25 papers · ranked by Valyu relevance
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…
Narayanan Sundaram, Nadathur Satish, Mostofa Patwary, Subramanya R. Dulloor + 3 more
'Subramanya R. Dulloor' 'Satya Gautam Vadlamudi' 'Dipankar Das' 'Pradeep Dubey'] Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly…
Jordan M. Eizenga, Adam M. Novak, Emily Kobayashi, Flavia Villani + 6 more
Pangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic data sets can be challenging to work at. These challenges have impeded…
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…
Peng Sun, Yonggang Wen, Ta Nguyen Binh Duong, Xiaokui Xiao
—Recent studies showed that single-machine graph processing systems can be as highly competitive as clusterbased approaches on large-scale problems. While several outof-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many…
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…
Peter A. Andrews, Joan Alexander, Jude Kendall, Michael Wigler
Effective and efficient exploration of numeric data and annotations as a function of genomic position requires specialized software. We present G-Graph, an interactive genomic scatter plot viewer. G-Graph stacks or tiles multiple data series in one graph using different colors and markers. It displays gene annotation…
Lei Jiang, Langshi Chen, Judy Qiu
—In the age of Big Data, parallel graph processing has been a critical technique to analyze and understand connected data. Meanwhile, Moore's Law continues by integrating more cores into a single chip in the deep-nano regime. Many-Integrated-Core (MIC) processors emerge as a promising solution to process large graphs.…
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…
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…
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…
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…
Miyuru Dayarathna, Toyotaro Suzumura
The development of scalable, representative, and widely adopted benchmarks for graph data systems have been a question for which answers has been sought for decades. We conduct an in-depth study of the existing literature on benchmarks for graph data management and processing, covering 20 different benchmarks developed…
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…
Benjamin Schiller, Clemens Deusser, Jeronimo Castrillon, Thorsten Strufe
'Thorsten Strufe'] Graphs are used to model a wide range of systems from different disciplines including social network analysis, biology, and big data processing. When analyzing these constantly changing dynamic graphs at a high frequency, performance is the main concern. Depending on the graph size and structure…
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…
Abdullah Gharaibeh, Elizeu Santos‐Neto, Lauro Beltrão Costa, Matei Ripeanu
'Matei Ripeanu'] The increasing scale and wealth of inter-connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable knowledge from large-scale graphs. Large real-world graphs, however, are famously difficult to process…
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…
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…
Elif Sema Balcioglu, Berkay Doner, Ekansh Sareen, Dimitri Van De Ville + 1 more
Functional connectivity (FC) between brain regions as manifested via fMRI entails signatures that can be used to identify individuals and decode cognitive tasks. In this work, we use methods from graph structure inference to estimate FC, which is in contrast to the conventional approach of deriving FC via correlation.…
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…
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…
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…
Mehmet Aziz Yirik, Maria Sorokina, Christoph Steinbeck
The generation of constitutional isomer chemical spaces has been a subject of cheminformatics since the early 1960s, with applications in structure elucidation and elsewhere. In order to perform such a generation efficiently, exhaustively and isomorphism-free, the structure generator needs to ensure the building of…
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…