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Found 18 papers, 12 papers with code
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
Leveraging these modified representations, DEGNN subsequently addresses downstream tasks, ensuring robustness against noise present in both edges and node features of real-world graphs.
Future-Proofing Class Incremental Learning
Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable.
Class-Incremental Learning using Diffusion Model for Distillation and Replay
Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and ImageNet demonstrate how this new approach can be used to further improve the performance of state-of-the-art methods for class-incremental learning on large scale datasets.
Feature Selection: Key to Enhance Node Classification with Graph Neural Networks
Graphs help to define the relationships between entities in the data.
Ranked #1 on
Node Classification
on twitch-gamers
Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node Classification
With extensive experiments, we show that our proposed model outperforms the state-of-the-art GNN models with remarkable improvements up to 27. 8%.
Modularity Optimization as a Training Criterion for Graph Neural Networks
Graph convolution is a recent scalable method for performing deep feature learning on attributed graphs by aggregating local node information over multiple layers.
Leaping Through Time with Gradient-based Adaptation for Recommendation
Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies.
Simplifying approach to Node Classification in Graph Neural Networks
In this work, we decouple the node feature aggregation step and depth of graph neural network, and empirically analyze how different aggregated features play a role in prediction performance.
Natural Image Reconstruction from fMRI using Deep Learning: A Survey
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain.
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph
To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations.
Improving Graph Neural Networks with Simple Architecture Design
Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model outperforms other state of the art GNN models and achieves up to 64% improvements in accuracy on node classification tasks.
Ranked #2 on
Node Classification
on Cornell
Balanced softmax cross-entropy for incremental learning with and without memory
When incrementally trained on new classes, deep neural networks are subject to catastrophic forgetting which leads to an extreme deterioration of their performance on the old classes while learning the new ones.
Adaptive Stacked Graph Filter
We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully-connected weights versus trainable polynomial coefficients.
Stacked Graph Filter
We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully connected weights versus trainable polynomial coefficients.
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning
In this paper, we propose MetAL, an AL approach that selects unlabeled instances that directly improve the future performance of a classification model.
Graph Convolutional Networks for Graphs Containing Missing Features
Notably, our approach does not increase the computational complexity of GCN and it is consistent with GCN when the features are complete.
Learning Graph Neural Networks with Noisy Labels
We study the robustness to symmetric label noise of GNNs training procedures.
Exploring Partially Observed Networks with Nonparametric Bandits
We formulate this problem as an exploration-exploitation problem and propose a novel nonparametric multi-arm bandit (MAB) algorithm for identifying which nodes to be queried.
