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A shrinkage adaptive filtering algorithm with graph filter models
In this study, we focus on an adaptive filtering algorithm that utilizes variable step-size and incorporates graph filter models within the realm of...
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A Local Explainability Technique for Graph Neural Topic Models
Topic modelling is a Natural Language Processing (NLP) technique that has gained popularity in the recent past. It identifies word co-occurrence...
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Integrating graph embedding and neural models for improving transition-based dependency parsing
This paper introduces an effective method for improving dependency parsing which is based on a graph embedding model. The model helps extract local...
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Hierarchical Bayesian adaptive lasso methods on exponential random graph models
The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health,...
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Probabilistic graph model and neural network perspective of click models for web search
Click behavior is a typical user behavior in the web search. How to capture and model users’ click behavior has always been a common research topic....
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Heterogeneous graph neural network with graph-data augmentation and adaptive denoising
Heterogeneous graphs are especially important in our daily life, which describe objects and their connections through nodes and edges. For this...
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Enhanced Graph Representations for Graph Convolutional Network Models
Graph Convolutional Network (GCN) is increasingly becoming popular among researchers for its capability of solving the task of classification of...
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Improving graph-based recommendation with unraveled graph learning
Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph...
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LightCapsGNN: light capsule graph neural network for graph classification
Graph neural networks (GNNs) have achieved excellent performances in many graph-related tasks. However, they need appropriate pooling operations to...
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Enhancing attack resilience of cyber-physical systems through state dependency graph models
This paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture...
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Graph foundation model
Graph Foundation Models represent an evolving direction in graph machine learning. Drawing inspiration from the success of Large Language Models in...
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Query-driven graph models in e-commerce
Graph model has been widely used in e-commerce applications to speed up query processing. The graph model’s flexibility has led to the designing of...
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Graph Contrastive Learning with Constrained Graph Data Augmentation
Studies on graph contrastive learning, which is an effective way of self-supervision, have achieved excellent experimental performance. Most existing...
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Incorporating self-attentions into robust spatial-temporal graph representation learning against dynamic graph perturbations
This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against...
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Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks
Hyperspectral image (HSI) classification benefits from effectively handling both spectral and spatial features. However, deep learning (DL) models,...
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A faster deep graph clustering network based on dynamic graph weight update mechanism
Deep graph clustering has attracted considerable attention for its potential in handling complex graph-structured data. However, existing approaches...
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Theories and Models in Graph Comprehension
Graph comprehension is the act of deriving meaning from graphs, an activity grounded in visuospatial reasoning that develops through a combination of...
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Semantic- and relation-based graph neural network for knowledge graph completion
Knowledge graph completion (KGC) refines missing entities, relationships, or attributes from a knowledge graph, which is significant for referral...
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Cardinality estimation for property graph queries with gated learning approach on the graph database
With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the...
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Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
AbstractA smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving...
