Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network defined in the vertex domain. We provi…
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48 results for “Topology Adaptive Graph Convolutional Networks”
Proposes GLNNs for robust semi-supervised classification using adaptive graphs.
problem Adaptive graph learning for robust semi-supervised classification.
method Optimizes graph structure from data and tasks using spectral graph theory and maximum a posteriori estimation.
result GLNNs outperform state-of-the-art approaches in semi-supervised classification.
GCNN research tackles graph data topology and prediction.
problem Graphs' irregularity and complexity make traditional CNN methods unsuitable.
method Review and categorization of GCNN techniques.
result TAGCN approach shows promise for improving graph data prediction.
Unified model generates representations for all nodes in growing graphs.
problem Cold start problem in growing graphs isolates new nodes.
method Generative graph convolutional network that learns adaptive node representations.
result Superior performance on citation network datasets.
Adaptive graph convolution improves attributed graph clustering performance.
problem Joint modeling of graph structures and node attributes is challenging.
method Adaptive graph convolution that captures global cluster structure and selects appropriate order for different graphs.
result Empirical results show our method compares favorably with state-of-the-art methods.
AGE improves graph embedding by smoothing features and iteratively enhancing node embeddings.
problem Challenges in attributed graph embedding, especially in preserving optimal low-pass characteristics and robustness.
method AGE, a novel framework combining Laplacian smoothing and adaptive encoding, addresses these issues.
result AGE consistently outperforms state-of-the-art methods on node clustering and link prediction tasks.
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.
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
problem Handling multi-channel graph signals with limited architectures.
method BankGCN decomposes multi-channel signals into subspaces and uses adapted filters for each subspace.
result BankGCN achieves excellent performance in graph classification on benchmark datasets.
GCN improves fault location in power grids with high accuracy.
problem Fault location in power distribution networks.
method Graph Convolutional Network (GCN) integrating multiple measurements and topology.
result GCN significantly outperforms other machine learning schemes in fault location accuracy.
Graph scattering transforms are stable to metric perturbations of network topology.
problem Stability of graph data representations under metric perturbations.
method Extending scattering transforms to network data using multiresolution graph wavelets and graph convolutions.
result Graph scattering transforms are stable to metric perturbations of the underlying network topology.
Graph Attention Networks improve image classification with superpixels.
problem Classifying images with irregular shapes and edges.
method Transform images into superpixel graphs, then apply GATs.
result GATs outperform other GNN models in image classification.
AGCRN forecasts traffic using adaptive graph and recurrent learning.
problem Forecasting traffic dynamics with complex spatial and temporal correlations.
method Adaptive Graph Convolutional Recurrent Network (AGCRN) with Node Adaptive Parameter Learning (NAPL) and Data Adaptive Graph Generation (DAGG).
result AGCRN outperforms state-of-the-art models without pre-defined graphs.
Graph Convolutional Networks improved with topological features for better accuracy.
problem Improving Graph Convolutional Networks for node classification.
method Using topological features of nodes and adjacency matrices with distant nodes of similar topology.
result Adding topological features to GCN significantly improves accuracy over state-of-the-art methods.
A distributed algorithm for training graph convolutional networks.
problem Training graph convolutional networks with sparse network topology and distributed agents.
method Formulate inference and optimization in a distributed scenario, propose a gradient descent procedure, and design communication topology.
result Convergence to stationary solutions of the GCN training problem under mild conditions.
MDGCN improves hyperspectral image classification by dynamically updating graphs.
problem Traditional CNNs struggle with irregular image regions and class boundaries.
method MDGCN uses dynamic graph convolution on hyperspectral images, adapting to local regions.
result MDGCN outperforms state-of-the-art methods on benchmark datasets.
GmCN adapts GCN feature aggregation to improve graph learning.
problem Fixed neighborhood graph aggregation in GCNs is biased and can be affected by graph structure noises.
method GmCN allows nodes to adaptively select optimal neighbors for feature aggregation.
result GmCN improves graph learning effectiveness through adaptive neighbor selection.
Proposes a new QSGCNN model for graph classification.
problem Information loss and imprecise representation in existing GCN models.
method Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model.
result Demonstrates effectiveness on benchmark graph classification datasets.
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networkscs.LG
Proposes dynamic graph and node feature learning in GCNNs for better adaptability.
problem Fixed graphs for all GCNN layers limit adaptability to node feature structures.
method Dynamic graph and node feature learning using Mahalanobis distance metric.
result Superior performance in point clouds and citation networks.
Adaptive graph auto-encoder improves general data clustering.
problem Extending graph convolution networks to general clustering tasks.
method Adaptive graph construction based on generative perspective, novel decoder design.
result Model performs well in weighted graph scenarios.
Novel graph network learns hierarchical network structure.
problem Lack of information in hierarchical network topology.
method Hierarchical clustering for multiscale decomposition, graph convolutional layers.
result Competitive performance on citation network benchmark.
Proposes a graph pooling method leveraging node proximity for hierarchical graph representation learning.
problem Efficiently exploiting the geometry of graph data for hierarchical representation learning.
method Combines node proximity with kernel representation of topology and node features for adaptive node signal similarities evaluation.
result Achieves state-of-the-art performance on graph classification benchmark datasets.
AdaGCN transfers labels across networks via adversarial domain adaptation and graph convolution.
problem Cross-network node classification with limited labeled data.
method Adversarial domain adaptation and graph convolution.
result AdaGCN successfully transfers labels with low labeled data on source networks and significant domain divergence.
Enhances GCNs' robustness to graph perturbations.
problem Vulnerability of GCNs to graph structure perturbations.
method Generates edge-dithered auxiliary graphs and uses them in an adaptive GCN.
result Significantly improved performance in SSL tasks with noisy inputs and adversarial attacks.
In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the -th order convolution operator and the adaptive filtering module. Importantly, our framework of High-order and Adaptive Graph Convo…
Simplicial neural networks extend graph neural networks to handle higher-order interactions.
problem Handling higher-order interactions in complex data structures.
method Define a convolution operation for simplicial complexes and use it to construct convolutional neural networks.
result SNNs effectively impute missing data in coauthorship complexes.
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are d…
GCNs improve regression tasks by aggregating neighbor signals.
problem GCNs' statistical properties in regression tasks are poorly understood.
method Examined two GCN convolutions and their impact on learning error.
result GCNs have a bias-variance trade-off that depends on neighborhood size and topology.
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph C…
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and con…
Graph neural networks leverage graph filters to learn from network data.
problem Learning from network data with graph structure.
method Characterize graph neural networks using graph signal processing and graph convolutional filters.
result Graph neural networks have permutation equivariance and stability to topology changes.
Develops BASGCN for graph classification with improved feature learning.
problem Graph classification with information loss and imprecise representation.
method Transforms graphs into grid structures and defines a new spatial graph convolution operation.
result Reduces information loss and improves feature representation compared to existing models.
GNNs maintain stability under minor graph topology changes.
problem Stability of GNNs to small graph topology changes.
method Proved stability of GNNs with integral Lipschitz filters.
result GNNs output change is bounded by relative graph topology change.
Enhances GCNs to resist graph attacks.
problem Vulnerability of GCNs to topological attacks.
method Introduces a new robust convolution operator and training paradigm.
result Improves adversarial robustness and performance.
The paper introduces a pooling mechanism for graph CNNs using NMF.
problem Pooling in graph structured data for efficient computation.
method Non-negative matrix factorization for node pooling.
result The pooling mechanism improves graph classification performance.
Deep Learning methods, specifically convolutional neural networks (CNNs), have seen a lot of success in the domain of image-based data, where the data offers a clearly structured topology in the regular lattice of pixels. This 4-neighbourhood topological simplicity makes the application of convolutional masks straightf…
AP-GCN improves graph data processing by dynamically deciding communication steps.
problem Designing efficient and adaptive graph convolutional networks.
method Adaptive halting units that decide the number of communication steps at each node.
result AP-GCN achieves state-of-the-art results with minimal additional parameters.
GNNs are stable to small topology changes and distinguish high frequencies.
problem Impact of topology changes on GNN outputs.
method Permutation equivariance, integral Lipschitz filters, frequency mixing.
result GNNs are stable to small topology changes and distinguish high frequencies.
Graph Convolutional Networks improve performance on complex data.
problem Processing high-dimensional, graph-based data for automation.
method Enhanced existing Graph Convolutional Network models with four improvements.
result Significant performance improvements on four benchmark datasets.
PAN improves graph neural networks using path integrals.
problem Efficiency and performance of graph neural networks.
method PAN uses path integrals to generalize graph Laplacian, incorporating all paths between nodes.
result PAN achieves state-of-the-art performance on benchmark tasks.
DANE adapts network embeddings across multiple domains.
problem Learning embeddings for multiple networks without transferability.
method Graph Convolutional Network with adversarial learning.
result DANE achieves superior performance in cross-network domain adaptation.
SWAG uses graph convolutions to recommend products.
problem Product recommendation using graph-structured data.
method Graph Convolutional Network (GCN) with weighted random walks and aggregations.
result Graph-based approach improves product recommendations.
Graph neural networks can be adapted to new graphs with a limit object called graphon NNs.
problem Transferability of graph neural networks across different graphs.
method Introduced graphon NNs as limit objects of GNNs and proved a bound on the difference between GNN and graphon-NN outputs.
result The bound on the difference between GNN and graphon-NN outputs vanishes with growing number of nodes if the graph convolutional filters are bandlimited.
CTGCN learns dynamic graph embeddings preserving both local and global graph structure.
problem Learning node representations for evolving graphs while preserving both local and global graph structure.
method CTGCN uses k-core based temporal graph convolutional network to learn dynamic graph embeddings.
result CTGCN outperforms existing methods in link prediction and structural role classification.
Dual-attention GCN improves text classification by adapting to textual complexity.
problem Challenges in learning discriminative features from texts due to graph variants.
method Proposes a dual-attention GCN with connection-attention and hop-attention mechanisms.
result Achieves state-of-the-art performance on text classification tasks.
T-GCN predicts traffic using neural networks for spatial and temporal data.
problem Accurate real-time traffic forecasting in urban networks.
method Combines GCN for spatial and GRU for temporal data analysis.
result T-GCN outperforms state-of-the-art baselines on real-world traffic datasets.
LFGCN uses Levy Flights for graph semi-supervised learning.
problem Semi-supervised learning on graphs with improved performance.
method Lévy Flights into random walks, preferential P-DropEdge method.
result Significant improvement in classification performance, especially for heterogeneous graphs.
Proposes clique pooling for graph classification.
problem Graph classification challenges.
method Clique-based graph pooling within GCN and GraphSAGE.
result Competitive performance on graph classification benchmarks.
Detects financial fraud schemes in networks using graph structure learning.
problem Identifying financial fraud schemes in complex networks.
method Adapting dictionary learning to network topologies, imposing Laplacian structure on dictionaries.
result Proposed methods effectively represent graph structure information for anomaly detection.