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 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 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 Multi block Ae Based LSTM for Social Network written by Jie Chen and published by . This book was released on 2021 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the popularity of social networks, the connection between people on the Internet has become a kind of social network, and these connections will change over time, called dynamic social networks. We present the network as a graphical format. In order to consider its temporal continuity, we divide a series of social networks into dynamic social networks at certain time intervals, and use them to observe the evolution of links to predict which links will appear or disappear in the future. This is called Dynamic Network Link Prediction (DNLP), which means that we can predict a target's future actions or preferences based on historical behaviors. We propose a model based on long and short-term memory (LSTM), called E-TLSTM-D. E-TLSTM-D combines cluster algorithm, auto-encoder and T-LSTM for link prediction of dynamic networks. We compress the clustered data, and then capture the correlation between the time series by T-LSTM. Finally, we reconstruct data and evaluate the similarity between the prediction results and the real data. At the same time, it is able to effectively predict the links that will appear and disappear in the next network graph for large social networks with limited memory.

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 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 Dynamic Social Network Prediction Based on Generative Adversarial Network written by Li-Ling Liu and published by . This book was released on 2020 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of online media, in the online virtual world, the relationship between people has formed a kind of social network, and these relationships will change over time, called dynamic social network. In order to deal the network as a graph and consider its time characteristics, we set the time point as a fixed interval and divide a series of snapshots into a dynamic social network to observe the connection trend of nodes and predict which nodes will appear or disappear in the graph. This process is called "Dynamic Network Link Prediction" (DNLP), which means that we can infer a specific group of people who will establish a relationship with the target user based on their historical behavior. We propose a new model based on Generative Adversarial Network (GAN), called Soc-GAN, combined with Long Short-Term Memory (LSTM), to do the link prediction of dynamic social networks and deal with the long-term prediction tasks for capture the relevance of vectors between the sequences and classify, distinguish whether the generated prediction snapshot is similar to real data. At the same time, it also has a more robust ability to predict the links that are going to appear or disappear in the next network graphs.

Download or read book A Novel Social Network Prediction Based on Virtual Knowledge Distillation written by Pin-Chen Tseng and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advancement of technology, social networks have become a part of people's lives. Through the interaction on social networks, we can make more friends, perhaps with the same hobbies or the same party, or even people with the same values. Due to the dynamic social network that changes every second, it is quite complicated to predict the future. We used C3D-LSTM combined with the concept of knowledge distillation to do prediction task. Distilling knowledge from a complicated teacher model to a relatively easy student model not only saves training time but also saves memory. Finally, we compare our model "KC3D-LSTM" with four baseline models. Our model has the highest Area Under Curve (AUC) which verifies the predictive performance of our model.

Download or read book Link Prediction in Dynamic Networks written by Jeyanthi Salem Narasimhan and published by . This book was released on 2015 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Link Prediction in dynamic networks aims to model the patterns of relationship formation between any two agents in a multi-agent network for predicting the future links. We present three contributions to the state-of-the-art supervised link prediction (SLP) solutions, approaching the problem from three mutually exclusive, nonetheless, related perspectives in dynamic networks.

Download or read book Modeling Evaluation and Analysis of Dynamic Networks for Social Network Analysis written by Ruthwik R. Junuthula and published by . This book was released on 2018 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many application settings involve the analysis of timestamped relations or events between a set of entities, e.g. messages between users of an on-line social network. Dynamic network models are typically used as analysis tools in these settings. They work by either aggregating events over time to form network snapshots, or model the network directly in continuous time. In dynamic network models the common problem researchers deal with is link prediction, which has been studied extensively in the literature, and many methods have been proposed. However On-line social networks (OSNs) often contain different types of relationships between users. When studying the structure of OSNs such as Facebook, two of the most commonly studied networks are friendship and interaction networks. The link prediction problem in friendship networks has been heavily researched. In Interaction networks where links are both added and removed over time, the link prediction or forecasting problem is more complex and involves predicting both newly added and newly removed links. This problem setting creates new challenges in the evaluation of dynamic link prediction methods. In this dissertation, I investigate several metrics currently used for evaluating the accuracy of dynamic link prediction methods and demonstrate why they are inappropriate and misleading in many cases. I provide several recommendations and propose a new metric to characterize link prediction accuracy fairly and effectively using a single number. The link prediction problem in interaction networks is still ongoing and in this dissertation, I study the predictive power of combining friendship and interaction networks. By leveraging friendship networks, I show that I can improve the accuracy of link prediction in interaction networks. I observe that leveraging friendships improves the accuracy of predicting interactions between people that have never interacted before, but has little or no impact on interactions between people who have interacted before. Continuous time network analysis is a relatively new topic of research compared to discrete time analysis. In this dissertation, a block point process model (BPPM) for dynamic networks, which evolves in continuous time in the form of events at irregular time intervals is introduced. It is shown that networks generated by the BPPM follow a stochastic block model (SBM) in the limit of a growing number of nodes and this property is leveraged to develop an efficient inference procedure for the BPPM. The BPPM has been fitted to several real network data sets, using a local search and variational inference approaches, which enable our model to scale to networks several orders of magnitude compared to other existing point process network models.

Download or read book Using Homophily to Analyze and Develop Link Prediction Models with Deep Learning Framework written by Kazi Zainab Khanam and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Twitter is a prominent social networking platform where users' short messages or "tweets" are often used for analysis. However, there has not been much attention paid to mining the medical professions, such as detecting users' occupations from their biographical content. Mining such information can be useful to build recommender systems for cost-effective advertisements. Conventional classifiers can be used to predict medical occupations, but they tend to perform poorly as there are a variety of occupations. As a result, the main focus of the research is to use various deep learning techniques to examine the textual properties of Twitter users' biographic contents, network properties, and the impact of homophily of Twitter users employed in medical professional fields. In Chapter 2, a survey is presented based on the concept of homophily as well as important social network topics that summarize the state of art methods that has been proposed in the past years to identify and measure the effect of homophily in multiple types of social networks. This enables us to find open challenges and directions for future research. In Chapter 3, a model has been developed to identify Twitter users working in medical professional fields by using textual properties of the Twitter Users' bio contents. We have conducted our analysis by annotating the content of Twitter users' bios and propose a method of combining word embedding with state-of-art neural network models. Finally, in Chapter 4, the research introduces a link prediction model based on the homophily concept by using the Twitter users' followers and following IDs identified from Chapter 3. Recent research has centered on analyzing rapidly evolving networks. While predicting links in dynamic networks is difficult, deep learning techniques and network representation learning algorithms, such as Node2vec, have demonstrated significant improvements in prediction accuracy. However, Node2vec's Stochastic Gradient Descent (SGD) approach is prone to falling into a local optimum, and as a consequence, Node2vec fails to capture the network's global structure. To address this problem, we propose NODDLE (integration of NOde2vec anD Deep Learning mEthod), a deep learning system in which we combine Node2vec's features and feed them into a four-layer hidden neural network. integration of NOde2vec anD Deep Learning mEthod (NODDLE) takes advantage of adaptive learning optimizers for improving the performance of link prediction. On different social network datasets, experimental findings show that our approach outperforms conventional methods.

Download or read book Social Network Modeling Link Prediction and Sentiment Impact Analysis written by Baojun Qiu and published by . This book was released on 2011 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Parameterization Schemes written by David J. Stensrud and published by Cambridge University Press. This book was released on 2007-05-03 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contents: 1.

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Download or read book Supervised Sequence Labelling with Recurrent Neural Networks written by Alex Graves and published by Springer. This book was released on 2012-02-06 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.

Download or read book A Wavelet Tour of Signal Processing written by Stephane Mallat and published by Elsevier. This book was released on 1999-09-14 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and École Polytechnique in Paris. Provides a broad perspective on the principles and applications of transient signal processing with wavelets Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection, multifractal analysis, and time-varying frequency measurements Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet Content is accessible on several level of complexity, depending on the individual reader's needs New to the Second Edition Optical flow calculation and video compression algorithms Image models with bounded variation functions Bayes and Minimax theories for signal estimation 200 pages rewritten and most illustrations redrawn More problems and topics for a graduate course in wavelet signal processing, in engineering and applied mathematics

Download or read book Soft Computing for Problem Solving 2019 written by Atulya K. Nagar and published by Springer Nature. This book was released on 2020-04-04 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features the outcomes of the 9th International Conference on Soft Computing for Problem Solving, SocProS 2019, which brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to identify potential future directions. The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in areas such as algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that cannot easily be solved using traditional methods.