24 papers · ranked by Valyu relevance
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
Maciej Besta, Emanuel Peter, Robert Gerstenberger, M. L. Fischer + 4 more
'Michał Podstawski' 'Claude Barthels' 'Gustavo Alonso' 'Torsten Hoefler'] MACIEJ BESTA, Department of Computer Science, ETH Zurich ROBERT GERSTENBERGER, Department of Computer Science, ETH Zurich EMANUEL PETER, Department of Computer Science, ETH Zurich MARC FISCHER, PRODYNA (Schweiz) AG MICHAŁ PODSTAWSKI, Future…
Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth + 3 more
'David Broneske' 'Gunter Saake' 'Robert Heyer'] Title: Abstract The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving…
Renzo Angles, Claudio Gutiérrez
A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graphoriented operations and type constructors. In this article we present the basic notions of graph databases, give an…
Tomo Miyazaki, Shinichiro Omachi
—This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest…
Amine Ghrab, Oscar Romero, Sabri Skhiri, Alejandro Vaisman + 1 more
'Esteban Zimányi'] Abstract. Graph databases have emerged as the fundamental technology underpinning trendy application domains where traditional databases are not well-equipped to handle complex graph data. However, current graph databases support basic graph structures and integrity constraints with no standard…
Sumit Purohit, Nhuy Van, George Chin
—Graphs are a natural and fundamental representation of describing the activities, relationships, and evolution of various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource…
Authors not listed
Graph Neural Networks (GNNs) have emerged as a powerful tool in predicting molecular properties based on structural data. While GNNs excel in identifying local patterns within molecules, their ability to capture global properties remains limited due to inherent structural challenges such as oversmoothing and their…
Jianqiang Dong, Junwu Liu, Yifan Wei, Peilin Huang + 1 more
In biomedical research, the utilization of Knowledge Graph (KG) has proven valuable in gaining deep understanding of various processes. In this study, we constructed a comprehensive biomedical KG, named as MegaKG, by integrating a total of 23 primary data sources, which finally consisted of 188, 844 nodes/entities and…
Sabah Al‐Fedaghi
- In software system design, one of the purposes of diagrammatic modeling is to explain something (e.g., data tables) to others. Very often, syntax of diagrams is specified while the intended meaning of diagrammatic constructs remains intuitive and approximate. Conceptual modeling has been developed to capture concepts…
Liying Chen, Satwik Acharyya, Chunyu Luo, Yang Ni + 1 more
Probabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad…
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…
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…
Luiz Anastacio Alves, Natiele Carla da Silva Ferreira, Victor Maricato, Anael Viana Pinto Alberto + 2 more
'Victor Maricato' 'Anael Viana Pinto Alberto' 'Evellyn Araujo Dias' 'Nt Jose Aguiar Coelho'] Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained…
Ragini Kihlman, Ilkka Launonen, Mikko J Sillanpää, Patrik Waldmann + 1 more
'D-J de Koning'] Title: Abstract In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated…
Tom C. Freeman, Sebastian Horsewell, Anirudh Patir, Josh Harling-Lee + 5 more
Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics…
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
Pratha Sah, Lisa O. Singh, Aaron Clauset, Shweta Bansal
A modular pattern, also called community structure, is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system’s functional components and the impact of local structures on dynamics at a global…
Authors not listed
We present a unified, set–theoretic framework that extends molecular graphs to hypergraphs and superhypergraphs via iterated power sets. We define Molecular Graphs, Molecular HyperGraphs, and Molecular SuperHyperGraphs, and develop four complements over them: Weighted, Rough, Neural, and Multipolar frameworks. We prove…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Sabrina Benbatata, Bilal Saoud, Ibraheem Shayea, Naif Alsharabi + 5 more
'Abdulraqeb Alhammadi' 'Ali Alferaidi' 'Amr Jadi' 'Yousef Ibrahim Daradkeh' 'José Alberto Benítez-Andrades'] In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the…
Authors not listed
A directed graph (or digraph) consists of a finite vertex set 𝑉 and a set of ordered edges 𝐸 ⊆ 𝑉 × 𝑉, each edge (𝑢, 𝑣) indicating a one-way connection from 𝑢 (source) to 𝑣 (target). A bidirected graph is a generalization of an undirected graph where each edge is assigned a direction at each of its endpoints…
Jirui Jin, Somayeh Faraji, Bin Liu, Mingjie Liu
Perovskite materials, renowned for their versatility and remarkable properties, pose challenges in discovering optimal candidates due to the vast compositional space. Data-driven machine learning (ML) offers promise in expediting material discovery; however, the trade-off between accuracy and efficiency across…
Joshua Levy, Carly Bobak, Brock Christensen, Louis Vaickus + 1 more
Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While…