Graph Neural Networks (GNNs) achieve strong accuracy but remain costly to train on large graphs and in resource-constrained settings. Coreset selection mitigates this by training on a compact, representative node subset, yet many existing methods rely on expensive spectral routines or bilevel and iterative optimizations. We propose a Task-Aware Spectral Ego-Graph Neural Network (TAS-EGNN) that scores nodes within lightweight ego-graphs by combining (i) local spectral complexity, (ii) predictive uncertainty, and (iii) supervised error signals, followed by a greedy coverage step to avoid redundancy. TAS-EGNN circumvents heavy optimization, using only local spectra (or moment proxies) and a single model forward pass to obtain task signals. We evaluate TAS-EGNN across three benchmark tasks: citation networks, social networks, and graph-based bank transaction fraud detection. The third task, in particular, underscores the algorithm’s effectiveness in anomaly detection for highly imbalanced settings. TAS-EGNN matches or surpasses state-of-the-art reduction baselines, across \emph{budgets} (i.e., the allowed size of the selected training subset, controlled via the coreset ratio), including condensation, coarsening, and ego-graph selection, while delivering substantial wall-clock and peak-memory savings. Time and memory profiling show that TAS-EGNN tracks the lower envelope among structure-aware methods and scales to large graphs, whereas several other works reach OOT/OOM. These results indicate that efficiently encoded task-aware structural priors enable robust, scalable coreset selection for both standard node classification and fraud detection. The source code will be available on GitHub.