In this paper, we present Training-Free Optimal Community Matching (TF-OCM), a novel, training-free approach to few-shot learning that leverages the compositional nature of images. Unlike current state-of-the-art deep learning-based methods, which rely on meta-training (inductive) or access the complete test-set distribution (transductive), TF-OCM operates entirely without training. Our method utilizes a frozen pre-trained encoder to obtain an initial patch-level image representation. This representation is then segmented into meaningful “communities" (segments) via optimizing the Modularity measure. Subsequently, these communities are compared between query and support images via minimum-cost bipartite matching, focusing on structural similarity. This approach enables TF-OCM to achieve robust performance across diverse domains. Experimental results demonstrate that TF-OCM significantly outperforms state-of-the-art inductive methods. Notably, it also surpasses some transductive methods, despite their access to the testing set distribution. Surprisingly, TF-OCM establishes a new state-of-the-art on the challenging BCCD dataset, exceeding the previous transductive state-of-the-art method by a significant 1.35%. This work highlights the strong potential of classical graph-based approaches to computer vision techniques in an era where deep learning dominates the field. TF-OCM offers a biologically plausible, training-free, and domain-generalizable solution for few-shot recognition, showcasing the potential of exploring alternative paradigms beyond traditional deep learning frameworks. Our code will be available at https://github.com/AhmedMostafaSoliman/TF-OCM .