A New Method of Identifying Core Designers and Teams Based on the Importance and Similarity of Networks

Abstract

In the process of product collaborative design, the association between designers can be described by a complex network. Exploring the importance of the nodes and the rules of information dissemination in such networks is of great significance for distinguishing its core designers and potential designer teams, as well as for accurate recommendations of collaborative design tasks. Based on the neighborhood similarity model, combined with the idea of network information propagation, and with the help of the ReLU function, this paper proposes a new method for judging the importance of nodes—LLSR. This method not only reflects the local connection characteristics of nodes but also considers the trust degree of network propagation, and the neighbor nodes’ information is used to modify the node value. Next, in order to explore potential teams, an LA-LPA algorithm based on node importance and node similarity was proposed. Before the iterative update, all nodes were randomly sorted to get an update sequence which was replaced by the node importance sequence. When there are multiple largest neighbor labels in the propagation process, the label with the highest similarity is selected for update. The experimental results in the related networks show that the LLSR algorithm can better identify the core nodes in the network, and the LA-LPA algorithm has greatly improved the stability of the original LPA algorithm and has stably mined potential teams in the network.

Application and Research of the Image Segmentation Algorithm in Remote Sensing Image Buildings

“…After each convolution operation, normalization, and in the activation operation, in order to avoid the phenomenon of neuron necrosis in ReLU [23][24][25][26][27][28], GELU is also selected as the activation function, and then the standard convolution of 3 × 3 is used to convert the number of feature map channels to 1, and then the obtained feature map is compared with the input image of this module. Fusion is performed to obtain the preliminary information of the prediction module, and finally, the fused feature map is classified by the Sigmoid function to obtain the final segmentation result map [29][30][31][32][33]. Compared with the original module, the improved edge correction module proposed in this paper has a deeper structure, and the extracted image features are richer.…”

Research on the Model of Industrial Interconnection Intelligent Manufacturing Supply and Demand Network and Its Robustness

“…Liu et al [19] considered the information dissemination factors in the network to further improve the LLS algorithm:…”