Estimating complex networks centrality via neural networks and machine learning

Grando, Felipe; Lamb, Luís C.

doi:10.1109/ijcnn.2015.7280334

Cited by 11 publications

(30 citation statements)

References 31 publications

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“…The most prevalent and used centrality measure of this group is the closeness centrality (C C ), first presented by Bavelas [59] and then formally defined by Sabidussi [60]. Closeness centrality is the sum of the geodesics inverse distances from the vertex analyzed to all other vertices (3) [57] [58].…”

Section: Proximity Centralitiesmentioning

confidence: 99%

“…Erdős and Rényi [74] introduced the first model that generates simple random graphs [58]. It defined a fixed number of vertices n and a probability p of connecting each pair of vertices, which also corresponds to the final clustering coefficient of the graph [58]. The higher is the value of p, the higher the mean degree and density, and the lower the diameter of the network [58].…”

Section: Erdős and Rényi Random Graphsmentioning

confidence: 99%

“…In small-world networks, most vertices can be reached within short paths [58]. In addition, these networks show a large number of small cycles, especially of size three [58].…”

Section: Watts and Strogatz Small-world Modelmentioning

confidence: 99%

“…In small-world networks, most vertices can be reached within short paths [58]. In addition, these networks show a large number of small cycles, especially of size three [58]. Watts and Strogatz [72] proposed a model to generate networks with the small world properties.…”

Section: Watts and Strogatz Small-world Modelmentioning

confidence: 99%

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Machine Learning in Network Centrality Measures

2018

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Complex networks are ubiquitous to several Computer Science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore are a robust, effective alternative in real-world applications. The methodology and proposed machine learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.

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Section: Proximity Centralitiesmentioning

confidence: 99%

Section: Erdős and Rényi Random Graphsmentioning

confidence: 99%

“…In small-world networks, most vertices can be reached within short paths [58]. In addition, these networks show a large number of small cycles, especially of size three [58].…”

Section: Watts and Strogatz Small-world Modelmentioning

confidence: 99%

Section: Watts and Strogatz Small-world Modelmentioning

confidence: 99%

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Machine Learning in Network Centrality Measures

2018

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“…Following the results in [37] and [38] we selected a fullyconnected multi-layer perceptron artificial neural network trained by the Levenberg-Marquardt algorithm (LM) [47].…”

Section: B Parameters Configuration and Validationmentioning

confidence: 99%

Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

Grando

Lamb

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks.

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Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

Avelar

Lemos

Prates

et al. 2019

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions

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The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings ∈ R d and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, i.e. is vertex v1 more central than vertex v2 given centrality c?. We then show that a GNN can be trained to develop a lingua franca of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves 89% accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained (n = 128). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.

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Estimating complex networks centrality via neural networks and machine learning

Cited by 11 publications

References 31 publications

Machine Learning in Network Centrality Measures

Machine Learning in Network Centrality Measures

Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

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