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

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 inform...

Full description

Bibliographic Details
Main Authors: Ying Xi, Xiaohui Cui
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/25/5/754
_version_ 1827741374031593472
author Ying Xi
Xiaohui Cui
author_facet Ying Xi
Xiaohui Cui
author_sort Ying Xi
collection DOAJ
description 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.
first_indexed 2024-03-11T03:45:44Z
format Article
id doaj.art-6aeef81dc2ff4aa3bd75a3b3edb185b7
institution Directory Open Access Journal
issn 1099-4300
language English
last_indexed 2024-03-11T03:45:44Z
publishDate 2023-05-01
publisher MDPI AG
record_format Article
series Entropy
spelling doaj.art-6aeef81dc2ff4aa3bd75a3b3edb185b72023-11-18T01:15:55ZengMDPI AGEntropy1099-43002023-05-0125575410.3390/e25050754Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship StrengthYing Xi0Xiaohui Cui1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, ChinaIdentifying 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.https://www.mdpi.com/1099-4300/25/5/754complex networksinformation entropyinfluential noderelationship strengthSIR
spellingShingle Ying Xi
Xiaohui Cui
Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength
Entropy
complex networks
information entropy
influential node
relationship strength
SIR
title Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength
title_full Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength
title_fullStr Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength
title_full_unstemmed Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength
title_short Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength
title_sort identifying influential nodes in complex networks based on information entropy and relationship strength
topic complex networks
information entropy
influential node
relationship strength
SIR
url https://www.mdpi.com/1099-4300/25/5/754
work_keys_str_mv AT yingxi identifyinginfluentialnodesincomplexnetworksbasedoninformationentropyandrelationshipstrength
AT xiaohuicui identifyinginfluentialnodesincomplexnetworksbasedoninformationentropyandrelationshipstrength