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Traffic speed inference enables many applications that are essential for everyday life. Most traffic-prediction approaches assume that a constant number of sensors are deployed on the roads, whether they are either stationary loop detectors or vehicles equipped with Global Positioning System (GPS) tracking devices. The static nature of those fixtures limits their ability to adapt to scenarios that are more dynamic. Rather than relying on several fixed sensors to detect changes and infer traffic, we use crowdsourcing to judiciously select individuals and then make predictions. Our solution consists of three core components: dynamic seed selection, regional cluster building and ensemble traffic prediction. In the first phase, we employ Efficient Transition Probability (ETP) to evaluate candidate seed sets. Road clusters are then formed using hierarchical clustering that is tweaked by a dynamic programming technique. This method assesses the eccentricity of every cluster to bond every road within each cluster more closely. Subsequently, we develop an ensemble-learning strategy in conjunction with Lasso regression to forecast traffic. The strength of our ensemble approach is its ability to manage absent seeds, a condition that has never been investigated, to our knowledge. Substantial experimental evaluation indicates that our claim of dynamic updates is valid and effective. Our solution outperforms the state-of-the-art techniques by a wide margin, in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Community evolution remains a heavily researched and challenging area in the analysis of dynamic complex network structures. Currently, the primary limitation of traditional event-based approaches for community evolution analysis is the lack of strict constraint conditions for distinguishing evolutionary events, which entails that as the cardinality of discovered events increases, so does the number of redundant events. Another limitation of existing approaches is the lack of consideration for weak events. Weak events can be generated by small changes in communities, which are empirically prevalent, and are typically not captured by traditional events. To manage these two aforementioned limitations, this research aims to formalize a weak and strong events-based framework, which includes the following newly discovered events: “weak shrink,” “weak expand,” “weak merge,” and “weak splity” predicated on the community overlapping degree and community degree membership, this article refines these traditional strong events, as well as new constraints for weak events. In addition, a community evolution mining framework, which is based on both strong and weak events, is proposed and denoted by a weak-event-based community evolution method (WECEM). The framework can be summarized by the following: 1) communities in complex networks with adjacent time-stamps are compared to determine the community overlapping degree and community membership degree; 2) the values of the community overlapping degree and membership degree meet the definition of events; and 3) weak events are effectively identified. Extensive experimental results, on real and synthetic data sets consisting of dynamic complex networks and online social networks, demonstrate that WECEM is able to identify weak events more effectively than traditional frameworks. Specifically, WECEM outperforms traditional frameworks by 22.9% in the number of discovered strong events. The detection accuracy of evolutionary events is approximately 12.2% higher than that of traditional event-based frameworks. It is also worth noting that, as the cardinality of the data grows, the proposed framework, when compared with traditional frameworks, can more effectively, and efficiently, mine large-scale complex networks. [ABSTRACT FROM AUTHOR]