GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case

Shetty, Ramya D; Bhattacharjee, Shrutilipi; Dutta, Animesh; Namtirtha, Amrita

doi:10.1109/tcss.2022.3180177

Cited by 27 publications

(8 citation statements)

References 62 publications

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“…The degree density from each node to v1 is computed according to (10) The output value of v1 for each node is calculated according to (11), and the results are shown in Tab. 4.…”

Section: G=(ve)mentioning

confidence: 99%

“…Scholars also integrate the attributes of nodes and edges to excavate important nodes. New algorithms include identification of nodes influence based on global structure model(GSM) 10 , identification of nodes influence based on Global Structure Influence(GSI) 11 , k-shell based key node recognition method(KBKNR) 12 , influential node identification by aggregating local structure information(ALSI) 1 , and others. GSM calculates the importance of nodes through the K-shell values and shortest paths, and it considers that importance is proportional to the K-shell value and inversely proportional to the length of the shortest path.…”

Section: Introductionmentioning

confidence: 99%

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Excavating Important Nodes in Complex Networks based on the Heat Conduction Model

Zheng

Wang

et al. 2023

Preprint

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Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the SIR model as the evaluation criteria. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

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“…The degree density from each node to v1 is computed according to (10) The output value of v1 for each node is calculated according to (11), and the results are shown in Tab. 4.…”

Section: G=(ve)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Excavating Important Nodes in Complex Networks based on the Heat Conduction Model

Zheng

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Scholars also integrate the attributes of nodes and edges to excavate important nodes. New algorithms include identification of nodes influence based on global structure model (GSM) 10 , identification of nodes influence based on Global Structure Influence(GSI) 11 , k-shell based key node recognition method (KBKNR) 12 , influential node identification by aggregating local structure information (ALSI) 1 , and others. GSM calculates the importance of nodes through the K-shell values and shortest paths, and it considers that importance is proportional to the K-shell value and inversely proportional to the length of the shortest path.…”

Section: Introductionmentioning

confidence: 99%

Excavating important nodes in complex networks based on the heat conduction model

Hu,

Zheng,

et al. 2024

Sci Rep

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Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

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“…These traditional metrics primarily focus on local or global network information [7], [8], frequently failing to capture the delicate interplay between the two. Local influence measurements, such as DC and CC, focus on a node's close connections and proximity to other nodes, elucidating its impact on information or resource flow within a narrow network section [9]- [11]. Global impact metrics, on the other hand, such as BC and PR, take into account the more extensive network structure and the importance of nodes to which a specific node is connected.…”

Section: Introductionmentioning

confidence: 99%

“…These metrics excel at identifying nodes that serve as bridges between distinct network groups or enhance network connectivity across the board. These global measures are limited since they are computationally expensive and do not function well in the absence of a complete network structure [9], [12]- [14]. Evaluating local and global influence is critical to have a complete sense of node relevance.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Global Structure Model for Unraveling Influential Nodes in Complex Networks

Mukhtar¹,

Abas²,

Rasib³

et al. 2023

IJACSA

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In graph analytics, the identification of influential nodes in real-world networks plays a crucial role in understanding network dynamics and enabling various applications. However, traditional centrality metrics often fall short in capturing the interplay between local and global network information. To address this limitation, the Global Structure Model (GSM) and its improved version (IGSM) have been proposed. Nonetheless, these models still lack an adequate representation of path length. This research aims to enhance existing approaches by developing a hybrid model called H-GSM. The H-GSM algorithm integrates the GSM framework with local and global centrality measurements, specifically Degree Centrality (DC) and K-Shell Centrality (KS). By incorporating these additional measures, the H-GSM model strives to improve the accuracy of identifying influential nodes in complex networks. To evaluate the effectiveness of the H-GSM model, real-world datasets are employed, and comparative analyses are conducted against existing techniques. The results demonstrate that the H-GSM model outperforms these techniques, showcasing its enhanced performance in identifying influential nodes. As future research directions, it is proposed to explore different combinations of index styles and centrality measures within the H-GSM framework.

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GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case

Cited by 27 publications

References 62 publications

Excavating Important Nodes in Complex Networks based on the Heat Conduction Model

Excavating Important Nodes in Complex Networks based on the Heat Conduction Model

Excavating important nodes in complex networks based on the heat conduction model

Hybrid Global Structure Model for Unraveling Influential Nodes in Complex Networks

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