Addressing the challenges of low detection precision and excessive parameter volume presented by the high resolution, significant scale variations, and complex backgrounds in UAV aerial imagery, this paper introduces MFP-YOLO, a lightweight detection algorithm based on YOLOv5s. Initially, a multipath inverse residual module is designed, and an attention mechanism is incorporated to manage the issues associated with significant scale variations and abundant interference from complex backgrounds. Then, parallel deconvolutional spatial pyramid pooling is employed to extract scale-specific information, enhancing multi-scale target detection. Furthermore, the Focal-EIoU loss function is utilized to augment the model’s focus on high-quality samples, consequently improving training stability and detection accuracy. Finally, a lightweight decoupled head replaces the original model’s detection head, accelerating network convergence speed and enhancing detection precision. Experimental results demonstrate that MFP-YOLO improved the mAP50 on the VisDrone 2019 validation and test sets by 12.9% and 8.0%, respectively, compared to the original YOLOv5s. At the same time, the model’s parameter volume and weight size were reduced by 79.2% and 73.7%, respectively, indicating that MFP-YOLO outperforms other mainstream algorithms in UAV aerial imagery detection tasks.

One key challenge within the domain of network science is accurately finding important nodes within a network. In recent years, researchers have proposed various node centrality indicators from different perspectives. However, many existing methods have their limitations. For instance, certain approaches lack a balance between time efficiency and accuracy, while the majority of research neglects the significance of local clustering coefficients, a crucial node property. Thus, this paper introduces a centrality metric called DNC (degree and neighborhood information centrality) that considers both node degree and local clustering coefficients. The combination of these two aspects provides DNC with the ability to create a more comprehensive measure of nodes’ local centrality. In addition, in order to obtain better performance in different networks, this paper sets a tunable parameter $\alpha$ to control the effect of neighbor information on the importance of nodes. Subsequently, the paper proceeds with a sequence of experiments, including connectivity tests, to validate the efficacy of DNC. The results of the experiments demonstrate that DNC captures more information and outperforms the other eight centrality metrics.

The identification of important nodes in complex networks has always been a prominent topic in the field of network science. Nowadays, the emergence of large-scale networks has sparked our research interest in complex network centrality methods that balance accuracy and efficiency. Therefore, this paper proposes a novel centrality method called Spon (Sum of the Proportion of Neighbors) Centrality, which combines algorithmic efficiency and accuracy. Spon only requires information within the three-hop neighborhood of a node to assess its centrality, thereby exhibiting lower time complexity and suitability for large-scale networks. To evaluate the performance of Spon, we conducted connectivity tests on 16 empirical unweighted networks and compared the monotonicity and algorithmic efficiency of Spon with other methods. Experimental results demonstrate that Spon achieves both accuracy and algorithmic efficiency, outperforming eight other methods, including CycleRatio, collective influence, and Social Capital. Additionally, we present a method called W-Spon to extend Spon to weighted networks. Comparative experimental results on 10 empirical weighted networks illustrate that W-Spon also possesses advantages compared to methods such as I-Core and M-Core.

Mining key nodes in complex networks has always been a promising research direction in the field of complex networks. Many precise methods proposed by researchers for mining influential special nodes in networks have been widely applied in a plethora of fields. However, some important node-mining methods often use the degree as a node attribute indicator for evaluating node importance, while the clustering coefficient, as an important attribute of nodes, is rarely utilized. Some methods only consider the global position of nodes in the network while ignoring the local structural information of nodes in special positions and the network. Hence, this paper introduces a novel node centrality method, KCH. The KCH method leverages K-shell to identify the global position of nodes and assists in evaluating the importance of nodes by combining information such as structural holes and local clustering coefficients of first-order neighborhoods. This integrated approach yields an enhanced performance compared to existing methods. We conducted experiments on connectivity, monotonicity, and zero models on 10 networks to evaluate the performance of KCH. The experiments revealed that when compared to the collective influence baseline methods, such as social capital and hierarchical K-shell, the KCH method exhibited superior capabilities in terms of collective influence.