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We are delighted to present the papers from the 2nd GRADES-NDA Joint Workshop on Graph Data Management Experiences & Systems and Network Data Analytics, which took place on 30th June, 2019 co-located with the ACM SIGMOD conference in Amsterdam, Netherlands. GRADES-NDA 2019 is the second joint meeting of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016.The focus of GRADES-NDA is the application areas, usage scenarios and open challenges in managing large-scale graph-shaped data. The workshop is a forum for exchanging ideas and methods for mining, querying, and learning with real-world network data, developing new common understandings of the problems at hand, sharing of data sets and benchmarks where applicable, and leveraging existing knowledge from different disciplines. GRADES-NDA aims to present technical contributions inside graph, RDF, and other data management systems on massive graphs. The purpose of this workshop is to bring together researchers from academia, industry, and government, (1) to create a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards (2) addressing domain specific challenges or (3) handling noise in real-world graphs.

Data mining --- Database management --- Graph theory Data processing.

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Spanning trees play an important role in the design of efficient routing algorithms and help solve computationally hard problems like the Steiner tree and the traveling salesperson problem. This is one of the most important concepts used in graph theory, as it forms the foundation of many theoretical results in practical fields like scheduling, clustering, data mining, and many others. Spanning Trees and Optimization Problems is the first book published on the use and applications associated with the modern study of spanning trees. It covers properties of spanning trees first, followed by the three categories of spanning trees: minimum spanning trees, shortest-paths trees, and minimum routing cost spanning trees.

Trees (Graph theory) --- Mathematical optimization --- Data processing --- Mathematical optimization. --- Data processing. --- Trees (Graph theory) - Data processing

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Graph grammars originated in the late 60s, motivated by considerations about pattern recognition and compiler construction. Since then the list of areas which have interacted with the development of graph grammars has grown quite impressively. Besides the aforementioned areas it includes software specification and development, VLSI layout schemes, database design, modeling of concurrent systems, massively parallel computer architectures, logic programming, computer animation, developmental biology, music composition, visual languages, and many others.The area of graph grammars and graph tra

Graph grammars. --- Graph theory --- Grammars, Graph --- Formal languages --- Data processing. --- Graph grammars --- Data processing --- Graph theory - Data processing

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Computer science --- Discrete mathematics --- Graph theory --- Data processing --- 519.17 --- 681.3*G22 --- Graph theory. Trees --- Graph theory: graph algorithms; network problems; path and tree problems; trees--See also {681.3*F22} --- 681.3*G22 Graph theory: graph algorithms; network problems; path and tree problems; trees--See also {681.3*F22} --- 519.17 Graph theory. Trees --- Graph theory - Data processing

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This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors.

Data mining --- Graph theory --- Algorithms --- Multidimensional scaling --- Data processing --- Information systems --- Discrete mathematics --- Data mining. --- Algorithms. --- Multidimensional scaling. --- Psychometrics --- Scaling (Social sciences) --- Algorism --- Algebra --- Arithmetic --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data processing. --- Foundations --- Graph theory - Data processing