Approximating Network Centrality Measures Using Node Embedding and Machine Learning

Mendonça, Matheus R. F.; Barreto, André; Ziviani, Artur

doi:10.1109/tnse.2020.3035352

Cited by 25 publications

(24 citation statements)

References 26 publications

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“…Local-based centrality will be less affected by network structure uncertainty than path-based centrality. Recent approaches have examined the robustness of centrality measures [133][134][135][136] and their calculation in dynamic graphs [137][138][139]. There are also evidence showing for network functions like synchronization, controllability, communication and spreading information [116].…”

Section: Discussionmentioning

confidence: 99%

A Mini Review of Node Centrality Metrics in Biological Networks

Wang

Zheng

2022

IJNDI

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Survey/review study A Mini Review of Node Centrality Metrics in Biological Networks Mengyuan Wang 1,2, Haiying Wang 1, and Huiru Zheng 1,* 1 School of Computing, Ulster University, Belfast, BT15 1ED, United Kingdom 2 Scotland’s Rural College, Edinburgh, EH25 9RG, United Kingdom * Correspondence: h.zheng@ulster.ac.uk Received: 31 October 2022 Accepted: 21 November 2022 Published: 22 December 2022 Abstract: The diversity of nodes in a complex network causes each node to have varying significance, and the important nodes often have a significant impact on the structure and function of the network. Although the interpretation of the results of biological networks must always depend on the topological study of nodes, there is presently no consensus on how to use these metrics, and most network analyses always result in a basic interpretation of a limited number of metrics. To thoroughly comprehend biological networks, it is necessary to consistently understand the notion of node centrality. Therefore, for 10 typical nodal metrics in biological networks, the study first assesses their current applications, advantages, disadvantages as well as potential applications. Then, a review of previous studies is provided, and suggestions are made correspondingly for the purpose of improving biological topology algorithms. Finally, the following recommendations are made in this study: (1) a comprehensive and accurate assessment of node centrality necessitates the use of multiple metrics, including both the target node and its surroundings, and density of maximum neighbourhood component(DMNC) can be used as a complement to other node centrality metrics; (2) different centrality metrics can be applied to identify nodes with different functions, which in this study are mapped as modular surroundings, bridging roles, and susceptibility; and (3) the following groups of node centrality can often be verified against each other, including degree and maximum neighbourhood component (MNC), eccentricity, closeness and radiality; stress and betweenness.

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Section: Discussionmentioning

confidence: 99%

A Mini Review of Node Centrality Metrics in Biological Networks

Wang

Zheng

2022

IJNDI

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“…Concerning the constant increasing size of network data, the calculation of some structural properties, such as node centrality, has a high computational cost. Some algorithms provide approximation solutions using sampling and calculate the single-source shortest path for a given sample of nodes [4,24]. Even though the accuracy of these algorithms is acceptable, the computational cost is still difficult to manage [12].…”

Section: Centralitymentioning

confidence: 99%

Validating Vector-Label Propagation for Graph Embedding

Bellandi

Damiani²,

Ghirimoldi³

et al. 2022

Lecture Notes in Computer Science

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Structural network analysis retrieves the holistic patterns of interactions among network instances. Due to the unprecedented growth of data availability, it is time to take advantage of Machine Learning to integrate the outcome of the structural analysis with better predictions on the upcoming states of large networks. Concerning the existing challenges of adopting methods embracing multi-dimensional, multi-task, transparent representations within incremental procedures, in our recent study, we proposed the AVPRA algorithm. It works as an embedder of both the network structure and domain-specific features making the aforementioned challenges feasible to address. In this paper, we elaborate on the validation of AVPRA by adopting it in multiple downstream Machine Learning tasks on the Twitter network of the Italian Parliament. Comparing the outcome with state-of-the-art algorithms of graph embedding, the capability of AVPRA in retaining either network structure properties or domain-specific features of the nodes is promising. In addition, the method is incremental and transparent.

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“…The degree of the node can always be used to characterize the network structure around the node. Next, we may consider the significance and position of the node using the concept of node centrality measures [13]. Then, we'll discuss defining the local network structure [23].…”

Section: Node Featuresmentioning

confidence: 99%

Machine Learning Methods with Node-Level Features

-¹

2022

IJFMR

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In this paper we describe several methods for obtaining node features. They can also be grouped based on structure-based features, which again, do not require degrees, such as the simplest one, and which count edges, clusters, and triangles, as well as significance features like node degree and various centrality measures. A generalization that counts additional structures in which a particular node of interest participates is the Graphlet Degree Vector (GDV).

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Approximating Network Centrality Measures Using Node Embedding and Machine Learning

Cited by 25 publications

References 26 publications

A Mini Review of Node Centrality Metrics in Biological Networks

A Mini Review of Node Centrality Metrics in Biological Networks

Validating Vector-Label Propagation for Graph Embedding

Machine Learning Methods with Node-Level Features

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