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Massive Graph Analytics
"Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics." — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national laboratories, and industry who wish to learn about the state-of-the-art algorithms, models, frameworks, and software in massive-scale graph analytics.
Complex Networks & Their Applications V
This book highlights cutting-edge research in the field of network science, offering scientists, researchers and graduate students a unique opportunity to catch up on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the fifth International Workshop on Complex Networks & their Applications (COMPLEX NETWORKS 2016), which took place in Milan during the last week of November 2016. The carefully selected papers are divided into 11 sections reflecting the diversity and richness of research areas in the field. More specifically, the following topics are covered: Network models; Network measures; Community structure; Network dynamics; Diffusion, epidemics and spreading processes; Resilience and control; Network visualization; Social and political networks; Networks in finance and economics; Biological and ecological networks; and Network analysis.
The Algorithm Design Manual
"My absolute favorite for this kind of interview preparation is Steven Skiena’s The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace ... graph problems are -- they should be part of every working programmer’s toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. ... every 1 – pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types." (Steve Yegge, Get that Job at Google) "Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and so...
SIAM International Meshing Roundtable 2023
This volume comprises selected papers from the SIAM International Meshing Roundtable Workshop 2023, SIAM IMR 2023, held in Amsterdam, the Netherlands, March 6-9, 2023. The IMR was started by Sandia National Laboratories in 1992 and, since 2021, it has been held under the umbrella of the Society for Industrial and Applied Mathematics (SIAM). The SIAM IMR 2023 was the first in-situ conference to be held since 2019, after two online conferences. It consisted of short courses, technical presentations from keynotes, contributed talks and research notes, a poster session and meshing contest, and discussion panels. The papers in these proceedings present novel contributions that range from theoretical advances to technical applications in engineering, geometric modeling, computer graphics, visualization, and machine learning.
Experimental Algorithms
This book constitutes the refereed proceedings of the 13th International Symposium on Experimental Algorithms, SEA 2014, held in Copenhagen, Denmark, in June/July 2014. The 36 revised full papers presented together with 3 invited presentations were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on combinatorial optimization, data structures, graph drawing, shortest path, strings, graph algorithms and suffix structures.
Euro-Par 2005 Parallel Processing
Euro-Par 2005 was the eleventh conference in the Euro-Par series. It was organized by the Centre for Informatics and Information Technology (CITI) and the Department of Informatics of the Faculty of Science and Technology of Universidade Nova de Lisboa, at the Campus of Monte de Caparica.
Euro-Par 2006 Parallel Processing
This book constitutes the refereed proceedings of the 12th International Conference on Parallel Computing, Euro-Par 2006. The book presents 110 carefully reviewed, revised papers. Topics include support tools and environments; performance prediction and evaluation; scheduling and load balancing; compilers for high performance; parallel and distributed databases, data mining and knowledge discovery; grid and cluster computing: models, middleware and architectures; parallel computer architecure and instruction-level parallelism; distributed systems and algorithms, and more.
Graph Partitioning and Graph Clustering
Graph partitioning and graph clustering are ubiquitous subtasks in many applications where graphs play an important role. Generally speaking, both techniques aim at the identification of vertex subsets with many internal and few external edges. To name only a few, problems addressed by graph partitioning and graph clustering algorithms are: What are the communities within an (online) social network? How do I speed up a numerical simulation by mapping it efficiently onto a parallel computer? How must components be organized on a computer chip such that they can communicate efficiently with each other? What are the segments of a digital image? Which functions are certain genes (most likely) responsible for? The 10th DIMACS Implementation Challenge Workshop was devoted to determining realistic performance of algorithms where worst case analysis is overly pessimistic and probabilistic models are too unrealistic. Articles in the volume describe and analyze various experimental data with the goal of getting insight into realistic algorithm performance in situations where analysis fails.
