A Reinforcement Learning Approach for Graph Rule Learning by Zhenzhen Mai, Wenjun Wang, Xueli Liu, Xiaoyang Feng, Jun Wang, Wenzhi Fu

“…Although some recent neural logic methods are more efficient in learning rules, they are generally restricted to learning chain-like rules with limited expressiveness. Taking the advantage of Reinforcement Learning (RL) in reducing search space, we implement a policy network based RL method for learning graph rules, denoted as GraphRulRL. …”
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Key determinants in implementation processes: a systematic review using the Consolidated Framework for Implementation Research (CFIR) by Marin Schmitt, Maren Hawkins, Paul Florsheim

“…While a substantial number of determinants have been identified, less research has examined the strength of their impact on the implementation process. …”
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Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search. by Davide Alocci, Julien Mariethoz, Oliver Horlacher, Jerven T Bolleman, Matthew P Campbell, Frederique Lisacek

“…The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. …”
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Satellite Mission Instruction Sequence Generation Algorithm Using a Flexible Weighted Directed Graph by Zhang Yahang, Zhang Jiakai, Yang Mengfei, Yang Peiyao, Song Xiangshuai

“…This algorithm can sort the instruction into sequence according to the instruction execution relationship with constraints, which can promote satellite operation performance of the ground station. Concepts like flexible edge, flexible zone, and implement zone were introduced, and the flexible weighted directed graph (FWDG) model was proposed. …”
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Advancing deformation calculation: a physics-informed deep graph learning framework for hyperelastic materials by Faras Brumand-Poor, Matthias Trautmann, Katharina Schmitz

“…Abstract In elastohydrodynamic lubrication (EHL) simulations, classical numerical methods like the finite difference method (FDM) and the finite element method (FEM) are commonly employed. …”
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Hardware implementation of a configurable and power-efficient current conveyor architecture by Ahmed Reda Mohamed, Muneer A. Al Absi

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Integration of OPC UA Information Models into Enterprise Knowledge Graphs by Arno Weiss, Steffen Ihlenfeldt

“…The developed practice is implemented, applied to combine a server’s structure with an existing knowledge graph containing an Asset Administration Shell and released open source.…”
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Recommendations for reporting regression-based norms and the development of free-access tools to implement them in practice. by Rok Blagus, Bojan Leskošek, Francisco B Ortega, Grant Tomkinson, Gregor Jurak

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Getting NBA Shots in Context: Analysing Basketball Shots with Graph Embeddings by Schmid Marc, Schöpf Moritz, Kolbinger Otto

“…Evaluating the quality of shots in basketball is crucial and requires considering the context in which they are taken. We introduce a graph neural network to process a graph based on player and ball tracking data to compute expected shot quality. …”
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Some More Results on Reciprocal Degree Distance Index and ℱ-Sum Graphs by Asima Razzaque, Saima Noor, Salma Kanwal, Ayesha Manzoor

“…A chemical invariant of graphical structure Z is a unique value characteristic that remains unchanged under graph automorphisms. …”
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Graph Neural Networks for Aerodynamic Analysis of Truck Platoon Under Heterogeneous Traffic Environment by Tong Liu, Fangyu Liu

“…By representing the platoon and surrounding traffic as a heterogeneous graph, the proposed GNPS model leverages node-level features such as wind configurations and vehicle geometries, alongside edge-level features like inter-vehicle distances. …”
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QUERY2BERT: Combining Knowledge Graph and Language Model for Reasoning on Logical Queries by Truong H. V. Phan, Phuc do

“…Then, embedded nodes were indexed with a K-D tree structure. Finally, we used nearest neighbor search on K-D tree to retrieve neighbor-embedded nodes and implemented logical operations like projection, intersection, union, and negation to find answers to complex questions. …”
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Parallel Simulation Using Reactive Streams: Graph-Based Approach for Dynamic Modeling and Optimization by Oleksii Sirotkin, Arsentii Prymushko, Ivan Puchko, Hryhoriy Kravtsov, Mykola Yaroshynskyi, Volodymyr Artemchuk

“…The resulting computational graph, implemented using reactive streams, offers a scalable framework for parallel computation. …”
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Optimization complexity and resource minimization of emitter-based photonic graph state generation protocols by Evangelia Takou, Edwin Barnes, Sophia E. Economou

“…These patterns allow us to process large graphs and still achieve a reduction of up to 66% in emitter CNOTs, without relying on subtle metrics such as edge density. …”
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Knowledge Graph-Augmented ERNIE-CNN Method for Risk Assessment in Secondary Power System Operations by Xiang Huang, Ping Li, Ye Wang, Xuchao Ren, Zhenbing Zhao, Gang Li

“…Furthermore, a visualization of the knowledge graph is implemented, providing interpretable decision support for risk management. …”
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NEOntometrics - A Public Endpoint for Calculating Ontology Metrics by Reiz, Achim, Sandkuhl, Kurt

“…Ontologies are the cornerstone of the semantic web and knowledge graphs. They are available from various sources, come in many shapes and sizes, and differ widely in attributes like expressivity, degree of interconnection, or the number of individuals. …”
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GraphFlood 1.0: an efficient algorithm to approximate 2D hydrodynamics for landscape evolution models by B. Gailleton, P. Steer, P. Davy, W. Schwanghart, T. Bernard

“…In this contribution, we introduce GraphFlood, a novel and efficient iterative method for computing river depth and water discharge in 2D with a digital elevation model (DEM). …”
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Enhanced Community Detection via Convolutional Neural Network: A Modified Approach Based on MRFasGCN Algorithm by Puneet Kumar, Dalwinder Singh, Mamoona Humayun, Ali Alqazzaz, Arun Malik, Ibrahim Alrashdi, Isha Batra, Ghadah Naif Alwakid

“…In this algorithm, researchers have integrated the technique of Graph Convolutional Neural Network (GCN) with the statistical model Markov Random Field (MRF) to get better results and after implementing it on large datasets comparison is done on its results with other state-of-the-art algorithms and got to know its performance is far better than any other algorithm. …”
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Advanced Anomaly Detection in Smart Grids Using Graph Convolutional Networks With Integrated Node and Line Sensor Data by Mahdi Zarif, Ramin Moghaddass

“…This article introduces an innovative method that uses a graph convolutional network (GCN) combined with a modified probability propagation matrix and dual graphs to identify and locate node/line anomalies using network sensors installed on both nodes and lines. …”
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Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). …”
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