The robustness of intelligent IoT device networking is vital for maintaining communication connectivity within intelligent manufacturing systems, impacting the reliability of the customized Industrial Internet of Things (CuIoT). Current studies enhance network connectivity and resilience against cyber attacks through combinatorial optimization theory by redeploying topologies. However, these approaches often overlook the transformative potential of network motifs in the optimization process. To address this, we introduce CuIoT-MET, an innovative approach that enhances CuIoT robustness by leveraging motif evolutionary transfer knowledge from historical evolution processes. By analyzing changes in connection relationships and emphasizing network motifs' unique contributions, we design a novel robustness metric to optimize the evolutionary trajectory, resulting in more robust CuIoT connection patterns. Extensive experiments show that CuIoT-MET outperforms state-of-the-art methods in improving network robustness.

Web 2.0 tools and environments have made tagging, the act of assigning keywords to on-line objects, a popular way to annotate shared resources. The success of now-prominent tagging systems makes tagging "the natural way for people to classify objects as well as an attractive way to discover new material". One of the most challenging problems is to harvest the semantics from these systems, which can support a number of applications, including tag clustering and tag recommendation. We conduct detailed studies on different types of tag relations and tag similarity measures, and propose a scalable measure that we name Reliability Factor Similarity Measure (RFSM). We compare it with two other measures having similar scalability by integrating them into hierarchical clustering methods and performing tag clustering on a subset of Flickr data. The results suggest that RFSM outperforms those two measures when it is applies for tag clustering purpose. We also present an alternative way of utilizing discovered tag relations to set up tag refining rules in order to deal with some noise in the initial tag sets, which can in turn improve the precision of tag relations.