Download or read book Kernel Methods for Graph structured Data Analysis written by Zhen Zhang (Electrical engineer) and published by . This book was released on 2019 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structured data modeled as graphs arise in many application domains, such as computer vision, bioinformatics, and sociology. In this dissertation, we focus on three important topics in graph-structured data analysis: graph comparison, graph embeddings, and graph matching, for all of which we propose effective algorithms by making use of kernel functions and the corresponding reproducing kernel Hilbert spaces.For the first topic, we develop effective graph kernels, named as "RetGK," for quantitatively measuring the similarities between graphs. Graph kernels, which are positive definite functions on graphs, are powerful similarity measures, in the sense that they make various kernel-based learning algorithms, for example, clustering, classification, and regression, applicable to structured data. Our graph kernels are obtained by two-step embeddings. In the first step, we represent the graph nodes with numerical vectors in Euclidean spaces. To do this, we revisit the concept of random walks and introduce a new node structural role descriptor, the return probability feature. In the second step, we represent the whole graph with an element in reproducing kernel Hilbert spaces. After that, we can naturally obtain our graph kernels. The advantages of our proposed kernels are that they can effectively exploit various node attributes, while being scalable to large graphs. We conduct extensive graph classification experiments to evaluate our graph kernels. The experimental results show that our graph kernels significantly outperform state-of-the-art approaches in both accuracy and computational efficiency.For the second topic, we develop scalable attributed graph embeddings, named as "SAGE." Graph embeddings are Euclidean vector representations, which encode the attributed and the topological information. With graph embeddings, we can apply all the machine learning algorithms, such as neural networks, regression/classification trees, and generalized linear regression models, to graph-structured data. We also want to highlight that SAGE considers both the edge attributes and node attributes, while RetGK only considers the node attributes. "SAGE" is a extended work of "RetGK," in the sense that it is still based on the return probabilities of random walks and is derived from graph kernels. But "SAGE" uses a totally different strategy, i.e., the "distance to kernel and embeddings" algorithm, to further represent graphs. To involve the edge attributes, we introduce the adjoint graph, which can help convert edge attributes to node attributes. We conduct classification experiments on graphs with both node and edge attributes. "SAGE" achieves the better performances than all previous methods.For the third topic, we develop a new algorithm, named as "KerGM," for graph matching. Typically, graph matching problems can be formulated as two kinds of quadratic assignment problems (QAPs): Koopmans-Beckmann's QAP or Lawler's QAP. In our work, we provide a unifying view for these two problems by introducing new rules for array operations in Hilbert spaces. Consequently, Lawler's QAP can be considered as the Koopmans-Beckmann's alignment between two arrays in reproducing kernel Hilbert spaces, making it possible to efficiently solve the problem without computing a huge affinity matrix. Furthermore, we develop the entropy-regularized Frank-Wolfe algorithm for optimizing QAPs, which has the same convergence rate as the original Frank-Wolfe algorithm while dramatically reducing the computational burden for each outer iteration. Furthermore, we conduct extensive experiments to evaluate our approach, and show that our algorithm has superior performance in both matching accuracy and scalability.

Download or read book Kernels For Structured Data written by Thomas Gartner and published by World Scientific. This book was released on 2008-08-29 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.

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 Graph Kernels written by Karsten Borgwardt and published by . This book was released on 2020-12-22 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

Download or read book From Data and Information Analysis to Knowledge Engineering written by Myra Spiliopoulou and published by Springer Science & Business Media. This book was released on 2006-02-09 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization.

Download or read book ECAI 2020 written by G. De Giacomo and published by IOS Press. This book was released on 2020-09-11 with total page 3122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.

Download or read book Chemoinformatics and Advanced Machine Learning Perspectives Complex Computational Methods and Collaborative Techniques written by Lodhi, Huma and published by IGI Global. This book was released on 2010-07-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks"--Provided by publisher.

Download or read book Comparison of Search based and Kernel based Methods for Graph based Relational Learning written by Chris Manuel Gonsalves and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-based relational learning has been the focus of relational learning for quite some time. As most of the real-world data is structured, and hence cannot be represented in a single table, various logic-based and graph-based techniques have been proposed for dealing with structured data. Our goal is to perform an in-depth analysis of two such graph-based learning systems. We have selected Subdue to represent the search-based approach and support vector machine (SVM) with graph kernels to represent the kernel-based approach. We perform a comparison between search-based and kernel-based approaches and evaluate their performance in various domains. A search-based approach to learning typically involves a search through a larger hypotheses space. The main concern of a search-based learning system is to search through the hypothesis space efficiently. Kernel-based approaches on the other hand do not involve generation and search of a hypotheses space. Instead, a kernel-based system maps the given input space to a higher-dimensional space to perform linear classification. (Abstract shortened by UMI.).

Download or read book Statistical and Machine Learning Approaches for Network Analysis written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-06-26 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Download or read book An Introduction to Lifted Probabilistic Inference written by Guy Van den Broeck and published by MIT Press. This book was released on 2021-08-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.

Download or read book Bridging the Gap Between Graph Edit Distance and Kernel Machines written by Michel Neuhaus and published by World Scientific. This book was released on 2007 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain ? commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.

Download or read book Graph Algorithms for Data Science written by Tomaž Bratanic and published by Simon and Schuster. This book was released on 2024-02-27 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description

Download or read book Kernel Methods in Computational Biology written by Bernhard Schölkopf and published by MIT Press. This book was released on 2004 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed overview of current research in kernel methods and their application to computational biology.

Download or read book Computer Recognition Systems 2 written by Marek Kurzynski and published by Springer Science & Business Media. This book was released on 2007-10-15 with total page 1745 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the results of the 5th International Conference on Computer Recognition Systems CORES’07 held 22-25 October 2007 in Hotel Tumski, Wroclaw, Poland. It brings together original research results in both methodological issues and different application areas of pattern recognition. The contributions cover all topics in pattern recognition including, for example, classification and interpretation of text, video, and voice.

Download or read book Complex Networks and Their Applications VII written by Luca Maria Aiello and published by Springer. This book was released on 2018-12-05 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.