The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT scenarios such as smart cities, industrial IoT, and intelligent transportation systems. However, the scale and diversity of IoT-generated data present significant challenges, and existing methods often struggle with preserving the structural integrity and semantic richness of these complex graphs. Many current approaches fail to maintain the balance between computational efficiency and the quality of the insights generated, leading to potential loss of critical information necessary for accurate decision-making in IoT applications. We introduce HeteroSample, a novel sampling method designed to address these challenges by preserving the structural integrity, node and edge type distributions, and semantic patterns of IoT-related graphs. HeteroSample works by incorporating the novel top-leader selection, balanced neighborhood expansion, and meta-path guided sampling strategies. The key idea is to leverage the inherent heterogeneous structure and semantic relationships encoded by meta-paths to guide the sampling process. This approach ensures that the resulting subgraphs are representative of the original data while significantly reducing computational overhead. Extensive experiments demonstrate that HeteroSample outperforms state-of-the-art methods, achieving up to 15% higher F1 scores in tasks such as link prediction and node classification, while reducing runtime by 20%.These advantages make HeteroSample a transformative tool for scalable and accurate IoT applications, enabling more effective and efficient analysis of complex IoT systems, ultimately driving advancements in smart cities, industrial IoT, and beyond.

The topological importance of nodes in complex networks has been analyzed in the literature from the perspectives of core-periphery structure and centrality metrics. While the core-periphery structure analysis of a network is more of a qualitative approach (and sometimes quantitative) at a mesoscopic level, centrality metrics are designed to quantify the topological importance of individual nodes in a network. The core-periphery analysis of a network is aimed at categorizing a node as either a core node or a peripheral node. The current status quo in the literature on the definitions of core nodes and peripheral nodes is that the core nodes need to be of larger degree and form a highly dense backbone to which the low degree peripheral nodes are connected to; the peripheral nodes are expected to be not connected to other peripheral nodes as well. Some of the works (e.g., [1-3]) in the literature have suggested that high degree nodes need not always be core nodes; but they still analyze the core-periphery structure and quantify the extent of coreness of a node within the realms of the above model.

Drawing on differing notions of core-periphery structure From a broad perspective, innovation is understood as a form of from [21] and [2], we distinguish decentralized core-periphery, collective problem-solving. For this and other reasons, the process centralized core-periphery, and affinity network structure. We generate of innovation is understood as a social process, as social collectives networks of these three classes from stochastic block models are capable of, and in some cases optimized for, both retaining (SBMs), and use them to run an agent-based model (ABM) of collective the knowledge of previous generations while building upon this cultural innovation, in which agents can only directly interact knowledge for subsequent innovations, a phenemonon we refer to with their network neighbors. In order to discover the highestscoring as "cumulative" culture [18, 19]. Human social networks tend to innovation, agents must discover and combine the highest exhibit core-periphery structures, whereby a'core' population is innovations from two completely parallel technology trees. We find heavily inter-connected, and connected in turn to more'peripheral' that decentralized core-periphery networks outperform both centralized individuals and subcommunities [2]. Prior work on the structure core-periphery networks and affinity networks, in terms of of human networks has suggested that innovation emerges at the mean crossover time for this final innovation. We hypothesize that boundary between the core and periphery of creative networks [4, decentralized core-periphery network structure provides a more 6]. Individual innovators are often in an intermediate position with fruitful environment for collective problem-solving, by allowing many core and peripheral connections, and successfully innovative for the relative shielding of periphery nodes from the optimal innovations teams tend to include both core and peripheral individuals [4].