Download or read book Scalable Community Detection in Massive Networks Using Aggregated Relational Data written by Timothy Jones and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Our inference method converges faster than existing methods by leveraging nodal information that often accompany real world networks. Conditioning on this extra information leads to a model that admits a parallel variational inference algorithm. We apply our method to a citation network with over three million nodes and 25 million edges. Our method converges faster than existing posterior inference algorithms for the MMSB and recovers parameters better on simulated networks generated according to the MMSB.

Download or read book Machine Learning Methods for Community Detection in Networks Using Known Community Information written by Meghana Venkata Palukuri and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a network, the problem of community detection refers to finding groups of nodes and edges that form ‘communities’ relevant to the field, such as groups of people with common interests in social networks and fraudulent websites linked to each other on the web. Community detection also yields downstream use-cases such as the summarization of massive networks into smaller networks of communities. We are most interested in mining protein complexes, i.e., communities of interacting proteins, accelerating biological experiments by providing candidates for previously unknown protein complexes. Characterization of protein complexes is important, as they play essential roles in cellular functions and their disruption often leads to disease. Previous methods in community detection comprise a majority of unsupervised graph clustering strategies, which work on the assumption that communities are dense subgraphs in a network - which is not always true. Also, many community detection algorithms are in-memory and serial and do not scale to large networks. In this dissertation, we use knowledge from communities, including rich features from graph nodes, with supervised and reinforcement learning, improving on accuracies, with parallel algorithms ensuring high performance and scalability. Specifically, we work on (1) learning a community fitness function using supervised machine learning methods with AutoML; (2) a distributed algorithm for finding candidate communities using multiple heuristics; (3) learning to walk trajectories on a network leading to communities with reinforcement learning and (4) feature augmentation with graph node information, such as images and additional graph node embeddings. While we optimize our algorithms on protein complexes that have characteristics such as being overlapping in nature with different topologies, our methods are generalizable to other domains since they learn and use characteristics of communities to predict new communities. Further, in domains with limited known information, the algorithms we develop can be applied by transferring learned knowledge such as dense community fitness functions from other domains. In conclusion, we build Super.Complex, RL complex detection, and DeepSLICEM - three accurate, efficient, scalable, and generalizable community detection algorithms, that effectively utilize known community information with different machine learning methods and also present 3 evaluation measures for accurate community evaluation

Download or read book Scalable Community Detection for Social Networks written by Arnau Prat Pérez and published by . This book was released on 2016 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many applications can be modeled intuitively as graphs, where nodes represent the entities and the edges the relationships between them. This way, we are able to better understand them and how they interact. One particularity of these graphs is that their entities are organized in modules called communities. A community is informally defined as a set of nodes more densely connected internally than externally. For instance, in the case of a social network, persons with similar characteristics are grouped forming communities. Community detection has become a hot topic in the research community during the last years, due to its amount of applications. For instance, in social networks, communities give information about the persons forming them, by just looking at the relationships linking them. This is used in directing marketing campaigns, recomendation systems or in link prediction. Because of the relevance of the problem, many community detection algorithms exist, which follow different strategies. Most of them are based on the well known modularity metric, though other techniques based on random walks and epidemics spreading also exist. The problem of existing algorithms is that they have been designed to be generic, completely ignoring the particularities of the graphs belonging to different domains. As a result and under certain circumstances, these algorithms tend to find groups of nodes with a lack of a community structure. This thesis, overcomes this issues by proposing a novel community detection algorithm design methodology, called Domain Specific Community detection. This methodology is based on defining a set of structural properties communities of a given domain should fulfill, as well a set of behavioral properties to be fulfilled by a community detection algorithm or metric. Based on this methodology, we propose a set of properties for the specific domain of social networks, consisting of three structural properties (Internal structure sensitive, Bridges resistant and Cut-Vertex resistant) and three behavioral properties (Scale independent, Adaptive and Lineal community cohesion). Based on the aforementioned properties, we design a novel community detection metric, called the Weighted Community Clustering (WCC), which takes the presence of a triangle as an indicator of a strong relation between two persons in a social network. We formally prove that WCC fulfills the proposed properties, thus guaranteeting that communities resulting from maximizing WCC have a minimum degree of quality. Moreover, we prove this last statement by performing an empirical analysis on communities from real graphs, showing that WCC is able to correclty rank these well. In this thesis we also propose an algorithm called Scalable Community Detection (SCD), based on the maximization of WCC. SCD is also designed with parallelism in mind, in order to take advantage of current many-core architectures. We show that SCD is to detect communities with an unprecedented quality, being its execution time faster than most of existing proposals, being able to process billion edge graphs in a few hours This thesis also includes a statistical study about the structural characteristics of the meta-groups found in several real graphs, comparing these to graph from two different synthetic graph generators. We show that communities produced by a synthetic graph generator commonly used in community detection research are very dissimilar to those found in real graphs. Finally, this thesis includes a study on how to implement a triangle counting algorithm on a modern many core architecture, more concretely the Intel Single Chip Cloud Computer (Intel SCC).

Download or read book Overlapping Community Detection in Massive Social Networks written by Joyce Jiyoung Whang and published by . This book was released on 2015 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive social networks have become increasingly popular in recent years. Community detection is one of the most important techniques for the analysis of such complex networks. A community is a set of cohesive vertices that has more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this thesis, we propose scalable overlapping community detection algorithms that effectively identify high quality overlapping communities in various real-world networks. We first develop an efficient overlapping community detection algorithm using a seed set expansion approach. The key idea of this algorithm is to find good seeds and then greedily expand these seeds using a personalized PageRank clustering scheme. Experimental results show that our algorithm significantly outperforms other state-of-the-art overlapping community detection methods in terms of run time, cohesiveness of communities, and ground-truth accuracy. To develop more principled methods, we formulate the overlapping community detection problem as a non-exhaustive, overlapping graph clustering problem where clusters are allowed to overlap with each other, and some nodes are allowed to be outside of any cluster. To tackle this non-exhaustive, overlapping clustering problem, we propose a simple and intuitive objective function that captures the issues of overlap and non-exhaustiveness in a unified manner. To optimize the objective, we develop not only fast iterative algorithms but also more sophisticated algorithms using a low-rank semidefinite programming technique. Our experimental results show that the new objective and the algorithms are effective in finding ground-truth clusterings that have varied overlap and non-exhaustiveness. We extend our non-exhaustive, overlapping clustering techniques to co-clustering where the goal is to simultaneously identify a clustering of the rows as well as the columns of a data matrix. As an example application, consider recommender systems where users have ratings on items. This can be represented by a bipartite graph where users and items are denoted by two different types of nodes, and the ratings are denoted by weighted edges between the users and the items. In this case, co-clustering would be a simultaneous clustering of users and items. We propose a new co-clustering objective function and an efficient co-clustering algorithm that is able to identify overlapping clusters as well as outliers on both types of the nodes in the bipartite graph. We show that our co-clustering algorithm is able to effectively capture the underlying co-clustering structure of the data, which results in boosting the performance of a standard one-dimensional clustering. Finally, we study the design of parallel data-driven algorithms, which enables us to further increase the scalability of our overlapping community detection algorithms. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using these design axes, we design and test a variety of PageRank implementations finding that data-driven, push-based algorithms are able to achieve a significantly superior scalability than standard PageRank implementations. The design choices affect both single-threaded performance as well as parallel scalability. The lessons learned from this study not only guide efficient implementations of many graph mining algorithms but also provide a framework for designing new scalable algorithms, especially for large-scale community detection.

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 Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Download or read book Multiplex Networks written by Emanuele Cozzo and published by Springer. This book was released on 2018-06-27 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the basis of a formal language and explores its possibilities in the characterization of multiplex networks. Armed with the formalism developed, the authors define structural metrics for multiplex networks. A methodology to generalize monoplex structural metrics to multiplex networks is also presented so that the reader will be able to generalize other metrics of interest in a systematic way. Therefore, this book will serve as a guide for the theoretical development of new multiplex metrics. Furthermore, this Brief describes the spectral properties of these networks in relation to concepts from algebraic graph theory and the theory of matrix polynomials. The text is rounded off by analyzing the different structural transitions present in multiplex systems as well as by a brief overview of some representative dynamical processes. Multiplex Networks will appeal to students, researchers, and professionals within the fields of network science, graph theory, and data science.

Download or read book Temporal Networks written by Petter Holme and published by Springer. This book was released on 2013-05-23 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of temporal networks is an extension of complex networks as a modeling framework to include information on when interactions between nodes happen. Many studies of the last decade examine how the static network structure affect dynamic systems on the network. In this traditional approach the temporal aspects are pre-encoded in the dynamic system model. Temporal-network methods, on the other hand, lift the temporal information from the level of system dynamics to the mathematical representation of the contact network itself. This framework becomes particularly useful for cases where there is a lot of structure and heterogeneity both in the timings of interaction events and the network topology. The advantage compared to common static network approaches is the ability to design more accurate models in order to explain and predict large-scale dynamic phenomena (such as, e.g., epidemic outbreaks and other spreading phenomena). On the other hand, temporal network methods are mathematically and conceptually more challenging. This book is intended as a first introduction and state-of-the art overview of this rapidly emerging field.

Download or read book Data Intensive Text Processing with MapReduce written by Jimmy Lin and published by Springer Nature. This book was released on 2022-05-31 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks

Download or read book Modularity and Dynamics on Complex Networks written by Renaud Lambiotte and published by Cambridge University Press. This book was released on 2022-02-03 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex networks are typically not homogeneous, as they tend to display an array of structures at different scales. A feature that has attracted a lot of research is their modular organisation, i.e., networks may often be considered as being composed of certain building blocks, or modules. In this Element, the authors discuss a number of ways in which this idea of modularity can be conceptualised, focusing specifically on the interplay between modular network structure and dynamics taking place on a network. They discuss, in particular, how modular structure and symmetries may impact on network dynamics and, vice versa, how observations of such dynamics may be used to infer the modular structure. They also revisit several other notions of modularity that have been proposed for complex networks and show how these can be related to and interpreted from the point of view of dynamical processes on networks.

Download or read book High Performance Modelling and Simulation for Big Data Applications written by Joanna Kołodziej and published by Springer. This book was released on 2019-03-25 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.

Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

Download or read book Cell Based Assays for High Throughput Screening written by Paul A. Clemons and published by Humana Press. This book was released on 2014-11-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the use of high-throughput screening expands and creates more interest in the academic community, the need for detailed reference materials becomes ever more pressing. Cell-Based Assays for High-Throughput Screening: Methods and Protocols aims to fill an important part of this need by providing an easily accessible reference volume for cell-based phenotypic screening. Leading researchers in the field contribute state-of-the-art methods with actionable protocols covering four major areas of study: model biological systems, screening modalities and assay systems, detection technologies, and approaches to data analysis. Written in the highly successful Methods in Molecular BiologyTM series format, each chapter includes a brief introduction to the subject, lists of necessary materials and reagents, step-by-step laboratory protocols, and a Notes section detailing tips on troubleshooting and avoiding known pitfalls. Cutting-edge and easy-to-use, Cell-Based Assays for High-Throughput Screening: Methods and Protocols presents an overview of relevant approaches, enabling the direct application of existing methods to new discoveries while also inspiring researchers to approach their screening projects in a conceptually modular fashion, enhancing the power to discover through new combinations of existing approaches.

Download or read book Managing and Mining Graph Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-02-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.

Download or read book Graph Analysis and Visualization written by Richard Brath and published by John Wiley & Sons. This book was released on 2015-01-30 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wring more out of the data with a scientific approach to analysis Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers. Study graphical examples of networks using clear and insightful visualizations Analyze specifically-curated, easy-to-use data sets from various industries Learn the software tools and programming languages that extract insights from data Code examples using the popular Python programming language There is a tremendous body of scientific work on network and graph theory, but very little of it directly applies to analyst functions outside of the core sciences – until now. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, Graph Analysis and Visualization is a thorough, authoritative resource.

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 Analyzing Social Media Networks with NodeXL written by Derek Hansen and published by Morgan Kaufmann. This book was released on 2010-09-14 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analyzing Social Media Networks with NodeXL offers backgrounds in information studies, computer science, and sociology. This book is divided into three parts: analyzing social media, NodeXL tutorial, and social-media network analysis case studies. Part I provides background in the history and concepts of social media and social networks. Also included here is social network analysis, which flows from measuring, to mapping, and modeling collections of connections. The next part focuses on the detailed operation of the free and open-source NodeXL extension of Microsoft Excel, which is used in all exercises throughout this book. In the final part, each chapter presents one form of social media, such as e-mail, Twitter, Facebook, Flickr, and Youtube. In addition, there are descriptions of each system, the nature of networks when people interact, and types of analysis for identifying people, documents, groups, and events. Walks you through NodeXL, while explaining the theory and development behind each step, providing takeaways that can apply to any SNA Demonstrates how visual analytics research can be applied to SNA tools for the mass market Includes case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and wikis Download companion materials and resources at https://nodexl.codeplex.com/documentation