Enhancing attributed network embedding via enriched attribute representations

Kakisim, Arzu Gorgulu

doi:10.1007/s10489-021-02498-w

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…At the core of these algorithms is the decision tree, a hierarchical structure designed to partition a data set with numerous records into smaller subsets through a series of decision rules. Essentially, a decision tree is a tool that, through sequential decision steps, segments substantial data sets into more manageable groups of records [27].…”

Section: Decision Treementioning

confidence: 99%

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Sentiment analysis of coronavirus data with ensemble and machine learning methods

Başarslan,

Kayaalp

2024

Turkish Journal of Engineering

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The coronavirus pandemic has distanced people from social life and increased the use of social media. People's emotions can be determined with text data collected from social media applications. This is used in many fields, especially in commerce. This study aims to predict people's sentiments about the pandemic by applying sentiment analysis to Twitter tweets about the pandemic using single machine learning classifiers (Decision Tree-DT, K-Nearest Neighbor-KNN, Logistic Regression-LR, Naïve Bayes-NB, Random Forest-RF) and ensemble learning methods (Majority Voting (MV), Probabilistic Voting (PV), and Stacking (STCK)). After vectorizing the tweets using two predictive methods, Word2Vec (W2V) and Doc2Vec, and two traditional word representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BOW), classification models built using single machine learning classifiers were compared to models built using ensemble learning methods (MV, PV and STCK) by heterogeneously combining single machine classifier algorithms. Accuracy (ACC), F-measure (F), precision (P), and recall (R) were used as performance measures, with training/test separation rates of 70%-30% and 80%-20%, respectively. Among these models, the ACC of ensemble learning models ranged from 89% to 73%, while the ACC of single classifier models ranged from 60% to 80%. Among the ensemble learning methods, STCK with Doc2Vec text representation/embedding method gave the best ACC result of 89%. According to the experimental results, ensemble models built with heterogeneous machine learning classifier algorithms gave better results than single machine learning classifier algorithms.

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Section: Decision Treementioning

confidence: 99%

“…ACC is the ratio of the true negative (𝑇 𝑁 ) and true positive (𝑇 𝑃 ) fields correctly predicted by the model to the sum of the false negative (𝐹 𝑁 ), and false positive (𝐹 𝑃 ) values contained in these fields. The ACC value is given by Equation ( 7) [27].…”

Section: Performance Metricsmentioning

confidence: 99%

Sentiment analysis of coronavirus data with ensemble and machine learning methods

Başarslan,

Kayaalp

2024

Turkish Journal of Engineering

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“…Machine learning is a revolutionary approach to improving the data analysis and learning capabilities of computers. Thanks to machine learning, solving complex problems has become more accessible and effective [35]. Information about the machine learning methods used in the study is given below.…”

Section: Machine Learningmentioning

confidence: 99%

A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Başarslan,

ÜNAL,

KAYAALP

2023

Preprint

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Customer churn is an important issue in increasing both the long and short-term revenues of a company. If companies identify customers’ churn behavior, they can prevent customer loss, ensure customer loyalty, and in turn gain better financial returns. The telecommunication sector is a customer-oriented sector that requires customer retention to survive in the market and, in this sector customer loss is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customers’ churn behavior. In this study, a customer churn analysis was conducted with publicly shared Telco telecom data. Predictive models were built with machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Voting), and deep learning (LSTM) methods. Besides, a 3-layered LSTM model was proposed. Accuracy, F1, precision, and recall rates were used to evaluate the models. As a result, the novel 3-layered LSTM model achieved 91% accuracy, 87% precision, 84% recall, and 89% F1 values. The proposed model is competitive with the literature.

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“…Researchers have gradually focused on expressing network nodes with low-latitude, high-density spatial vectors, thereby maintaining the structure and feature information of the original network. Learned feature vectors are represented such as by graph-based classification, clustering, and link prediction [9][10][11]. As a research direction of graph neural networks, graph embedding has also attracted attention [12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation

Zhang

et al. 2022

Mathematics

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The node embedding method enables network structure feature learning and representation for social network community detection. However, the traditional node embedding method only focuses on a node’s individual feature representation and ignores the global topological feature representation of the network. Traditional community detection methods cannot use the static node vector from the traditional node embedding method to calculate the dynamic features of the topological structure. In this study, an incremental dynamic community detection model based on a graph neural network node embedding representation is proposed, comprising the following aspects. A node embedding model based on influence random walk improves the information enrichment of the node feature vector representation, which improves the performance of the initial static community detection, whose results are used as the original structure of dynamic community detection. By combining a cohesion coefficient and ordinary modularity, a new modularity calculation method is proposed that uses an incremental training method to obtain node vector representation to detect a dynamic community from the perspectives of coarse- and fine-grained adjustments. A performance analysis based on two dynamic network datasets shows that the proposed method performs better than benchmark algorithms based on time complexity, community detection accuracy, and other indicators.

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Enhancing attributed network embedding via enriched attribute representations

Cited by 8 publications

References 20 publications

Sentiment analysis of coronavirus data with ensemble and machine learning methods

Sentiment analysis of coronavirus data with ensemble and machine learning methods

A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation

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