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.
Highlights
In the real world, the identification of important nodes is related to the network structure and related to factors such as trust in network propagation
Due to the random selection process in LPA, it is mainly manifested in the order of node label update and the large randomness in the label propagation process, which may lead to a large community, which does not accord with the actual situation. erefore, many scholars have proposed improved algorithms
(2) Aiming at the problem of updating node labels by randomizing the order of nodes in the LPA algorithm and the problem of randomly selecting labels when multiple maximum neighbor labels appear during updating labels, we propose LA-LPA algorithm based on node importance LLSR and node similarity Adamic-Adar, and node labels are updated with the node importance ranking obtained by LLSR algorithm; when multiple maximum neighbor labels appear during updating the label, the Adamic-Adar coefficient is introduced into the LPA algorithm, and the node label with the highest Adamic-Adar similarity is selected
Summary
2.1. eoretical Basis of Node Importance (1) Degree Centrality. Degree centrality refers to the number of connected edges of a node vi, that is, the number of neighbor nodes of the node vi, and its calculation equation is as follows: N. H-index centrality determines the importance of a node by considering the degree of the node itself and the degree of neighboring nodes:. LPA mainly uses the propagation characteristics of the network to affect the label of each node, so as to detect the community structure in the network, but the algorithm has instability and randomness. All nodes in the network are assigned a label, and the label of the node represents the community in which it is located (2) Label Propagation. When the labels of all nodes remain unchanged or the set number of iterations is reached, the algorithm stops (4) Community Detection. E LPA algorithm has linear time complexity and does not need to set the number of communities in advance and is suitable for community detection in large-scale networks. If the label of node 5 is selected by node 6, the labels of all 4 nodes in the lower part are a. is led to the annexation of the following communities, and the entire network eventually became a community
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