graph neural networks

Cognitive Graph Transformers for Multi-Modal Reasoning

This paper introduces a novel approach to multi-modal reasoning by integrating cognitive principles into the Graph Transformer architecture. We propose a 'cognitive memory' module that enhances the model's ability to capture and leverage past interactions and contextual information. The attention mechanism is then bias...

Cognitive Graph Transformers for Multi-Modal Reasoning

This paper introduces a novel approach to multi-modal reasoning by integrating cognitive principles into the Graph Transformer architecture. We propose a 'cognitive memory' module that enhances the model's ability to capture and leverage past interactions and contextual information. The attention mechanism is then bias...

##基于自适应的图神经网络模型

The ability to effectively model complex relationships within data is crucial for many applications in fields ranging from natural language processing to image recognition. Traditional graph neural networks (GNNs) offer a powerful framework for this, but their performance can be significantly impacted by the data's inh...

Adaptive Kernel Methods for Non-Euclidean Data

Traditional kernel methods have demonstrated remarkable success in various machine learning tasks, particularly in capturing non-linear relationships within data. However, their applicability is severely limited when dealing with non-Euclidean data, where the notion of distance and similarity becomes ambiguous. This wo...

Title: Emergent Topological Order in Graph Neural Networks

Graph neural networks (GNNs) have demonstrated remarkable success in various tasks involving graph data, including social network analysis, drug discovery, and knowledge graph reasoning. However, a fundamental challenge remains: effectively capturing and utilizing the inherent topological order within these graphs. Thi...

Information-Theoretic Limits on Graph Neural Network Compression

Graph Neural Networks (GNNs) have achieved remarkable success in various domains, including node classification, graph classification, and link prediction. However, their large size and high computational cost pose significant challenges for deployment, particularly on resource-constrained devices. Graph Neural Network...

Dynamic Graph Representation Learning via Graph Transformers

Graph representation learning has emerged as a powerful technique for analyzing and understanding complex data represented as graphs. Traditional Graph Neural Networks (GNNs) typically learn static representations of nodes, often neglecting the evolving nature of relationships within the graph. This work introduces a n...

Hypergraph Neural Networks with Symbolic Regression

This paper proposes a novel approach to learning complex functions and representations by integrating Hypergraph Neural Networks (HNNs) with Symbolic Regression (SR). The core idea is to leverage the graph representation capabilities of HNNs to extract informative features from hypergraph data, and then utilize a symbo...

ToxBench: a leakage-audited multi-task benchmark for predictive toxicology with calibration, uncertainty, and applicability domain analysis

Machine learning models for toxicity prediction are routinely evaluated using random train/test splits, allowing structurally similar compounds to appear in both sets and inflating reported performance metrics. A rigorous benchmark quantifying this overestimation alongside calibration, uncertainty, and applicability do...

Network structure of the WISC–V: Exploratory graph analysis of the 16 subtest full battery within a neurodevelopmental mutualism framework

Lymph node metastasis detection in breast cancer via Triple DAG Neural Networks with Adaptive chaotic Satin Bowerbird optimization

Het-node2vec: second-order random walk sampling for heterogeneous graph embedding

Abstract Many real-world problems are naturally modeled as heterogeneous graphs, where nodes and edges represent multiple types of entities and relations. Existing learning models for heterogeneous graph representation usually depend on the computation of specific, user-defined heterogeneous paths, or on the applicatio...