Complex systems can be more accurately described by higher-order interactions among multiple units. Hypergraphs excel at depicting these interactions, surpassing the binary limitations of traditional graphs. However, retrieving valuable information from hypergraphs is often challenging due to their intricate interconnections. To address this issue, we introduce a new category of structural patterns, hypermotifs, which are defined as statistically significant local structures formed by interconnected hyperedges. We propose a systematic framework for hypermotif extraction. This framework features the encoding, census, and evaluation of higher-order patterns, effectively overcoming their inherent complexity and diversity. Our experimental results demonstrate that hypermotifs can serve as higher-order fingerprints of real-world hypergraphs, helping to identify hypergraph classes based on network functions. These motifs potentially represent preferential attachments and key modules in real-world hypergraphs, arising from specific mechanisms or constraints. Our work validates the efficacy of hypermotifs in exploring hypergraphs, offering a powerful tool for revealing the design principles and underlying dynamics of interacting systems.
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The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain sensitive information. Therefore, the data exchange process raises privacy concerns, especially when the integration of health data from multiple sources (linkage attack) results in further leakage. Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. Furthermore, adversaries launch poisoning attacks to falsify the health data, which leads to misdiagnosing or even physical damage. To protect private health data, we propose a personalized differential privacy model based on the trust levels among users. The trust is evaluated by a defined community density, while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy. To avoid linkage attacks in personalized differential privacy, we design a noise correlation decoupling mechanism using a Markov stochastic process. In addition, we build the community model on a blockchain, which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs. Extensive experiments and analysis on real-world datasets have testified the proposed model, and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.