Learning Graph Structures With Autoregressive Graph Signal Models by Kyle Donoghue, Ashkan Ashrafi

“…This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals with propagation delay. …”
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Anomaly Detection and Localization via Graph Learning by Olabode Amusan, Di Wu

“…Phasor measurement units (PMUs) are being installed at an unprecedented rate on power systems, offering unique situation awareness capability. This paper presents a graph learning-based method for detecting and locating anomalies using PMU data. …”
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Discriminative graph regularized representation learning for recognition. by Jinshan Qi, Rui Xu

“…To uncover compact low-dimensional feature representations with strong generalization and discrimination capabilities for recognition tasks, in this paper, we present a novel discriminative graph regularized representation learning (DGRL) model that is able to elegantly incorporate both global and local geometric structures as well as the label structure of data into a joint framework. …”
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Anomaly Detection Over Multi-Relational Graphs Using Graph Structure Learning and Multi-Scale Meta-Path Graph Aggregation by Chi Zhang, Junho Jeong, Jin-Woo Jung

“…Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. …”
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Deep graph representation learning: methods, applications, and challenges by ZHANG Xulong, QU Xiaoyang, XIAO Chunguang, WANG Jianzong

“…These vectors play a vital role in downstream tasks such as node classification, link prediction, and anomaly detection. This paper presents a comprehensive survey of graph representation learning methods, categorizing them into traditional graph embedding methods and Graph Neural Network (GNN) based approaches. …”
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Leveraging Graph Networks to Model Environments in Reinforcement Learning by Viswanath Chadalapaka, Volkan Ustun, Lixing Liu

“…This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). …”
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Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry by Tsz Wai Ko, Bowen Deng, Marcel Nassar, Luis Barroso-Luque, Runze Liu, Ji Qi, Atul C. Thakur, Adesh Rohan Mishra, Elliott Liu, Gerbrand Ceder, Santiago Miret, Shyue Ping Ong

“…At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. …”
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GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks by Feng Wang, Xiangwei Dai, Liyan Shen, Shan Chang

“…This approach underscores the significant potential for applications in immunodiagnostics and vaccine development, merging advanced deep learning-based representation learning with graph-based modeling.…”
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Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data by Alma Ademovic Tahirovic, David Angeli, Adnan Tahirovic, Goran Strbac

“…Building on this foundation, we present the Petri Graph Neural Network (PGNN), a new class of graph neural networks capable of learning over higher-order, multimodal structures. …”
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Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation by YU Yuchen, WU Siqi, ZHAO Qinghua, WU Xuhong, WANG Lei

“…[Purposes] Considering the problems of the existing Graph Convolutional Network (GCN) recommendation models, such as low model convergence efficiency, over-smoothing, and deteriorative recommendations for long-tail items caused by the effect of high-degree nodes on presentation learning, a Contrastive Learning-based Simplified Graph Convolutional Network recommendation algorithm (SGCN-CL) is presented. …”
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Learning Multi-Attribute Differential Graphs With Non-Convex Penalties by Jitendra K. Tugnait

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The Construction of Knowledge Graphs in the Assembly Domain Based on Deep Learning by Jing Qu, Yanmei Li, Huilong Du, Wen Wang, Weiping Fu

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Topological Data Analysis and Graph-Based Learning for Multimodal Recommendation by Khalil Bachiri, Ali Yahyaouy, Maria Malek, Nicoleta Rogovschi

“…However, current multimodal methods face challenges such as modality heterogeneity, data sparsity, and feature redundancy, which can result in less effective performance when dealing with complex, high-dimensional datasets. In this study, we present a new framework that combines Topological Data Analysis (TDA) with graph-based learning to improve multimodal recommendations (TDA-MMRec). …”
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Entity Profiling in Knowledge Graphs by Xiang Zhang, Qingqing Yang, Jinru Ding, Ziyue Wang

“…Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. …”
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PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions by Yash Semlani, Mihir Relan, Krithik Ramesh

“…To learn best from this representation, we design Particle Chebyshev Network (PCN), a graph neural network (GNN) using Chebyshev graph convolutions (ChebConv). …”
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Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network by Nisa Vuran Sarı, Mehmet Acı, Çiğdem İnan Acı

“…Therefore, developing innovative detection frameworks that can effectively analyze and interpret these complex patterns has become critical. This work presents a novel framework integrating API call sequences and DLL information into a unified, graph-based representation to analyze malware behavior comprehensively. …”
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Alternative audio-graphic method for presenting structural information in mathematical graphs designed for low-vision users by Ewa Dzierzgowska, Michał Maćkowski, Mateusz Kawulok, Piotr Brzoza, Stella Maćkowska, Dominik Spinczyk

“…Abstract Despite advances in assistive technologies, existing tools for teaching mathematics to students with low vision often fail to effectively convey structural information in graphs and function plots. Current methods, such as screen readers or magnifiers, are frequently limited in their ability to present complex visual data, leading to increased cognitive load and reduced learning efficiency. …”
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Predicting traffic flow with federated learning and graph neural with asynchronous computations network by Muhammad Yaqub, Shahzad Ahmad, Malik Abdul Manan, Muhammad Salman Pathan, Lan He

“…The task of achieving a balance between prediction precision and computational efficiency presents a significant challenge. In this article, we present a novel deep-learning method called Federated Learning and Asynchronous Graph Convolutional Network (FLAGCN). …”
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A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion by Ningning Jia, Cuiyou Yao

“…Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. …”
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Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools by Alfredo Cuzzocrea, Abderraouf Hafsaoui, Carmine Gallo

“…In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. …”
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