Link prediction in complex networks using node centrality and light gradient boosting machine

Kumar, Sanjay; Mallik, Abhishek; Panda, B. S.

doi:10.1007/s11280-021-01000-3

Cited by 34 publications

(18 citation statements)

References 43 publications

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“…We use the Random Forest classifier [35,39,53] in the main text. Similar results by Gradient Boosting [42,60] and AdaBoost [39,61] are presented in Supplementary Information S6. The classifier finds a mapping function y = f (x) to transform the input x to the score y for prediction, with the aim to further improve the prediction performance.…”

Section: Resultssupporting

confidence: 77%

“…The prediction performance is gauged by the extent to which L P outscores L N . One measure most commonly applied is AUC [39][40][41][42][43]. The AUC can be calculated by a sampling method [34,48,53,55].…”

Section: Resultsmentioning

confidence: 99%

“…One topological feature can be quantified through multiple indexes, giving rise to different index values. The obtained index value can be directly used for prediction, corresponding to an unsupervised heuristic approach [33][34][35][36][37], or further processed by a machine learning algorithm for a supervised prediction [38][39][40][41][42]. The prediction outcome can be gauged by different measures such as AUC and precision [30,31,[39][40][41][42][43][44][45].…”

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confidence: 99%

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The maximum capability of a topological feature in link prediction

Ran¹,

Xu²,

Jia³

2022

Preprint

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Link prediction aims to predict links of a network that are not directly visible, with profound applications in biological and social systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a particular feature can be leveraged to infer missing links.Here, we show that the maximum capability of a topological feature follows a simple mathematical expression, which is independent of how an index gauges the feature. Hence, a family of indexes associated with one topological feature shares the same performance limit. A feature's capability is lifted in the supervised prediction, which in general gives rise to better results compared with unsupervised prediction. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks, which can be applied to feature selection and the analysis of network characteristics associated with a topological feature in link prediction.

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Section: Resultssupporting

confidence: 77%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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The maximum capability of a topological feature in link prediction

Ran¹,

Xu²,

Jia³

2022

Preprint

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“…Light Gradient Boosting Machine, is a robust and effective gradient boosting classifier. The purpose of this classifier is to facilitate the training and optimization of gradient boosting models, a well-established machine learning model recognized for their exceptional predictive capabilities across a range of applications, including classification, regression, and ranking [32]. Equation (1) shows the mathematical operation of LGBM.…”

Section: Light Gradient Boosting Machine (Lgbm)mentioning

confidence: 99%

Comparative performance of machine learning models for the classification of human gait

Thakur,

Lalwani

2024

Biomed. Phys. Eng. Express

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The efficacy of human activity recognition (HAR) models mostly relies on the characteristicsderived from domain expertise. The input of the classification algorithmconsists of many characteristics that are utilized to accurately and effectively classifyhuman physical activities. In contemporary research, machine learning techniqueshave been increasingly employed to automatically extract characteristics from unprocessedsensory input to develop models for Human Activity Recognition (HAR)and classify various activities. The primary objective of this research is to compareand contrast several machine learning models and determine a reliable and preciseclassification model for classifying activities. This study does a comparison analysisin order to assess the efficacy of 10 distinct machine learning models using frequentlyused datasets in the field of HAR. In this work, three benchmark public human walkingdatasets are being used. The research is conducted based on eight evaluatingparameters. Based on the study conducted, it was seen that the machine learningclassification models Random Forest, Extra Tree, and Light Gradient Boosting Machinehad superior performance in all the eight evaluating parameters compared tospecific datasets. Consequently, it can be inferred that machine learning significantlyenhances performance within the area of Human Activity Recognition (HAR). Thisstudy can be utilized to provide suitable model selection for HAR-based datasets.Furthermore, this research can be utilized to facilitate the identification of variouswalking patterns for bipedal robotic systems.

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“…Light gradient boosting machine, (LGBM) classifier is based on decision trees to increases the efficiency of the model and reduces memory usage. It is described in [57].…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Retweet Prediction based on Heterogeneous Data Sources: The Combination of Text and Multilayer Network Features

Meštrović¹,

Petrović²,

Beliga³

2022

Preprint

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Retweet prediction is an important task related to different problems such as information spreading analysis, the automatic detection of fake news, social media monitoring, etc. In this study we explore the possibilities of retweet prediction based on heterogeneous data sources. In order to classify the tweet according to the amount of retweets, we combine features extracted from the multilayer network and the text. More specifically, we introduce a multilayer framework that proposes the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships as well as other key properties of communication on Twitter. We select a set of local network measures from each layer and construct a set of multilayer network features. In addition, we adopt a BERT-based language model, namely Cro-CoV-cseBERT to capture high-level semantics and structure of tweets as a set of text features. Then, we train six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category embedding model, neural oblivious decision ensembles and attentive interpretable tabular learning model in the task of retweet prediction. We compare the performance of all six algorithms in three different setups (i) using only text features, (ii) using only multilayer network features and (iii) using both sets of features. We evaluate all setups in terms of standard evaluation measures i.e. precision, recall, F1-score and accuracy. For this task, we first prepare and use an empirical dataset of 199,431 tweets in the Croatian language posted during the period between January 1, 2020 and May 31, 2021. Our results indicate that by integrating multilayer network features with text features the prediction model would perform better than using just one set of features.

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Link prediction in complex networks using node centrality and light gradient boosting machine

Cited by 34 publications

References 43 publications

The maximum capability of a topological feature in link prediction

The maximum capability of a topological feature in link prediction

Comparative performance of machine learning models for the classification of human gait

Retweet Prediction based on Heterogeneous Data Sources: The Combination of Text and Multilayer Network Features

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