Identification of Important Nodes in Multilayer Heterogeneous Networks Incorporating Multirelational Information

Wan, Liangtian; Zhang, Mingyue; Li, Xiaona; Sun, Lu; Wang, Xianpeng; Liu, Kaihui

doi:10.1109/tcss.2022.3161305

Cited by 19 publications

(6 citation statements)

References 49 publications

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“…Therefore, increasing attention has been paid to heterogeneous networks with multiple node and edge types. Wan et al first divided the heterogeneous network into core layers and auxiliary layers, calculated centrality scores and influence weights of nodes in each layer, and obtained key nodes in the core layer [4]. Soheila et al proposed the Entropy Ranking Method by considering neighbors, meta-path instances, and both combined, using their linear combination as node importance [5].…”

Section: Related Workmentioning

confidence: 99%

“…Given this, more and more researchers are turning to study key nodes identification in heterogeneous networks. Some recent work has tried hierarchical modeling [4] or extracting multiple meta-path instances [5] in heterogeneous networks to measure node importance. However, these methods overlook the varied influence probabilities between different node pairs, leading to unsatisfactory performance in identifying key nodes.…”

Section: Introductionmentioning

confidence: 99%

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Meta-path-based key node identification in heterogeneous networks

Wang,

Shu,

Liu

et al. 2024

Front. Phys.

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Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages meta-paths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Meta-path-based key node identification in heterogeneous networks

Wang,

Shu,

Liu

et al. 2024

Front. Phys.

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“…Multilayer networks are more suitable than single-layer networks for capturing complex interactions in the spatio-temporal dimension (Wan et al, 2022). In the financial field, a bipartite multilayer network focusing on centrality measurement was used for risk monitoring, and the model achieved excellent risk prediction performance by studying the nonlinear correlation of variables (Óskarsd ottir and Bravo, 2021).…”

Section: Studies On Multilayer Network In Crisis Managementmentioning

confidence: 99%

“…Multilayer networks are more suitable than single-layer networks for capturing complex interactions in the spatio-temporal dimension (Wan et al. , 2022).…”

Section: Related Workmentioning

confidence: 99%

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Li,

Xu,

Riaz

et al. 2024

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Purpose This study aims to use geographical information on social media for public opinion risk identification during a crisis. Design/methodology/approach This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs. Findings In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced. Originality/value Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

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“…Wan et al, utilized multiple relationships and global network topology features in complex networks and classified heterogeneous nodes into core and auxiliary layers based on the node type. They quantified the importance of the auxiliary layer by determining the weighted interlayer influence based on different levels of connectivity, thereby measuring the importance of core layer nodes [17]. However, structural holes mainly analyze nodes from the perspective of local information.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

Zeng,

Sun,

Zhang

2024

Entropy

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Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus–subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric’s performance in important node identification is greatly improved, and it demonstrates good network resilience.

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Identification of Important Nodes in Multilayer Heterogeneous Networks Incorporating Multirelational Information

Cited by 19 publications

References 49 publications

Meta-path-based key node identification in heterogeneous networks

Meta-path-based key node identification in heterogeneous networks

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

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