An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs

TL;DR

This paper empirically compares budget-aware online kernel algorithms for graph streams, demonstrating that feature space methods outperform dual approaches under strict memory constraints in terms of speed and accuracy.

Contribution

It introduces and evaluates feature space kernel algorithms for graph streams, a novel approach tailored to memory-limited online learning with graphs.

Findings

Feature space algorithms are more efficient under memory constraints.

Working in feature space yields better classification performance.

Feature space methods are viable alternatives to dual approaches for graph streams.

Abstract

Kernel methods are considered an effective technique for on-line learning. Many approaches have been developed for compactly representing the dual solution of a kernel method when the problem imposes memory constraints. However, in literature no work is specifically tailored to streams of graphs. Motivated by the fact that the size of the feature space representation of many state-of-the-art graph kernels is relatively small and thus it is explicitly computable, we study whether executing kernel algorithms in the feature space can be more effective than the classical dual approach. We study three different algorithms and various strategies for managing the budget. Efficiency and efficacy of the proposed approaches are experimentally assessed on relatively large graph streams exhibiting concept drift. It turns out that, when strict memory budget constraints have to be enforced, working in feature space, given the current state of the art on graph kernels, is more than a viable alternative to dual approaches, both in terms of speed and classification performance.