Download or read book Scalable and Efficient Graph Algorithms and Analysis Techniques for Modern Machines written by Quanquan Catherine Liu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last part concludes with lower bounds. We show via hard instances the hardness of obtaining an optimal computation schedule on directed acyclic computation graphs in the external-memory model. We then demonstrate that such graphs can be used to construct static-memory-hard hash functions that use disk memory to deter large-scale password-cracking attacks.

Download or read book Large scale Graph Analysis System Algorithm and Optimization written by Yingxia Shao and published by Springer Nature. This book was released on 2020-07-01 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.

Download or read book Scalable Graph and Mesh Algorithms on Distributed memory Systems written by Thap Panitanarak and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Big datasets are now becoming a standard quantity in large-scale data analysis; they involve social and information network, and scientific mesh computations. These datasets are commonly stored and processed across multiple machines due to limited capabilities (such as memory and CPU) of single machines. However, many available analysis tools are still lacking in terms of an ability to fully utilize existing distributed-memory architectures. As these datasets are usually processed and analyzed in the form of graphs or meshes, we propose scalable and efficient approaches for graph and mesh computations for distributed-memory systems in this dissertation. Although graph and mesh computations are closely related regarding their parallelization approaches, some of their unique characteristics still need to be addressed separately. Thus, we organize the dissertation into two parts. The first part is for distributed graph computations, and the second part is for distributed mesh computations.In the first part of the dissertation, we focus on graph computations. First, we study a problem of Single-Source Shortest Path (SSSP) by analyzing and evaluating three well-known SSSP algorithms, i.e, Dijkstra's, Bellman-Ford, and $\Delta$-stepping algorithms. We implement these algorithms to run on distributed-memory systems based on a bulk synchronous parallel model. Their performances are evaluated and compared. Next, we propose our SSSP algorithm by combining advantages of these SSSP algorithms and utilizing a two-dimensional (2D) graph layout for our graph data structures. Then, we extend our study of the 2D graph data structures and optimization approaches to other well-known graph algorithms including breadth-first search, approximate diameter, connected components, and PageRank on various real-world graphs. Our objective is to implement an efficient graph framework for distributed-memory systems that works efficiently for many graph algorithms on various graph types. Finally, we propose graph coloring algorithms that are scalable and can be efficiently used for both graph and mesh applications.In the second part of the dissertation, we focus on parallel mesh computations on distributed-memory systems. First, we propose a domain decomposition method for 2D parallel mesh generation based on the MeTis partitioner with angle improvements. Our method is fast and gives good subdomain quality in terms of subdomain angles and mesh quality. Next, we propose a general-purpose parallel mesh warping method based on a parallel formulation of a sequential, log barrier-based mesh warping algorithm called LBWARP. Our parallel algorithm utilizes a modified distributed graph data structure with a vertex ghosting technique resulting in an efficient mesh warping algorithm which employs minimal communication. Since the algorithm needs to solve a sparse linear system with three right-hand sides (for 3D meshes), i.e., are each for the final $x$-, $y$- and $z$-coordinates in the deformed meshes, we also provide three parallel sparse linear solvers that support multiple right-hand sides for users to choose from based on the size of the problem and the number of available cores. These solvers further improve the overall performance of the algorithm, especially when a sequence of multiple deformations is required.

Download or read book Distributed Graph Analytics written by Unnikrishnan Cheramangalath and published by Springer Nature. This book was released on 2020-04-17 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph processing applications in data or network analysis requires innovations at multiple levels: algorithms, associated data structures, their implementation and tuning to a particular hardware. Further, programming languages and the associated compilers play a crucial role when it comes to automating efficient code generation for various architectures. This book discusses the essentials of all these aspects. The book is divided into three parts: programming, languages, and their compilation. The first part examines the manual parallelization of graph algorithms, revealing various parallelization patterns encountered, especially when dealing with graphs. The second part uses these patterns to provide language constructs that allow a graph algorithm to be specified. Programmers can work with these language constructs without worrying about their implementation, which is the focus of the third part. Implementation is handled by a compiler, which can specialize code generation for a backend device. The book also includes suggestive results on different platforms, which illustrate and justify the theory and practice covered. Together, the three parts provide the essential ingredients for creating a high-performance graph application. The book ends with a section on future directions, which offers several pointers to promising topics for future research. This book is intended for new researchers as well as graduate and advanced undergraduate students. Most of the chapters can be read independently by those familiar with the basics of parallel programming and graph algorithms. However, to make the material more accessible, the book includes a brief background on elementary graph algorithms, parallel computing and GPUs. Moreover it presents a case study using Falcon, a domain-specific language for graph algorithms, to illustrate the concepts.

Download or read book Big Graph Analytics on Just A Single PC written by Kai Wang and published by . This book was released on 2019 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: As graph data becomes ubiquitous in modern computing, developing systems to efficiently process large graphs has gained increasing popularity. There are two major types of analytical problems over large graphs: graph computation and graph mining. Graph computation includes a set of problems that can be represented through liner algebra over an adjacency matrix based representation of the graph. Graph mining aims to discover complex structural patterns of a graph, for example, finding relationship patterns in social media network, detecting link spam in web data. Due to their importance in machine learning, web application and social media, graph analytical problems have been extensively studied in the past decade. Practical solutions have been implemented in a wide variety of graph analytical systems. However, most of the existing systems for graph analytics are distributed frameworks, which suffer from one or more of the following drawbacks: (1) many of the (current and future) users performing graph analytics will be domain experts with limited computer science background. They are faced with the challenge of managing a cluster, which involves tasks such as data partitioning and fault tolerance they are not familiar with; (2) not all users have access to enterprise cluster in their daily development tasks; (3) distributed graph systems commonly suffer from large startup and communication overhead; and (4) load balancing in a distributed system is another major challenge. Some graph algorithms have dynamic working sets and and it is thus hard to distribute the workload appropriately before the execution. In this dissertation, we identify three categories of graph workloads for which single-machine systems are more suitable than distributed systems: (1) analytical queries that do not need exact answers; (2) program analysis tasks that are widely used to find bugs in real-world software; and (3) graph mining algorithms that are important for many information-retrieval tasks. Based on these observations, we have developed a set of single-machine graph systems to deliver efficiency and scalability specifically for these workloads. In particular, this dissertation makes the following contributions. The first contribution is the design and implementation of a single-machine graph query system named GraphQ, which divides a large graph into partitions and merges them with the guidance from an abstraction graph. By using multiple levels of abstraction, it can quickly rule out infeasible solutions and identify mergeable partitions. GraphQ uses the memory capacity as a budget and tries its best to find solutions before exhausting the memory, making it possible to answer analytical queries over very large graphs with resources affordable to a single PC. The second contribution is the design and implementation of Graspan, a single-machine, disk-based graph processing system tailored for interprocedural static analyses. Given a program graph and a grammar specification of an analysis, Graspan uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. With the help of novel graph processing techniques, we turn sophisticated code analyses into scalable Big Graph analytics. The third contribution of this dissertation is a single-machine, out-of-core graph mining system, called RStream, which leverages disk support to support efficient edge streaming for mining very large graphs. RStream employs a rich programming model that exposes relational algebra for developers to express a wide variety of mining tasks and implements a runtime engine that delivers efficiency with tuple streaming. In conclusion, this dissertation attempts to explore the opportunities of building single-machine graph systems for scenarios where distributed systems do not work well. Our experimental results demonstrate that the techniques proposed in this dissertation can efficiently solve big graph analytical problems on a single consumer PC. We hope that these promising results will encourage future work to continue building affordable single-machine systems for a rich set of datasets and analytical tasks.

Download or read book Graph Algorithms in the Language of Linear Algebra written by Jeremy Kepner and published by SIAM. This book was released on 2011-01-01 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current exponential growth in graph data has forced a shift to parallel computing for executing graph algorithms. Implementing parallel graph algorithms and achieving good parallel performance have proven difficult. This book addresses these challenges by exploiting the well-known duality between a canonical representation of graphs as abstract collections of vertices and edges and a sparse adjacency matrix representation. This linear algebraic approach is widely accessible to scientists and engineers who may not be formally trained in computer science. The authors show how to leverage existing parallel matrix computation techniques and the large amount of software infrastructure that exists for these computations to implement efficient and scalable parallel graph algorithms. The benefits of this approach are reduced algorithmic complexity, ease of implementation, and improved performance.

Download or read book Scalable Algorithms for Data and Network Analysis written by Shang-Hua Teng and published by . This book was released on 2016-05-04 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of Big Data, efficient algorithms are in high demand. It is also essential that efficient algorithms should be scalable. This book surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning.

Download or read book Graph Algorithms written by Mark Needham and published by "O'Reilly Media, Inc.". This book was released on 2019-05-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark

Download or read book Modern Graph Theory Algorithms with Python written by Colleen M. Farrelly and published by Packt Publishing Ltd. This book was released on 2024-06-07 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms Key Features Learn how to wrangle different types of datasets and analytics problems into networks Leverage graph theoretic algorithms to analyze data efficiently Apply the skills you gain to solve a variety of problems through case studies in Python Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWe are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You’ll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you’ll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you’ll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you’ll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.What you will learn Transform different data types, such as spatial data, into network formats Explore common network science tools in Python Discover how geometry impacts spreading processes on networks Implement machine learning algorithms on network data features Build and query graph databases Explore new frontiers in network science such as quantum algorithms Who this book is for If you’re a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations.

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 Irregular Graph Algorithms on Modern Multicore Manycore and Distributed Processing Systems written by George Slota and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph analysis is the study of real-world interaction data, be it through biological or chemical interaction networks, human social or communication networks, or other graph-representable datasets pervasive throughout the social and physical sciences. Due to increasing data sizes and complexities, it is important to develop efficient and scalable approaches for the algorithms, tools, and techniques used to study such data. Efficient utilization of the increasing heterogeneity and complexity of modern high performance computing systems is another major consideration for these efforts. The primary contributions of this thesis are as follows: First, parallel and scalable solutions to several basic graph analytics are presented. An implementation of the color-coding algorithm for subgraph isomorphism is introduced as FASCIA (Fast Approximate Subgraph Counting and Enumeration). Using several optimizations for work avoidance, memory usage reduction, and cache/data movement efficiency, FASCIA demonstrates up to a five orders-of-magnitude per-core speedup relative to prior art. FASCIA is able to calculate the counts of subgraphs up to 10 vertices on multi-billion edge graphs in minutes on a modest 16 node cluster and use these counts for a variety of analytics. Using FASCIA's baseline approach, FastPath is also introduced to find minimum weight paths in weighted networks. The Multistep method is next introduced as an approach for graph connectivity, weak connectivity, and strong connectivity, with a generalization of Multistep also presented for graph biconnectivity. The Multistep approaches are shown to demonstrate a 2-7x mean speedup relative to the prior state-of-the-art. A graph partitioner called PuLP (Partitioning using Label Propagation) is also introduced along with a general distributed graph layout strategy, DGL. PuLP was specifically designed to partition small-world graphs having skewed degree distributions, such as social interaction networks and web graphs. PuLP is able to partition such graphs an order of magnitude faster and with a fraction of the memory of other comparable partitioners (ParMETIS, PT-Scotch) while giving comparable partitions in terms of cut quality and balance. Additionally, this thesis presents how using techniques derived from these efforts, a suite of distributed graph analytics could be implemented and applied to the largest publicly-available web crawl of 3.5 billion pages and 130 billion links. End-to-end execution of analysis using these implementations completing in 20 minutes on only 256 nodes of the Blue Waters supercomputing system. Throughout this thesis, analyses of the algorithms and subroutines that comprise the Multistep, FASCIA/FastPath, and PuLP/DGL implementations is undertaken. Common optimizations are then identified (e.g., multiple levels of queues to match the memory hierarchy, techniques for non-blocking and asynchronous updates to shared data, efficient distributed communication patterns, among others) and their effects on performance are quantified. It is demonstrated how the optimization techniques can be utilized when processing under the higher degree of parallelism available in modern manycores (GPUs, Intel Xeon Phis) as well as how the techniques can be extended for more general-purpose graph processing in both the shared- and distributed-memory spaces. Also under consideration is the state of current hardware trends, with the goal of identifying how to modify and extend these general optimizations for forthcoming high performance computing architectures. Additionally, new optimizations and potential further research areas are introduced which might also be applicable for accelerating graph processing on these future systems.

Download or read book Graph Machine Learning written by Claudio Stamile and published by Packt Publishing Ltd. This book was released on 2021-06-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

Download or read book Scaling Up Machine Learning written by Ron Bekkerman and published by Cambridge University Press. This book was released on 2012 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Download or read book Individual and Collective Graph Mining written by Danai Koutra and published by Springer Nature. This book was released on 2022-06-01 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas: Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities. Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity. The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science, to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.

Download or read book Scalable Graph Algorithms Using Practically Efficient Data Reductions written by Sebastian Lamm and published by . This book was released on 2022* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Characterizing and Improving Graph Algorithm Performance on Multicore Systems written by Nicole Celeste Rodia and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The rise of big data analytics has contributed to the growing popularity and scale of graph datasets, positioning graph analysis as an important research area. Graph analysis is an essential tool in many domains, including the physical and social sciences, healthcare, business intelligence, and cybersecurity. The increasing scale of graph analysis problems, with graphs containing millions or billions of vertices and edges, has made parallel and distributed graph algorithms essential for effective analysis of these large datasets. At the same time, modern multicore systems have been scaling to higher core counts, with dozens of complex cores in a single system. At first glance, it would seem that graph algorithms can leverage data-level parallelism across graph vertices and edges to utilize this large number of cores to quickly process large datasets. In fact, on multicore systems, graph algorithms are typically inefficient and perform poorly. The real-world informatics graphs used for today's big data analytics are derived from online social networks, web page links, genomics data, and the like. These networks possess fundamental properties that differ from traditional graphs like trees or meshes, resulting in different execution characteristics. We study the factors behind this lack of performance and demonstrate software and hardware techniques that improve performance. First, we analyze the perfor- mance characteristics of a core set of graph analysis algorithms across several infor- matics, physical, and synthetic graph datasets using a multicore microarchitectural simulator. Our characterization indicates that poor performance is due to several fac- tors, including irregular data access patterns, load imbalance, high communication- to-computation ratio, and ineffective caching techniques. To investigate the potential for caching to improve graph algorithm performance, we study the algorithms' data locality. Cache miss rates are an unreliable metric for data locality because they are heavily influenced by dataset size, cache size, and replacement policy. Thus, we use cache-independent locality analysis techniques, including reuse distance and a probability-based locality score, to analyze data locality in graph algorithms. Based on our analysis of data locality, we find that LRU-based cache replacement policies do not provide good performance for the data access patterns characteristic of graph algorithms. Further, we show that data access patterns correlate with algorithm characteristics, graph dataset structure, and vertex degree. These insights indicate that utilization of algorithm- and dataset-specific locality information paired with an improved cache replacement policy could significantly improve graph algorithm performance. Second, we employ our knowledge of real-world graph properties to redesign the algorithm for detecting strongly connected components (SCCs) in a directed graph, a fundamental graph analysis algorithm used in many scientific and engineering do- mains. Traditional approaches in parallel SCC detection show limited performance and poor scaling behavior when applied to large real-world graph instances. We investigate the shortcomings of the conventional approach and propose a series of ex- tensions that account for the fundamental properties of real-world graphs, particularly the small-world property. Our scalable implementation offers excellent performance on diverse small-world graphs resulting in a factor of 5 to 29 times parallel speedup over an optimal sequential algorithm on 16 cores and 32 hardware threads. Third, we propose a new cache replacement policy based on our observations of data locality in graph algorithms. The Graph Priority Insertion Policy (GPIP) uses per-data-structure software priority hints to improve last-level cache hit rates by maintaining data with higher locality in the cache. This policy provides an average reduction in misses per thousand instruction (MPKI) of 3% over least-recently used (LRU) replacement. Overall, our contributions serve to expand understanding of the characteristics of graph algorithms and improve graph algorithm performance through both software and hardware means.

Download or read book Hands On Graph Analytics with Neo4j written by Estelle Scifo and published by Packt Publishing Ltd. This book was released on 2020-08-21 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook Description Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data. What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is for This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.