Traditional analysis of interconnection networks primarily relies on graph-theoretic metrics such as node degree, diameter, and connectivity, which provide only aggregate-level insights and fail to capture detailed structural patterns. This limitation restricts the ability to distinguish between network topologies that may exhibit similar global properties but differ significantly in their internal connectivity distributions. To address this issue, this study proposes a Boolean matrix-based analytical framework that enables fine-grained, pattern-oriented analysis of network structures. In the proposed approach, interconnection networks are represented using Boolean adjacency matrices B=[b_ij], where b_ij∈{0,1}. Fundamental logical operations—AND (∧), OR (∨), and XOR (⊕)—are applied to these matrices to extract structural characteristics such as common connectivity, overall reachability, and structural deviations. The framework is applied to two prominent network topologies: the Hypercube Network and the Perfect Difference Network (PDN). The results reveal distinct structural patterns between the two networks. The Hypercube exhibits high regularity, symmetry, and uniform connectivity, while the PDN demonstrates comparatively irregular but efficient connectivity patterns. Quantitative metrics such as number of 1s, density, symmetry, and variance, along with XOR-based similarity measures, provide deeper insights into network morphology. This work contributes a novel Boolean-based comparison framework that enables precise structural analysis and comparison of interconnection networks, offering a mathematically rigorous and extensible approach for network design and performance evaluation.

Boolean Matrix Hypercube Network Perfect Difference Network (PDN) XOR Operation Structural Difference Measure Connectivity Analysis Parallel Processing Networks