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.

中文翻译：

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基于网络重要性和相似性的核心设计师和团队识别新方法

在产品协同设计过程中，设计师之间的关联可以用复杂的网络来描述。探索此类网络中节点的重要性和信息传播规律，对于区分其核心设计师和潜在设计师团队，以及协同设计任务的准确推荐具有重要意义。基于邻域相似度模型，结合网络信息传播的思想，借助ReLU函数，提出一种判断节点重要性的新方法——LLSR。该方法不仅反映了节点的本地连接特征，还考虑了网络传播的信任度，并利用邻居节点的信息来修改节点值。接下来，为了挖掘潜在团队，提出了一种基于节点重要性和节点相似度的LA-LPA算法。在迭代更新之前，对所有节点进行随机排序，得到更新序列，并用节点重要性序列代替。当传播过程中存在多个最大邻居标签时，选择相似度最高的标签进行更新。在相关网络中的实验结果表明，LLSR算法能够更好地识别网络中的核心节点，而LA-LPA算法大大提高了原有LPA算法的稳定性，稳定地挖掘了网络中的潜在团队。