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19 pages, 1096 KiB

Open AccessArticle

Link Prediction Based on Heterogeneous Social Intimacy and Its Application in Social Influencer Integrated Marketing

by Shugang Li, He Zhu, Zhifang Wen, Jiayi Li, Yuning Zang, Jiayi Zhang, Ziqian Yan and Yanfang Wei

Mathematics 2023, 11(13), 3023; https://doi.org/10.3390/math11133023 - 07 Jul 2023

Cited by 1 | Viewed by 779

Abstract

The social influencer integrated marketing strategy, which builds social influencers through potential users, has gained widespread attention in the industry. Traditional Scoring Link Prediction Algorithms (SLPA) mainly rely on homogeneous network indicators to predict friend relationships, which cannot provide accurate link prediction results [...] Read more.

The social influencer integrated marketing strategy, which builds social influencers through potential users, has gained widespread attention in the industry. Traditional Scoring Link Prediction Algorithms (SLPA) mainly rely on homogeneous network indicators to predict friend relationships, which cannot provide accurate link prediction results in cold-start situations. To overcome these limitations, the Closeness Heterogeneous Link Prediction Algorithm (CHLPA) is proposed, which uses node closeness centrality to describe the social intimacy of nodes and provides a heterogeneous measure of a network based on this. Three types of heterogeneous indicators of social intimacy were proposed based on the principle of three-degree influence. Due to scarce overlapping node sample data, CHLPA uses gradient boosting trees to select the most suitable index, the second most suitable index, and the third most suitable index from Social Intimacy Heterogeneous Indexes (SIHIs) and SLPAs. Then, these indicators are weighted and combined to predict the likelihood of other node users in the two product circles in an online brand community becoming friends with overlapping node users. Finally, a hill-climbing algorithm is designed based on this to build integrated marketing social influencers, and the effectiveness and robustness of the algorithm are validated. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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19 pages, 1552 KiB

Open AccessArticle

Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

by Shugang Li, Hui Chen, Xin Liu, Jiayi Li, Kexin Peng and Ziming Wang

Mathematics 2023, 11(13), 2792; https://doi.org/10.3390/math11132792 - 21 Jun 2023

Cited by 1 | Viewed by 1023

Abstract

To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant [...] Read more.

To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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18 pages, 2114 KiB

Open AccessArticle

Brain Tumor Segmentation Using a Patch-Based Convolutional Neural Network: A Big Data Analysis Approach

by Faizan Ullah, Abdu Salam, Mohammad Abrar and Farhan Amin

Mathematics 2023, 11(7), 1635; https://doi.org/10.3390/math11071635 - 28 Mar 2023

Cited by 11 | Viewed by 2454

Abstract

Early detection of brain tumors is critical to ensure successful treatment, and medical imaging is essential in this process. However, analyzing the large amount of medical data generated from various sources such as magnetic resonance imaging (MRI) has been a challenging task. In [...] Read more.

Early detection of brain tumors is critical to ensure successful treatment, and medical imaging is essential in this process. However, analyzing the large amount of medical data generated from various sources such as magnetic resonance imaging (MRI) has been a challenging task. In this research, we propose a method for early brain tumor segmentation using big data analysis and patch-based convolutional neural networks (PBCNNs). We utilize BraTS 2012–2018 datasets. The data is preprocessed through various steps such as profiling, cleansing, transformation, and enrichment to enhance the quality of the data. The proposed CNN model utilizes a patch-based architecture with global and local layers that allows the model to analyze different parts of the image with varying resolutions. The architecture takes multiple input modalities, such as T1, T2, T2-c, and FLAIR, to improve the accuracy of the segmentation. The performance of the proposed model is evaluated using various metrics, such as accuracy, sensitivity, specificity, Dice similarity coefficient, precision, false positive rate, and true positive rate. Our results indicate that the proposed method outperforms the existing methods and is effective in early brain tumor segmentation. The proposed method can also assist medical professionals in making accurate and timely diagnoses, and thus improve patient outcomes, which is especially critical in the case of brain tumors. This research also emphasizes the importance of big data analysis in medical imaging research and highlights the potential of PBCNN models in this field. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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15 pages, 329 KiB

Open AccessArticle

Notes on the Localization of Generalized Hexagonal Cellular Networks

by Muhammad Azeem, Muhammad Kamran Jamil and Yilun Shang

Mathematics 2023, 11(4), 844; https://doi.org/10.3390/math11040844 - 07 Feb 2023

Cited by 41 | Viewed by 1587

Abstract

The act of accessing the exact location, or position, of a node in a network is known as the localization of a network. In this methodology, the precise location of each node within a network can be made in the terms of certain [...] Read more.

The act of accessing the exact location, or position, of a node in a network is known as the localization of a network. In this methodology, the precise location of each node within a network can be made in the terms of certain chosen nodes in a subset. This subset is known as the locating set and its minimum cardinality is called the locating number of a network. The generalized hexagonal cellular network is a novel structure for the planning and analysis of a network. In this work, we considered conducting the localization of a generalized hexagonal cellular network. Moreover, we determined and proved the exact locating number for this network. Furthermore, in this technique, each node of a generalized hexagonal cellular network can be accessed uniquely. Lastly, we also discussed the generalized version of the locating set and locating number. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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43 pages, 7061 KiB

Open AccessArticle

Covert Network Construction, Disruption, and Resilience: A Survey

by Annamaria Ficara, Francesco Curreri, Giacomo Fiumara, Pasquale De Meo and Antonio Liotta

Mathematics 2022, 10(16), 2929; https://doi.org/10.3390/math10162929 - 14 Aug 2022

Cited by 5 | Viewed by 2573

Abstract

Covert networks refer to criminal organizations that operate outside the boundaries of the law; they can be mainly classified as terrorist networks and criminal networks. We consider how Social Network Analysis (SNA) is used to analyze such networks in order to attain a [...] Read more.

Covert networks refer to criminal organizations that operate outside the boundaries of the law; they can be mainly classified as terrorist networks and criminal networks. We consider how Social Network Analysis (SNA) is used to analyze such networks in order to attain a greater knowledge of criminal behavior. In fact, SNA allows examining the network structure and functioning by computing relevant metrics and parameters to identify roles, positions, features, and other network functioning that are not otherwise easily discovered at first glance. This is why Law Enforcement Agencies (LEAs) are showing growing interest in SNA, which is also used to identify weak spots and disrupt criminal groups. This paper provides a literature review and a classification of methods and real-case applications of disruption techniques. It considers covert network adaptability to such dismantling attempts, herein referred to as resilience. Critical problems of SNA in criminal studies are discussed, including data collection techniques and the inevitable incompleteness and biases of real-world datasets, with the aim of promoting a new research stream for both dismantling techniques and data collection issues. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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23 pages, 1448 KiB

Open AccessArticle

An Orchestration Perspective on Open Innovation between Industry–University: Investigating Its Impact on Collaboration Performance

by Călin Florin Băban and Marius Băban

Mathematics 2022, 10(15), 2672; https://doi.org/10.3390/math10152672 - 29 Jul 2022

Cited by 1 | Viewed by 1742

Abstract

Since open innovation between industry–university is a highly complex phenomenon, its orchestration may be of great support for better collaboration between these organizations. However, there is a lack of evidence on how an orchestration framework impacts the collaboration performance between these organizations in [...] Read more.

Since open innovation between industry–university is a highly complex phenomenon, its orchestration may be of great support for better collaboration between these organizations. However, there is a lack of evidence on how an orchestration framework impacts the collaboration performance between these organizations in such a setting. Based on a research model that investigates the influence of the main orchestration dimensions on the performance of collaboration, this study offers one of the first perspectives of an orchestration process between the industry and university actors in open innovation. The developed research model was assessed using a deep learning dual-stage PLS-SEM and artificial neural network (ANN) analysis. In the first stage, the hypotheses of the research model were tested based on a disjoint two-stage approach of PLS-SEM, and the results reveal the orchestration dimensions that have a significant impact on collaboration performance. In the second stage, a deep learning network approach was successfully employed to capture the complex relationships among the significant orchestration dimensions identified through the PLS-SEM analysis. An importance–performance map analysis provided useful insights into the relative importance of the components of each orchestration dimension based on their effects on the collaboration performance. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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19 pages, 2127 KiB

Open AccessArticle

The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm

by Shugang Li, Ziming Wang, Beiyan Zhang, Boyi Zhu, Zhifang Wen and Zhaoxu Yu

Mathematics 2022, 10(14), 2424; https://doi.org/10.3390/math10142424 - 12 Jul 2022

Cited by 1 | Viewed by 1036

Abstract

One of the main problems encountered by social networks is the cold start problem. The term “cold start problem” refers to the difficulty in predicting new users’ friendships due to the limited number of links those users have with existing nodes. To fill [...] Read more.

One of the main problems encountered by social networks is the cold start problem. The term “cold start problem” refers to the difficulty in predicting new users’ friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using “betweenness centrality,” and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the “products rapidly attracting users” in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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Review

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12 pages, 1088 KiB

Open AccessReview

Neural Network Optimization Based on Complex Network Theory: A Survey

by Daewon Chung and Insoo Sohn

Mathematics 2023, 11(2), 321; https://doi.org/10.3390/math11020321 - 07 Jan 2023

Cited by 4 | Viewed by 2039

Abstract

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can [...] Read more.

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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26 pages, 1300 KiB

Open AccessReview

Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review

by Shugang Li, Fang Liu, Yuqi Zhang, Boyi Zhu, He Zhu and Zhaoxu Yu

Mathematics 2022, 10(19), 3554; https://doi.org/10.3390/math10193554 - 29 Sep 2022

Cited by 9 | Viewed by 4806

Abstract

In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on [...] Read more.

In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research. Full article

(This article belongs to the Special Issue Big Data and Complex Networks)

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