23 papers · ranked by Valyu relevance
Alif Ahmed, Farzana Ahmed Siddique, Kevin Skadron
—Streaming graph processing involves performing batched updates and analytics on a time-evolving graph. The underlying representation format largely determines the throughputs of these updates and analytics phases. Existing representation formats usually employ variations of hash tables or adjacency lists. However, a…
Luca Cappelletti, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr + 7 more
'Tiffany J. Callahan' 'Carlos Cano' 'Marcin P. Joachimiak' 'Christopher J. Mungall' 'Peter N. Robinson' 'Justin Reese' 'Giorgio Valentini'] Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise…
Hongfu Li
An efficient data structure is fundamental to meeting the growing demands in dynamic graph processing. However, the dual requirements for graph computation efficiency (with contiguous structures) and graph update efficiency (with linked list-like structures) present a conflict in the design principles of graph…
Simon Heumos, Andrea Guarracino, Jan-Niklas M. Schmelzle, Jiajie Li + 5 more
The increasing availability of complete genomes demands for models to study genomic variability within entire populations. Pangenome graphs capture the full genomic similarity and diversity between multiple genomes. In order to understand them, we need to see them. For visualization, we need a human readable graph…
Haoran Sun, Likai Liang, Zhongrui Wang
Graph Neural Networks (GNNs) have become essential for analyzing graph-structured data, yet their deployment on resource-constrained edge devices is severely limited by high computational complexity and irregular memory access patterns. Here, we introduce DynamiGraph, a specialized FPGA-based overlay accelerator…
Wilfried Agbeto, Camille Coti, Vladimir Reinharz
Subgraph isomorphism is a combinatorial problem that involves finding one or all occurrences of a pattern graph within a target graph. Subgraph isomorphism has numerous applications in fields such as biology, chemistry, social network analysis, and pattern recognition. Although subgraph isomorphism is generally…
Fawaz Dabbaghie
The representation of genomes and genomic sequences through graph structures has undergone a period of rapid development in recent years, particularly to accommodate the growing size of genome sequences that are being produced. Genome graphs have been employed extensively for a variety of purposes, including assembly…
Tom C. Freeman, Sebastian Horsewell, Anirudh Patir, Josh Harling-Lee + 6 more
'Tim Regan' 'Barbara B. Shih' 'James Prendergast' 'David A. Hume' 'Tim Angus' 'Eli Zunder'] Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to…
Wilfried Agbeto, Camille Coti, Vladimir Reinharz
Subgraph isomorphism is a fundamental combinatorial problem that involves finding one or more occurrences of a pattern graph within a target graph. It arises in a wide range of application domains, including biology, chemistry, social network analysis, and pattern recognition. Although subgraph isomorphism is…
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…
Puneet Mehrotra, Vaastav Anand, Daniel Margo, Milad Rezaei Hajidehi + 1 more
'Margo Seltzer'] Graph-structured data is prevalent in domains such as social networks, financial transactions, brain networks, and protein interactions. As a result, the research community has produced new databases and analytics engines to process such data. Unfortunately, there is not yet widespread benchmark…
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…
Nibedita Behera, Ashwina Kumar, Chougule, Atharva + 3 more
With the growth of unstructured and semistructured data, parallelization of graph algorithms is inevitable for efficiency. Unfortunately, due to the inherent irregularity of computation, memory access, and communication, graph algorithms are traditionally challenging to parallelize. To tame this challenge, several…
Zhengyang Lv, Mingyu Yan, Xin Liu, Mengyao Dong + 3 more
'Dongrui Fan' 'Ninghui Sun'] Graph-related applications have experienced significant growth in academia and industry, driven by the powerful representation capabilities of graph. However, efficiently executing these applications faces various challenges, such as load imbalance, random memory access, etc. To address…
Zuxi Chen, ShiFan Zhang, XianLi Zeng, Meng Mei + 3 more
'Lixiao Zheng' 'Sedat Akleylek'] This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected…
Jianshu Zhao, Jean Pierre Both, Rob Knight
Graph/network representation learning (or graph/network embedding) is a widely used machine learning technique in industry recommending systems and has recently been applied in computational biology. Popular network representation learning algorithms include random walk and matrix factorization methods, but they do not…
Ali TehraniJamsaz, Quazi Ishtiaque Mahmud, Le Chen, Nasreen K. Ahmed + 1 more
'Ali Jannesari'] The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis. However, a key challenge in adopting the latest machine learning methods is the representation of programming languages, which directly impacts the ability…
Xin Zhao, Xuan Wang, Xianzhe Zou, Huiming Liang + 5 more
'Ning Zhang' 'Xin Huang' 'Fangfang Zhou' 'Ying Zhao'] Web-based libraries, such as D3.js, ECharts.js, and G6.js, are widely used to generate node-link graph visualizations. These libraries allow users to call application programming interfaces (APIs) without identifying the details of the encapsulated techniques such…
Brandon Walker, Nathan Miller, Brett Yang, Dhatri V. L. Penna + 15 more
Rapid generation and evaluation of diverse synthesis pathways play a critical role in exploring a broader chemical space and identifying potent drug candidates. Drug discovery often relies on laborintensive manual processes for retro synthetic route finding, resulting in challenges related to scalability and…
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
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…
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…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…