Download or read book HOW TO USE ANN FOR LINK PREDICTION IN SOCIAL NETWORK written by sneha soni and published by Blue Rose Publishers. This book was released on 2022-07-25 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Networks (SNs) have attracted many users and have become an integrated part of the individual’s daily practices. The rapid climb of SNs like Twitter and Facebook has generated a great deal of knowledge that sets direction for research in social relationships. The knowledge network represented by Facebook is predicated on information transmission, sharing, and exchange. The prediction process from prior information of the event helps to know the evolution of social networks and assists the companies in effective decision making during a typical recommendation system . Social network connection prediction is an efficient technique for the analysis of the evolution of social organizations and formation of the social network relations.Link prediction is a crucial research direction within the field of complex networks and data processing . Some complex physical processes like local stochastic processes also are wont to measure the similarity between network nodes and improve the accuracy of the link prediction . In other words two linked nodes during a network may have a possible relationship. Analyzing whether there's a possible relationship can help to seek out potential links and tightness measures the intensity of the connection. Currently with the rapid development, online social networks have been a neighborhood of people’s life.

Download or read book Hidden Link Prediction in Stochastic Social Networks written by Pandey, Babita and published by IGI Global. This book was released on 2019-05-03 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.

Download or read book Link Prediction in Social Networks written by Srinivas Virinchi and published by Springer. This book was released on 2016-01-22 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.

Download or read book A Novel RNN based Ensemble Model for Link Prediction on Dynamic Social Network written by Chen-Min Su and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networking sites have gained much popularity in the recent years. With millions of people connected virtually generate loads of data to be analyzed to infer meaningful associations among links, arising in different applications ranging from recommendation systems to social networks. Most of the existing methods predict interactions between individuals for static networks, ignoring the dynamic features of social networks. In this paper, we describe the most popular similarity indices and compare their performance in their ability to show links with the highest probability of being added from the initial network. Moreover, we propose a novel RNN-based ensemble model for link prediction on a dynamic social network. We ensemble some similarity indices to obtain a higher performance of link prediction. It applies long short-term memory (LSTM) neural network to predict the possibility of potential links of social network. Finally, we evaluate the link prediction performances of our proposed method and 11 similarity indices with different accuracy measures. ex: AUC, precision, recall, error rate. We also discuss window size influence with AUC. Experimental results show that our method can always find an ensemble with better accuracy than all similarity indices regardless of the dataset.

Download or read book Link Prediction in Social Networks by Neutrosophic Graph written by Rupkumar Mahapatra and published by Infinite Study. This book was released on with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: The computation of link prediction is one of the most important tasks on a social network. Several methods are available in the literature to predict links in networks and RSM index is one of them. The RSM index is applicable in the fuzzy environment and it does not incorporate the notion of falsity and indecency parameters which occur frequently in uncertain environments. In the present method, the behaviors of the common neighbor and the other parameters, like nature of job, location, etc., are considered. In this paper, more parameters are included in the RSM index for making it more flexible and realistic and it is best fitted in the neutrosophic environment. Many important properties are studied for this modified RSM index. A small network from Facebook is considered to illustrate the problem.

Download or read book Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and published by Springer Nature. This book was released on 2019-11-11 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.

Download or read book Prediction and Inference from Social Networks and Social Media written by Jalal Kawash and published by Springer. This book was released on 2017-03-16 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives; they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field.

Download or read book Understanding User Interactions Through Link Analysis in Social Networks written by Mo Yu and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networks exist in many places throughout the world. A typical example of a social network captures a group of human beings and their associated interactions, with vertices representing human beings and links representing human interactions. Most social networks are dynamic, and they grow with both vertices and links. From the perspective of link analysis, link prediction is a fundamental task, because social network growth and development depend heavily on user interactions, and link prediction results can be easily applied to boost user interactions. Also, link prediction has a wide range of applications, such as recommendation systems. In this thesis, our research aims at developing effective link prediction models.In the real world, most social networks are heterogeneous and have various types of links. However, current social network research often treat all links homogeneously. Such a simplification has negative implications for link prediction. Different types of links have different properties. We should be able identify such properties to design distinctive models to predict different links. Also, by identifying link types, we can focus on only those links that are under our interests, and break large social networks into small subnetworks to increase computational efficiency in link prediction. Thus, to facilitate link prediction, and to achieve a deeper understanding of social networks, we also need effective link classification models.To conduct our research for link prediction, we design two recommender systems and test their effectiveness on data from a major U.S. online dating site. Online dating is a fast growing market in recent years, and most sites adopt recommender systems to suggest potential dates. We notice that, for most social networks, new links can be introduced in two ways. First, they can be added when new members join. Second, existing members can establish connections among themselves. As a result, we conduct two distinctive studies. In the first study, we aim to provide reciprocal online dating recommendation for new users. To accomplish this task, we take a hybrid approach. We analyze the preferences of existing users based on their activities, and cluster them into different communities. We then link new users to such communities in a probabilistic way and make recommendations for new users based on activities of communities formed by existing users. Compared with the baseline, our model achieves significant improvements across multiple evaluations. In the second study, we analyze interaction patterns for existing online dating users and design a new collaborative filtering algorithm to make recommendations for them. The algorithm considers both the taste and attractiveness of users. We apply these two considerations to two main design stages of collaborative filtering. When compared against two separate baselines, our algorithm achieves better results in both precision and recall, especially for those reciprocal connections. Because links in online dating networks are homogeneous, we take another dataset for our research of link classification. We conduct a study on a cellphone network, where some of its user pairs are labeled with one of three relationship types. Cellphone networks are some of the largest social networks in the world, and they contain various types of links. To design an effective method of classifying user pairs, we extract three categories of features: network topology, communication, and co-location features. By applying several classification algorithms over these features, we successfully classify three types of links. We also find that communication features are very powerful in identifying family relationship, while co-location features provide best performance in identifying colleague relationships.With this research, we hope to provide some insights about the origin, development, and nature of links in social networks.

Download or read book Social Network Analysis written by Sabrine Mallek and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networks are large structures that depict social linkage between millions of actors. Social network analysis came out as a tool to study and monitor the patterning of such structures. One of the most important challenges in social network analysis is the link prediction problem. Link prediction investigates the potential existence of new associations among unlinked social entities. Most link prediction approaches focus on a single source of information, i.e. network topology (e.g. node neighborhood) assuming social data to be fully trustworthy. Yet, such data are usually noisy, missing and prone to observation errors causing distortions and likely inaccurate results. Thus, this thesis proposes to handle the link prediction problem under uncertainty. First, two new graph-based models for uniplex and multiplex social networks are introduced to address uncertainty in social data. The handled uncertainty appears at the links level and is represented and managed through the belief function theory framework. Next, we present eight link prediction methods using belief functions based on different sources of information in uniplex and multiplex social networks. Our proposals build upon the available information in data about the social network. We combine structural information to social circles information and node attributes along with supervised learning to predict new links. Tests are performed to validate the feasibility and the interest of our link prediction approaches compared to the ones from literature. Obtained results on social data from real-world demonstrate that our proposals are relevant and valid in the link prediction context.

Download or read book Learning Automata Approach for Social Networks written by Alireza Rezvanian and published by Springer. This book was released on 2019-01-22 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.

Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-03 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Download or read book Link Age written by Samet Akcay and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This work extends a previous one that investigated link age and its effect on network evolu- tion. Whether aging adversely influences prediction power of links in network evolution is the fundamental question partially answered in the previous work. Additionally, this study argues whether reliable old connections in a network have a great impact on future link predictions. One of our hypotheses is that aging of a link is a crucial factor in link prediction. The other one is that prediction power of a link usually lessens over time. Using logistic regression and mixture extension, younger links are observed to dominate the link prediction process in most cases. However, this is not always the case. We cannot ignore the links that are old but still powerful. In addition to prediction power of the links, using a mixture model improves the overall link prediction accuracy. The findings of this research support the implications of the previous work that some old and unstable links might be removed from the network.

Download or read book Link Prediction in Social Networks written by and published by . This book was released on 2015 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Trends in Social Network Analysis written by Rokia Missaoui and published by Springer. This book was released on 2017-04-29 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field.

Download or read book A Dynamic Social Network Prediction Based on Convolution and Recurrent Neural Networks written by Shih-Lun Huang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growing popularity of the Internet, an online society has been formed between people and people, which called social network. Through social network interaction, we can understand users' circle of friend-making and recommend which users are suitable to be added as friends. We present social networks graphically, using time-step to fix the interval, and divide it into dynamic network, to observe evolutionary trend between links, then predict which links will appear or disappear from the graph in the future. This is called Dynamic Network Link Prediction (DNLP). We propose a new model based on CNN+LSTM, C3D-LSTM, which predict for dynamic networks. C3D-LSTM combined Conv3D and LSTM. We separate the data in 3D form, and train the dataset, then comparing with GRU, LSTM, Conv2D-LSTM to see which one is better. In the result, C3D-LSTM had the best performance.

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Download or read book Link Prediction Analysis in Social Networks written by A. Chaturvedi and published by . This book was released on 2013-09-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: