Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one…
Amin Sahebi, Marco Barbone, Marco Procaccini, Wayne Luk + 2 more
'Georgi Gaydadjiev' 'Roberto Giorgi'] Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph…
Chiwan Park, Ha-Myung Park, U. Kang, Roland Bouffanais
How can we analyze large graphs such as the Web, and social networks with hundreds of billions of vertices and edges? Although many graph mining systems have been proposed to perform various graph mining algorithms on such large graphs, they have difficulties in processing Web-scale graphs due to massive communication…
Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai + 6 more
'Wenyuan Yu' 'Wenjie Zhang' 'Xuemin Lin' 'Jingren Zhou'] LINGKAI MENG, Antai College of Economics and Management, Shanghai Jiao Tong University, China YU SHAO, East China Normal University, China LONG YUAN∗ , Nanjing University of Science and Technology, China LONGBIN LAI, Alibaba Group, China PENG CHENG, East China…
Gabriel Tanase, Toyotaro Suzumura, Jinho Lee, Chun-Fu Chen + 4 more
'Jason Crawford' 'Hiroki Kanezashi' 'Song Zhang' 'Warut D. Vijitbenjaronk'] Motivated by the need to extract knowledge and value from interconnected data, graph analytics on big data is a very active area of research in both industry and academia. To support graph analytics efficiently a large number of in memory graph…
Alessio Guerrieri, Alberto Montresor
—The availability of larger and larger graph datasets, growing exponentially over the years, has created several new algorithmic challenges to be addressed. Sequential approaches have become unfeasible, while interest on parallel and distributed algorithms has greatly increased. Appropriately partitioning the graph as…
Jie Cao, Haoxiang Wang, Jingru Jiao, Kekun Hu + 1 more
With the rapid expansion of social networks, efficiently mining and analyzing massive graph data has become a fundamental challenge in social network research. Graph partitioning plays a pivotal role in enhancing the performance of such analyses. However, conventional graph partitioning methods predominantly rely on…
Vasiliki Kalavri, Vladimir Vlassov, Seif Haridi
—Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs arise in application domains such as machine learning, recommendation, web search, and social network analysis. Writing…
Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola, Danny Bickson + 2 more
'Carlos Guestrin' 'Joseph M. Hellerstein'] While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient…
Erfan Farhangi Maleki, Nasser Ghadiri, Maryam Lotfi Shahreza, Zeinab Maleki
Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The process of mining and knowledge extraction from these networks is so complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. In this paper, two distributed label…
Jannis Koch, Christian Staudt, Maximilian Vogel, Henning Meyerhenke
We describe and compare programming models for distributed computing with a focus on graph algorithms for large-scale complex network analysis. Four frameworks – GraphLab, Apache Giraph, Giraph++ and Apache Flink – are used to implement algorithms for the representative problems Connected Components, Community…
Peng Zhang, Wenzhang Dou, Huaping Liu
Flowcharts have broad applications in the fields of software development, engineering design, and scientific experimentation. Current flowchart data structure is mainly based on the adjacency list, cross-linked list, and adjacency matrix of the graph structure. Such design originated from the fact that any two nodes…
Jordan K. Matelsky, Erik C. Johnson, Brock Wester, William Gray-Roncal
Neuroscientists now have the opportunity to analyze synaptic resolution connectomes that are larger than the memory on single consumer workstations. As dataset size and tissue diversity have grown, there is increasing interest in conducting comparative connectomics research, including rapidly querying and searching for…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Wilfried Agbeto, Camille Coti, Vladimir Reinharz
Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex structures by identifying conserved sub-structures is a key milestone towards the artificial design of…
Sabuzima Nayak, Ripon Patgiri
Big Graph is a graph having thousands of vertices and hundreds of thousands of edges. The study of graphs is crucial because the interlinkage among the vertices provides various insights and uncovers the hidden truth developed due to their relationship. The graph processing has non-linear time complexity. The…
Authors not listed
Background: Chemical reactions form intricate, highly connected networks whose exploration is essential for discovering more efficient and sustainable synthetic routes. As reaction data from literature, patents, and high‑throughput experimentation continue to surge, so does the need for tools that can collectively…
Michael Statt, Brian Rohr, Dan Guevarra, Ja'Nya Breeden + 2 more
Materials knowledge is inherently hierarchical. While high-level descriptors such as composition and structure are valuable for contextualizing materials data, the data must ultimately be considered in the context of its low-level acquisition details. Graph databases offer an opportunity to represent hierarchical…
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Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
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Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD framework, a scalable, maintainable, and secure microservices platform designed to support the sustainable…