Identifying Key Nodes in Complex Networks Based on Local Structural Entropy and Clustering Coefficient

Li, Peng; Wang, Shilin; Guangwu, Chen; Bao, Chengqi; Yan, Guanghui

doi:10.1155/2022/8928765

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Here, we introduce four evaluation criteria to verify the validity of the proposed method. The more detailed information can be found in the literature [ 42 , 43 , 44 , 45 ].…”

Section: Experimental Constructionmentioning

confidence: 99%

The Structure Entropy-Based Node Importance Ranking Method for Graph Data

Liu

2023

Entropy

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Due to its wide application across many disciplines, how to make an efficient ranking for nodes in graph data has become an urgent topic. It is well-known that most classical methods only consider the local structure information of nodes, but ignore the global structure information of graph data. In order to further explore the influence of structure information on node importance, this paper designs a structure entropy-based node importance ranking method. Firstly, the target node and its associated edges are removed from the initial graph data. Next, the structure entropy of graph data can be constructed by considering the local and global structure information at the same time, in which case all nodes can be ranked. The effectiveness of the proposed method was tested by comparing it with five benchmark methods. The experimental results show that the structure entropy-based node importance ranking method performs well on eight real-world datasets.

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“…Here, we introduce four evaluation criteria to verify the validity of the proposed method. The more detailed information can be found in the literature [ 42 , 43 , 44 , 45 ].…”

Section: Experimental Constructionmentioning

confidence: 99%

The Structure Entropy-Based Node Importance Ranking Method for Graph Data

Liu

2023

Entropy

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“…Zhang et al [20] selected four node features and used information entropy for weighting to obtain the final node representation. Previous experiments [19][20][21][22][23] have shown that by using information entropy combined with the centrality of nodes approach, performance is superior to that of a single feature.…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength

小西

Cui

2023

Entropy

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Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.

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