12 research outputs found
Distributed Training of Graph Convolutional Networks
The aim of this work is to develop a fully-distributed algorithmic framework
for training graph convolutional networks (GCNs). The proposed method is able
to exploit the meaningful relational structure of the input data, which are
collected by a set of agents that communicate over a sparse network topology.
After formulating the centralized GCN training problem, we first show how to
make inference in a distributed scenario where the underlying data graph is
split among different agents. Then, we propose a distributed gradient descent
procedure to solve the GCN training problem. The resulting model distributes
computation along three lines: during inference, during back-propagation, and
during optimization. Convergence to stationary solutions of the GCN training
problem is also established under mild conditions. Finally, we propose an
optimization criterion to design the communication topology between agents in
order to match with the graph describing data relationships. A wide set of
numerical results validate our proposal. To the best of our knowledge, this is
the first work combining graph convolutional neural networks with distributed
optimization.Comment: Published on IEEE Transactions on Signal and Information Processing
over Network
Explainability in subgraphs-enhanced Graph Neural Networks
Paper submitted to https://septentrio.uit.no/index.php/nldl/indexRecently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance
the expressive power of Graph Neural Networks
(GNNs), which was proved to be not higher than
the 1-dimensional Weisfeiler-Leman isomorphism
test. The new paradigm suggests using subgraphs
extracted from the input graph to improve the
model’s expressiveness, but the additional complexity exacerbates an already challenging problem in
GNNs: explaining their predictions. In this work,
we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs. The proposed explainer accounts for the contribution of all the different subgraphs and can produce a meaningful explanation that humans can interpret. The experiments that we performed both on real and synthetic datasets show that our framework is successful in explaining the decision process of an SGNN
on graph classification tasks
Explainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been
introduced to enhance the expressive power of Graph Neural Networks (GNNs),
which was proved to be not higher than the 1-dimensional Weisfeiler-Leman
isomorphism test. The new paradigm suggests using subgraphs extracted from the
input graph to improve the model's expressiveness, but the additional
complexity exacerbates an already challenging problem in GNNs: explaining their
predictions. In this work, we adapt PGExplainer, one of the most recent
explainers for GNNs, to SGNNs. The proposed explainer accounts for the
contribution of all the different subgraphs and can produce a meaningful
explanation that humans can interpret. The experiments that we performed both
on real and synthetic datasets show that our framework is successful in
explaining the decision process of an SGNN on graph classification tasks
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several applications, ensuring the fairness of the node (or graph) representations with respect to some protected attributes is crucial for their correct deployment. Yet, fairness in graph deep learning remains underexplored, with few solutions available. In particular, the tendency of similar nodes to cluster on several real-world graphs (i.e., homophily) can dramatically worsen the fairness of these procedures. In this article, we propose a novel biased edge dropout algorithm (FairDrop) to counter-act homophily and improve fairness in graph representation learning. FairDrop can be plugged in easily on many existing algorithms, is efficient, adaptable, and can be combined with other fairness-inducing solutions. After describing the general algorithm, we demonstrate its application on two benchmark tasks, specifically, as a ran..
Explainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance
the expressive power of Graph Neural Networks
(GNNs), which was proved to be not higher than
the 1-dimensional Weisfeiler-Leman isomorphism
test. The new paradigm suggests using subgraphs
extracted from the input graph to improve the
model’s expressiveness, but the additional complexity exacerbates an already challenging problem in
GNNs: explaining their predictions. In this work,
we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs. The proposed explainer accounts for the contribution of all the different subgraphs and can produce a meaningful explanation that humans can interpret. The experiments that we performed both on real and synthetic datasets show that our framework is successful in explaining the decision process of an SGNN
on graph classification tasks
Machine Un-learning: An Overview of Techniques, Applications, and Future Directions
ML applications proliferate across various sectors. Large internet firms employ ML to train intelligent models using vast datasets, including sensitive user information. However, new regulations like GDPR require data removal by businesses. Deleting data from ML models is more complex than databases. Machine Un-learning (MUL), an emerging field, garners academic interest for selectively erasing learned data from ML models. MUL benefits multiple disciplines, enhancing privacy, security, usability, and accuracy. This article reviews MUL’s significance, providing a taxonomy and summarizing key MUL algorithms. We categorize modern MUL models by criteria, including model independence, data driven, and implementation considerations. We explore MUL applications in smart devices and recommendation systems. We also identify open questions and future research areas. This work advances methods for implementing regulations like GDPR and safeguarding user privacy