Prediction of link evolution using community detection in social network

Kumari, Anisha; Behera, Ranjan Kumar; Sahoo, Bibudatta; Sahoo, Sarmila

doi:10.1007/s00607-021-01035-4

Cited by 21 publications

(4 citation statements)

References 29 publications

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“…Predictive Analysis: The goal of the predictive analysis is to recognize the performance of graphs when come from the same model. As suggested byKumari et al (2022) andPulipati et al (2021) graphs that perform better in predictive tasks may have clear and consistent structure. Therefore, for predictive analysis, we performed both link prediction and community detection.…”

mentioning

confidence: 84%

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hasan,

Bakar,

Yaakub

2024

Preprint

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Social network analysis is a powerful tool for understanding various phenomena, but it requires data with explicit connections among users. However, such data is hard to obtain in real-time, especially from platforms like X, commonly known as Twitter, where users share topic-related content rather than personal connections. Therefore, this paper tackles a new problem of building a social network graph in real-time where explicit connections are unavailable. Our methodology is centred around the concept of user similarity as the fundamental basis for establishing connections, suggesting that users with similar characteristics are more likely to form connections. To implement this concept, we extracted easily accessible attributes from the Twitter platform and proposed a novel graph model based on similarity. We also introduce an Attribute-Weighted Euclidean Distance (AWED) to calculate user similarities. We compare the proposed graph with synthetic graphs based on network properties, online social network characteristics, and predictive analysis. The results suggest that the AWED graph provides a more precise representation of the dynamic connections that exist in real-world online social networks, surpassing the inherent constraints of synthetic graphs. We demonstrate that the proposed method of graph construction is simple, flexible, and effective for network analysis tasks.

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confidence: 84%

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hasan,

Bakar,

Yaakub

2024

Preprint

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“…In a graph G , the degree of any particular node is the total number of edges incident to the vertex 40,43,44 . The degree of vertex v is denoted by deg( v ).…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In a graph G, the degree of any particular node is the total number of edges incident to the vertex. 40,43,44 The degree of vertex v is denoted by deg(v). Degree centrality is the number of links or relationships incident to a node that implies the influence of a particular node in the network.…”

Section: Degree Centrality Algorithmmentioning

confidence: 99%

Hybridizing graph‐based Gaussian mixture model with machine learning for classification of fraudulent transactions

Prusti

Behera

Rath

2022

Computational Intelligence

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It has been observed that a good number of financial organizations often face a number of threats due to credit card fraud that affects consistently to the card holder as well as the organizations. This is one of the fastest-growing frauds of its kind and the most emerging problems for the institutions to prevent. A number of researchers and analysts have shown interest to work on this area in order to identify such issues in an effective manner by applying various supervised as well as unsupervised learning approaches. In this assessment, three classification techniques such as support vector machine (SVM), k-nearest neighbor (k-NN), and extreme learning machine (ELM) that come under supervised learning category are applied to the BankSim data to categorize the normal and fraudulent class transactions in credit card. These algorithms are incorporated with the graph features extracted from the dataset by using a database tool Neo4j. The nodes of the graph represent the transactional data samples and the edges create relationships among the nodes to find the patterns of data using connected data analysis. k-fold cross validation approach in Gaussian mixture model (GMM) has been applied for classification of the credit card transaction data in a single distribution. Further, a combined graph-based Gaussian mixture model 2134

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“…Several ways have been put forth for this issue, with earlier methods relying on unsupervised methods and more modern ones incorporating supervised techniques. Regarding unsupervised techniques, 7 suggested leveraging node proximity and property data, and 8 used a hierarchical network method to forecast missing connections. Contrarily, supervised techniques have included supervised random walk algorithms that use labels to boost the likelihood of traversing established links, 9 while 10,11 extract features from outside sources as well as train methods on them to anticipate link development.…”

Section: Review Of Literaturementioning

confidence: 99%

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Sonti,

Mulpuri Santhi Sri,

Pamulapati

2024

Concurrency and Computation

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SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.

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Prediction of link evolution using community detection in social network

Cited by 21 publications

References 29 publications

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hybridizing graph‐based Gaussian mixture model with machine learning for classification of fraudulent transactions

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

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