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Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction
Credit assessment for Small and Medium-sized Enterprises (SMEs) is of great interest to financial institutions such as commercial banks …
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Task-adaptive Neural Process for User Cold-Start Recommendation
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of …
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Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled …
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Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the …
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Graph Geometry Interaction Learning
While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the …
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Graph Stochastic Neural Networks for Semi-supervised Learning
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most …
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Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure
Graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent …
OpenWGL: Open-World Graph Learning
In traditional graph learning tasks, such as node classification, the learning is carried out in a closed-world setting where the …
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including …
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Grounding Visual Concepts for Multimedia Event Detection and Multimedia Event Captioning in Zero-shot Setting
The flourishing of social media platforms requires techniques for understanding the content of media on a large scale. However, …
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