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 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 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 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 Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

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 Big Data Analytics written by Sanjay Madria and published by Springer Nature. This book was released on 2019-12-12 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Big Data analytics, BDA 2019, held in Ahmedabad, India, in December 2019. The 25 papers presented in this volume were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; search and information extraction; predictive analytics in medical and agricultural domains; graph analytics; pattern mining; and machine learning.

Download or read book 2015 IEEE International Conference on Smart City SocialCom SustainCom SmartCity written by IEEE Staff and published by . This book was released on 2015-12-19 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart Transportation

Download or read book Machine Learning and Knowledge Discovery in Databases written by Frank Hutter and published by Springer Nature. This book was released on 2021-02-24 with total page 783 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Download or read book Deep Learning on Graphs written by Yao Ma and published by Cambridge University Press. This book was released on 2021-09-23 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

Download or read book Introduction to Graph Neural Networks written by Zhiyuan Zhiyuan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Download or read book Beyond the Hype written by Anthony Hernandez and published by . This book was released on 2020 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in neural network-based machine learning algorithms promise a rev-olution in prediction tasks across a variety of domains. Of these, forecasting user activity insocial media is particularly relevant for problems such as modeling and predicting informa-tion diffusion and designing intervention techniques to mitigate disinformation campaigns.Another potential task is anonymizing social network datasets to facilitate their distributionand promote research. Given the success of deep generative models, it may be possible touse them for anonymization. Social media seems an ideal context for applying neural net-work techniques, as they provide large data sets and challenging prediction objectives. Yet,our experiments find a number of limitations in the power of deep neural networks and tra-ditional machine learning approaches in predicting user activity on social media platformsas well as creating anonymized networks. Two studies are conducted in this work. Thefirst describes whether a Generative Adversarial Network could produce slightly dissimilarattributed graphs from an original which may implicitly anonymize it. We find issues inhow the graph is assembled and how the generator learns attributes for nodes. The secondstudy describes the challenges we encountered while attempting to forecast user activity ontwo popular social interaction sites: Twitter and GitHub. The custom sequence-to-sequencearchitecture that is used suffers limitations related to dataset characteristics, specificallytemporal aspects of user behavior.

Download or read book MDATA A New Knowledge Representation Model written by Yan Jia and published by Springer Nature. This book was released on 2021-03-06 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge representation is an important task in understanding how humans think and learn. Although many representation models or cognitive models have been proposed, such as expert systems or knowledge graphs, they cannot represent procedural knowledge, i.e., dynamic knowledge, in an efficient way. This book introduces a new knowledge representation model called MDATA (Multi-dimensional Data Association and inTelligent Analysis). By modifying the representation of entities and relations in knowledge graphs, dynamic knowledge can be efficiently described with temporal and spatial characteristics. The MDATA model can be regarded as a high-level temporal and spatial knowledge graph model, which has strong capabilities for knowledge representation. This book introduces some key technologies in the MDATA model, such as entity recognition, relation extraction, entity alignment, and knowledge reasoning with spatiotemporal factors. The MDATA model can be applied in many critical applications and this book introduces some typical examples, such as network attack detection, social network analysis, and epidemic assessment. The MDATA model should be of interest to readers from many research fields, such as database, cyberspace security, and social network, as the need for the knowledge representation arises naturally in many practical scenarios.

Download or read book Bioinformatics and Biomedical Engineering written by Ignacio Rojas and published by Springer Nature. This book was released on 2023-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the 10th International Work-Conference on IWBBIO 2023, held in Meloneras, Gran Canaria, Spain, during July 12-14, 2022. The total of 79 papers presented in the proceedings, was carefully reviewed and selected from 209 submissions. The papers cove the latest ideas and realizations in the foundations, theory, models, and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, biology, bioinformatics, and biomedicine.

Download or read book Big Data and Social Computing written by Xiaofeng Meng and published by Springer Nature. This book was released on with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

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.