Graph-Based Extractive Text Summarization Models: A Systematic Review by Abdulkadir Bichi, Pantea Keikhosrokiani, Rohayanti Hassan, Khalil Almekhlafi

“…This paper presents a novel systematic review of various graph-based automatic text summarization models.…”
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Accurate prediction of protein function using statistics-informed graph networks by Yaan J. Jang, Qi-Qi Qin, Si-Yu Huang, Arun T. John Peter, Xue-Ming Ding, Benoît Kornmann

“…However, more than 200 million proteins remain uncharacterized, and computational efforts heavily rely on protein structural information to predict annotations of varying quality. Here, we present a method that utilizes statistics-informed graph networks to predict protein functions solely from its sequence. …”
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Knowledge graph based entity selection framework for ad-hoc retrieval by Pankaj Singh, Plaban Kumar Bhowmick

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SVM directed machine learning classifier for human action recognition network by Dharmanna Lamani, Pramod Kumar, A Bhagyalakshmi, J. Maria Shanthi, Lakshmana Phaneendra Maguluri, Mohammad Arif, C Dhanamjayulu, Sathish Kumar. K, Baseem Khan

“…The suggested method presents an innovative pipeline for creating spatial motion data from raw video inputs, which makes successful latent representation learning of human motions easier to accomplish. …”
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Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction by Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Muhammad Shahzad Sarfraz, Yang Yu, Hafiz Tayyab Rauf

“…To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. …”
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A Review of 2D Lidar SLAM Research by Yingying Ran, Xiaobin Xu, Zhiying Tan, Minzhou Luo

“…This paper reviews the progress of 2D Lidar SLAM algorithms based on four principles: filter-based SLAM, matching-based SLAM, graph optimization-based SLAM, and deep learning-based SLAM, highlighting their advantages, disadvantages, and applicability. …”
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Complex Network Method for Inferring Well Interconnectivity in Hydrocarbon Reservoirs by M. Mayoral-Villa, F. A. Godínez, J. A. González-Guevara, J. Klapp, J. E. V. Guzmán

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Gene2role: a role-based gene embedding method for comparative analysis of signed gene regulatory networks by Xin Zeng, Shu Liu, Bowen Liu, Weihang Zhang, Wanzhe Xu, Fujio Toriumi, Kenta Nakai

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A Hotspot Prediction Scheme Based on Ensemble Learning With Adaptive Beamforming for Virtual Small Cell in 5G Networks by Aws Majeed Ghalib Alawadi, Hadi Seyedarabi, Reza Afrouzian, Javad Musevi Niya

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Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization by Miguel García-Ortegón, Srijit Seal, Carl Rasmussen, Andreas Bender, Sergio Bacallado

“…Graph NPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as alternative techniques for transfer learning and meta-learning. …”
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Fusing multiplex heterogeneous networks using graph attention-aware fusion networks by Ziynet Nesibe Kesimoglu, Serdar Bozdag

“…Enabling these architectures to work on networks with multiple node and edge types brings additional challenges due to the heterogeneity of the networks and the multiplicity of the existing associations. In this study, we present a framework, named GRAF (Graph Attention-aware Fusion Networks), to convert multiplex heterogeneous networks to homogeneous networks to make them more suitable for graph representation learning. …”
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Accelerating Thermal Homann Flow Simulation With Mesh-Based Graph Neural Networks by Dara Rahmat Samii, Moussa Tembely

“…Due to the prominence of this flow and its computationally intensive nature when dealing with turbulent regimes in complex geometries, the present paper explores the use of machine learning (ML) to solve Homann flow with heat transfer both rapidly and accurately. …”
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Infant cry classification using an efficient graph structure and attention-based model by Qiao X., Jiao S., Li H.

“…The experimental results demonstrate that the use of the efficient graph structure improved the accuracy by an average of 8.01% compared to using standalone speech features, and the AlgNet model achieved an average accuracy improvement of 5.62% compared to traditional deep learning models. …”
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Rumor detection using dual embeddings and text-based graph convolutional network by Barsha Pattanaik, Sourav Mandal, Rudra M. Tripathy, Arif Ahmed Sekh

“…This graph undergoes convolution, and through graph-based learning, the model detects a rumor. …”
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A non-anatomical graph structure for boundary detection in continuous sign language by Razieh Rastgoo, Kourosh Kiani, Sergio Escalera

“…In addition, we propose to present a non-anatomical graph structure to better present the hand joints movements and relations during the signing. …”
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Decoding Gestures in Electromyography: Spatiotemporal Graph Neural Networks for Generalizable and Interpretable Classification by Hunmin Lee, Ming Jiang, Jinhui Yang, Zhi Yang, Qi Zhao

“…Harnessing these tailored graph structures, we present Graph Convolution Network (GCN)-based classification models adept at effectively extracting and aggregating key features associated with various gestures. …”
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MuRelSGG: Multimodal Relationship Prediction for Neurosymbolic Scene Graph Generation by Muhammad Junaid Khan, Adil Masood Siddiqui, Hamid Saeed Khan, Faisal Akram, M. Jaleed Khan

“…Neurosymbolic Scene Graph Generation (SGG) is a promising approach that jointly leverages the perception capabilities of deep neural networks and the reasoning capabilities of symbolic techniques for scene understanding and visual reasoning. …”
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Multi dynamic temporal representation graph convolutional network for traffic flow prediction by Zuojun Wu, Xiaojun Liu, Xiaoling Zhang

“…To address this issue, we propose a novel Multi Dynamic Temporal Representation Graph Convolutional Network (MDTRGCN). Specifically, we introduce a dynamic graph construction method that learns the time‒space dependencies specific to road segments. …”
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A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification by Ziwei Li, Weiming Xu, Shiyu Yang, Juan Wang, Hua Su, Zhanchao Huang, Sheng Wu

“…However, redundant background interference, varying feature scales, and high interclass similarity in remote sensing images present significant challenges for RSSC. To address these challenges, this article proposes a novel hierarchical graph-enhanced transformer network (HGTNet) for RSSC. …”
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KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection by Quan Yan, Junwen Duan, Jianxin Wang

“…KEXNet employs a unique strategy akin to expert radiologists, integrating a knowledge graph based on expert annotations with an interpretable graph learning approach. …”
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