World PulseNow
Newsletter

Heterogeneous Attributed Graph Learning via Neighborhood-Aware Star Kernels

arXiv — cs.LG•Monday, November 17, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
- The Neighborhood-Aware Star Kernel (NASK) has been proposed as a novel approach for attributed graph learning, addressing the challenges of capturing heterogeneous attribute semantics and neighborhood information in attributed graphs. This development is significant as it enhances the ability to analyze complex data structures in various fields, including social networks and bioinformatics, potentially leading to improved insights and applications. Although there are no directly related articles, the introduction of NASK aligns with ongoing advancements in graph kernels and machine learning methodologies, emphasizing the importance of integrating diverse data attributes.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
Explicit Multimodal Graph Modeling for Human-Object Interaction Detection
arXiv — cs.CV2 days ago
Explicit Multimodal Graph Modeling for Human-Object Interaction Detection
PositiveArtificial Intelligence
Recent advancements in Human-Object Interaction (HOI) detection have seen the rise of Transformer-based methods. However, these methods do not adequately model the relational structures essential for recognizing interactions. This paper introduces Multimodal Graph Network Modeling (MGNM), which utilizes Graph Neural Networks (GNNs) to better capture the relationships between human-object pairs, thereby enhancing HOI detection through a four-stage graph structure and a multi-level feature interaction mechanism.
Read full article
via arXiv — cs.CV
Multi-View Polymer Representations for the Open Polymer Prediction
arXiv — cs.LG2 days ago
Multi-View Polymer Representations for the Open Polymer Prediction
PositiveArtificial Intelligence
The article discusses a novel approach to polymer property prediction using a multi-view design that incorporates various representations. The system combines four families of representations: tabular RDKit/Morgan descriptors, graph neural networks, 3D-informed representations, and pretrained SMILES language models. This ensemble method achieved a public mean absolute error (MAE) of 0.057 and a private MAE of 0.082, ranking 9th out of 2241 teams in the Open Polymer Prediction Challenge at NeurIPS 2025.
Read full article
via arXiv — cs.LG
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
arXiv — cs.LG2 days ago
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
PositiveArtificial Intelligence
Graph neural networks (GNNs) are essential for analyzing relational data, categorized into convolutional, attentional, and message-passing variants. The standard message-passing approach, while expressive, overlooks the rich contextual information from the broader local neighborhood, limiting its ability to learn complex relationships. This article introduces a new framework called neighborhood-contextualized message-passing (NCMP) to address this limitation, enhancing the expressivity and efficiency of GNNs.
Read full article
via arXiv — cs.LG
Flow-Attentional Graph Neural Networks
arXiv — cs.LG2 days ago
Flow-Attentional Graph Neural Networks
PositiveArtificial Intelligence
Graph Neural Networks (GNNs) are crucial for analyzing graph-structured data, but current models overlook the conservation laws relevant to physical resource flows, such as electrical currents in power grids. To improve performance, a new approach called flow attention is introduced, which aligns with Kirchhoff's first law. Experiments on electronic circuits and power grids demonstrate that this method enhances the effectiveness of attention-based GNNs in classification and regression tasks.
Read full article
via arXiv — cs.LG
Hypergraph Neural Network with State Space Models for Node Classification
arXiv — cs.LG2 days ago
Hypergraph Neural Network with State Space Models for Node Classification
PositiveArtificial Intelligence
Recent advancements in graph neural networks (GNNs) have highlighted their effectiveness in node classification tasks. However, traditional GNNs often neglect role-based characteristics that can enhance node representation learning. To overcome these limitations, a new model called the hypergraph neural network with state space model (HGMN) has been proposed, integrating role-aware representations and employing hypergraph construction techniques to capture complex relationships among nodes.
Read full article
via arXiv — cs.LG
Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss
arXiv — cs.LG2 days ago
Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss
NeutralArtificial Intelligence
The article presents a novel approach to incomplete multi-view clustering (IMVC) through Dynamic Deep Graph Learning with Masked Graph Reconstruction Loss. It highlights the limitations of existing methods, particularly their reliance on K-Nearest Neighbors (KNN) and Mean Squared Error (MSE) loss, which can introduce noise and reduce graph robustness. The proposed method aims to enhance the effectiveness of IMVC by addressing these challenges, thereby contributing to the advancement of Graph Neural Networks (GNNs) in this field.
Read full article
via arXiv — cs.LG
Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports
arXiv — cs.LG2 days ago
Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports
NeutralArtificial Intelligence
Graph neural networks (GNNs) are increasingly utilized in urban spatiotemporal forecasting, particularly for predicting infrastructure issues like potholes and rodent problems. Government inspection ratings provide insights into the state of incidents in various neighborhoods, but these ratings are limited to a sparse selection of areas. To enhance prediction accuracy, a new multiview, multioutput GNN model integrates both government ratings and crowdsourced reports, addressing biases in reporting behavior and improving the understanding of urban incidents.
Read full article
via arXiv — cs.LG
When Genes Speak: A Semantic-Guided Framework for Spatially Resolved Transcriptomics Data Clustering
arXiv — cs.LG2 days ago
When Genes Speak: A Semantic-Guided Framework for Spatially Resolved Transcriptomics Data Clustering
PositiveArtificial Intelligence
The article discusses SemST, a new semantic-guided deep learning framework for clustering spatial transcriptomics data. This framework utilizes Large Language Models (LLMs) to interpret gene symbols, transforming them into biologically informed embeddings. By integrating these embeddings with spatial relationships through Graph Neural Networks (GNNs), SemST aims to enhance the understanding of gene expression in tissue microenvironments, achieving state-of-the-art clustering performance.
Read full article
via arXiv — cs.LG