A new method combines topological features with graph convolutional networks for improved paper classification.
problem Classifying papers based on their content and structure.
method Combining topological features of nodes with information propagation through Graph Convolutional Networks (GCN).
result The method improves classification accuracy on CiteSeer and Cora datasets, matching or exceeding text-based classification results.
A new GCN model learns higher-order neighbors without explicit adjacency matrix computation.
problem GCN's performance drops for deeper structures due to limited neighborhood information.
method Assumes higher-order neighbors are similar to first-order neighbors, learns weights through Lasso to minimize feature loss.
result HWGCN achieves state-of-the-art results on various datasets.
This paper explores the recently proposed Graph Convolutional Network architecture proposed in (Kipf & Welling, 2016) The key points of their work is summarized and their results are reproduced. Graph regularization and alternative graph convolution approaches are explored. I find that explicit graph regularization was…
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to lear…
GWNN uses graph wavelets for efficient graph CNNs.
problem Spectral graph CNNs' high computational cost and lack of interpretability.
method Graph wavelet transform for efficient graph convolution.
result GWNN significantly outperforms spectral graph CNNs.
We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs). BVAT addresses the shortcoming of GCNs that do not consider the smoothness of the model's output distribution against local perturbations around the input. We propose two algorithms, sample-based…
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over thei…
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries th…
New graph representation learning network improves scalability and feature integration.
problem Scalability and feature integration in graph neural networks for large, dense graphs.
method Adaptive sampling of neighbours based on weighted multi-step transition probabilities.
result Comparable or better results on various graph benchmarks.
Graph autoencoders improve node embeddings using random walk regularization.
problem Graph autoencoders' reconstruction loss ignores latent representation distribution.
method Random walk regularization to improve latent representations.
result The method achieves state-of-the-art accuracy on link prediction tasks.
MMGAN creates graphs with higher-order motifs for better network simulation.
problem Generative models fail to capture higher-order connectivity patterns in real-world networks.
method Combines multiple biased random walks to capture different motif structures.
result Outperforms NetGAN at creating graphs with accurate network motif statistics.
Develops neural network for directed hypergraphs for node classification.
problem Irregular data structure, particularly directed graphs.
method Directed hypergraph neural network and semi-supervised learning method.
result Novel directed hypergraph neural network achieves highest accuracies on node classification tasks.
GraphMix improves GNNs for semi-supervised learning.
problem Improving GNNs for semi-supervised classification.
method Parameter sharing and interpolation-based regularization.
result GraphMix improves generalization bounds and state-of-the-art performance.
We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words.…
Simple linear model outperforms GCN in graph AE tasks.
problem Challenging tasks like link prediction and node clustering.
method Replaced GCN with a simple linear model on adjacency matrix.
result Simple model consistently reaches competitive performances.
Estimates binary labels from dependent data using Markov Random Fields.
problem Statistical estimation from dependent data across spatial, temporal, and social domains.
method Modeling dependencies as Markov Random Fields and providing efficient estimation algorithms.
result Statistically efficient estimation rates for Ising models from a single sample.
GraphNAS uses reinforcement learning to automatically design graph neural network architectures.
problem Designing effective graph neural network architectures requires manual work and domain knowledge.
method GraphNAS generates variable-length strings to describe architectures and trains a recurrent network with reinforcement learning to maximize validation accuracy.
result GraphNAS achieves consistently better performance on various citation and protein networks.
Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution, the classic GCN can not take full advantage of unlabeled data, especially when the unlabeled node is far from labeled ones. To capitalize on…
Proposes GIL for semi-supervised graph classification.
problem Semi-supervised classification of graph data with limited labeled nodes.
method Graph Inference Learning framework that learns node label inference from graph topology.
result Significantly improves semi-supervised node classification performance.
Enhances neural networks with logical knowledge for better performance.
problem Improving neural network performance with logical knowledge.
method Integrating logical knowledge into neural networks through a new final layer with learnable clause weights.
result KENN outperforms other methods in collective classification tasks with relational data.
New aggregation method improves GNN robustness to structural perturbations.
problem Graph Neural Networks (GNNs) are vulnerable to adversarial attacks that manipulate graph structure.
method Proposes a robust aggregation function with a breakdown point of 0.5, inspired by robust statistics.
result Improves GNN robustness by a factor of 3 on Cora ML and 5.5 on Citeseer, and 8 for low-degree nodes.
Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed …
SF-GCN improves semi-supervised classification by fusing multi-view data structures.
problem Semi-supervised classification challenges due to multi-view data diversity and complexity.
method Structure fusion based on graph convolutional networks (SF-GCN) that balances specificity and commonality.
result SF-GCN outperforms state-of-the-art methods on citation networks datasets.
GG-SAGE predicts links in directed graphs with attributes, outperforming existing methods.
problem Predicting links in directed graphs with node attributes.
method Gravity-GraphSAGE, a modified GraphSAGE model with a gravity-inspired decoder.
result GG-SAGE outperforms state-of-the-art GDL link prediction techniques.
Extends feature selection to GNNs, improving accuracy and feature ranking.
problem Improving feature selection in Graph Neural Networks (GNNs).
method Implemented a feature selection algorithm using Gumbel Softmax for ranking and selecting features in GNNs.
result Selected 225 features out of 1433 for the Cora dataset, improving classification accuracy.
Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (\eg articles with citation link tend to be in the same class), graph neural networks could be more sensiti…
MAGNA improves graph neural networks by incorporating multi-hop context information.
problem Limited context in current graph neural networks.
method Diffuses attention scores across the network, accounting for all paths between nodes.
result State-of-the-art performance on node classification and knowledge graph completion benchmarks.
Unified framework sparsifies GNNs for faster inference on large graphs.
problem Space and computational bottlenecks in GNNs due to graph size and connectivity.
method Unified GNN sparsification (UGS) framework that prunes graph adjacency matrix and model weights.
result Graph lottery tickets (GLTs) can be trained in isolation to match full model performance.