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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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Improving explainability results of convolutional neural networks in microscopy images
Explaining the predictions of neural networks to comprehend which region of an image influences the most its decision has become an imperative...
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Graph neural networks with selective attention and path reasoning for document-level relation extraction
Document-level Relation Extraction (DocRE) aims to extract relations from multiple sentences simultaneously. Existing graph-based methods adopt...
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Disinformation detection using graph neural networks: a survey
The creation and propagation of disinformation on social media is a growing concern. The widespread dissemination of disinformation can have...
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On Glocal Explainability of Graph Neural Networks
Graph Neural Networks (GNNs) derive outstanding performance in many graph-based tasks, as the model becomes more and more popular, explanation...
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L2XGNN: learning to explain graph neural networks
Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn , a...
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A comparison of graph neural networks for malware classification
Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature...
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Evaluating Link Prediction Explanations for Graph Neural Networks
Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing...
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Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening
Molecular property prediction is a fundamental task in the field of drug discovery. Several works use graph neural networks to leverage molecular...
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ADVISE: ADaptive feature relevance and VISual Explanations for convolutional neural networks
To equip convolutional neural networks (CNNs) with explainability, it is essential to interpret how opaque models make specific decisions, understand...
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Concept Distillation in Graph Neural Networks
The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by...
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Graph similarity learning for change-point detection in dynamic networks
Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectivity, population migrations, and social networks....
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Graph Neural Network Operators: a Review
Graph Neural Networks (GNN) is one of the promising machine learning areas in solving real world problems such as social networks, recommender...
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Polynomial-based graph convolutional neural networks for graph classification
Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggregating scheme, to compute representations of...
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Connected-C \(\textrm{F}^2\) : Learning the Explainability of Graph Neural Network on Counterfactual and Factual Reasoning via Connected Component
Structural data, such as social networks, molecules, citation networks, etc., exists everywhere in various fields. The complex topology makes it...
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Global and session item graph neural network for session-based recommendation
Session-based recommendation algorithm is a research hotspot with economic significance and research value. Most of the algorithms are based on how...
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A novel approach for detecting deep fake videos using graph neural network
Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited...
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Graph Neural Networks for Node Classification
Graph Neural Networks are neural architectures specifically designed for graph-structured data, which have been receiving increasing attention...
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Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications
Video abnormality behavior identification plays a pivotal role in improving the safety and security of surveillance systems by identifying unusual...
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Exploring Multi-Task Learning for Explainability
Machine Learning (ML) model understanding and interpretation is an essential component of several applications in different domains. Several...