AdaGCN uses AdaBoost to efficiently integrate high-order neighbor knowledge in graph neural networks.
problem Efficiently exploring and exploiting knowledge from different hops of neighbors in graph neural networks.
method Incorporates AdaBoost into graph convolutional networks to integrate knowledge from high-order neighbors.
result AdaGCN achieves state-of-the-art prediction performance across different graphs and label rates.
DeepMap learns deep graph representations via CNNs, improving graph classification performance.
problem Quantifying graph similarities for tasks like classification.
method Proposes DeepMap framework extending CNNs to arbitrary graphs, learning dense low-dimensional vectors.
result DeepMap achieves state-of-the-art performance on graph classification benchmarks.
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…
Unified deep learning for graph signals, simplifying existing models.
problem Efficiency of Convolutional Neural Networks on graph signals.
method Unified formalism for existing deep learning models on graph signals.
result Unified formalism simplifies and compares existing models.
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.
Sparse molecular representations improve interpretability in graph neural networks.
problem Difficulty in understanding which molecular graph aspects drive deep learning predictions.
method Constrain weights in a graph convolutional neural network using the Gini index to maximize representation inequality.
result The Gini-constrained approach does not degrade evaluation metrics and allows for interpretable representation combination.
Improves molecular activity prediction using graph convolutional neural networks considering graph distances.
problem Predicting molecular activity using graph convolutional neural networks with improved distance representation.
method Proposed three improvements: modified graph distances, distance-dependent weight matrices, and weighted sum conversion.
result The proposed method slightly outperforms the original weave module in compound activity prediction.
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision. In this paper, we expose and tackl…
PSTN improves traffic condition forecasting with deep neural networks.
problem Challenges in accurately forecasting traffic conditions due to complex spatiotemporal correlations.
method Proposes PSTN with three modules: graph convolutional network, temporal convolutional network, and gated recurrent unit framework.
result Significantly outperforms state-of-the-art benchmarks in short-term traffic conditions forecasting.
Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea proposed almost a decade ago. In this paper, we extend this basic component, following an intuition derived from the well-known convolutional…
Graph convolutional deep kernel machine learns representations for graph tasks.
problem Limited representation learning in infinite-width neural networks.
method Developed a graph convolutional deep kernel machine as an infinite-width limit.
result Representation learning improves performance for heterophilous node classification tasks.
This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neural Network (GCNN) models, the proposed QSGCNN model incorporates the process of identifying transit…
DEMO-Net improves graph neural networks by focusing on node degree.
problem Limited analysis of graph convolution properties and lack of degree-specific graph structure.
method Proposes DEMO-Net, a degree-specific graph neural network that recursively identifies 1-hop neighborhood structures and uses multi-task learning for node representation learning.
result Demonstrates effectiveness and efficiency of DEMO-Net on node and graph classification benchmarks.
Deep learning detects bid-rigging cartels with high accuracy.
problem Detecting bid-rigging cartels using pairwise bidding interactions.
method Convolutional neural networks applied to graphs of normalized bid values.
result Convolutional neural networks achieve around 90% accuracy in classifying collusive and competitive bidding interactions.
Graph neural network framework learns graph representations from node features and local structures.
problem Lack of hierarchical pooling to preserve graph structure in graph neural networks.
method Introduces a pooling operator based on graph Fourier transform to combine node features and local structures.
result Framework $\m$ improves graph classification performance on 6 benchmarks.
New GCNs improve graph classification with deeper multi-scale information.
problem Limited expressive power of existing GCNs.
method Generalized spectral graph convolution and deep GCN architectures, showing equivalence under certain conditions.
result Two new architectures achieve better performance on node classification tasks.
FGNN generalizes graph neural networks to capture higher-order dependencies.
problem Capturing higher-order dependencies in graph-structured data.
method Introducing a factor graph neural network (FGNN) that can represent Max-Product Belief Propagation.
result FGNN effectively represents Max-Product Belief Propagation and performs well on both synthetic and real datasets.
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.
Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise …
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we de…
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation o…
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in v…
Scattering GCN improves graph neural networks by filtering oversmoothing.
problem Oversmoothing in GCNs limits their ability to distinguish graph nodes.
method Augmenting GCNs with geometric scattering transforms and residual convolutions.
result Scattering GCN outperforms GAT in semi-supervised node classification.
Survey on methods to learn graph data representations.
problem Designing optimal Neural Network architectures for arbitrary graphs.
method Review of graph kernel methods, convolutional approaches, graph neural networks, graph embedding, and probabilistic approaches.
result Discussion of various methods for learning graph data representations.
EWS-GCN improves credit scoring by analyzing money transfer connections.
problem Improving credit scoring in transactional banking data.
method Edge Weight-Shared Graph Convolutional Network (EWS-GCN) combining graph and recurrent neural networks.
result EWS-GCN outperforms state-of-the-art models in credit scoring.
DeeperGCN tackles deep GCNs by overcoming vanishing gradient and over-smoothing issues.
problem Vanishing gradient, over-smoothing, and over-fitting issues in deep GCNs.
method DeeperGCN uses differentiable generalized aggregation functions and a novel normalization layer (MsgNorm) to train deep GCNs.
result DeeperGCN significantly boosts performance on large-scale graph learning tasks.
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.
Recently, graph neural networks have been adopted in a wide variety of applications ranging from relational representations to modeling irregular data domains such as point clouds and social graphs. However, the space of graph neural network architectures remains highly fragmented impeding the development of optimized …
Graph Neural Networks improve El Niño forecasts.
problem Improving seasonal forecasting models for ENSO.
method Application of spatiotemporal Graph Neural Networks.
result Preliminary results outperform state-of-the-art systems for 1 and 3-month projections.
IGT learns graph representations without supervision.
problem Building deep unsupervised graph representations.
method Generic complex-valued spectral graph architecture from Fourier transform generalization, greedy concave objective for discriminative and invariant features.
result IGT learns both discriminative and invariant features from graph topology.
New approach to deeper graph neural networks to avoid performance degradation.
problem Performance degradation of graph neural networks when going deeper.
method Decoupling representation transformation and propagation in graph convolution operations.
result Deeper graph neural networks can be used to learn graph node representations from larger receptive fields.
Study compares deep learning models for traffic forecasting, highlighting graph elements' impact.
problem Challenges in forecasting spatial-temporal traffic patterns.
method In-depth comparative study of four deep neural network models with different basic elements.
result Graph attention improves long-term predictions in traffic forecasting models.
Deep networks show less disorder and more monotonic behavior.
problem Understanding the disorder and monotonicity in deep neural networks.
method Computing frustration and analyzing near-monotonicity in signed graphs of deep networks.
result Deep networks exhibit less disorder and more monotonic behavior than expected.
Graph neural networks improve El Niño forecasts.
problem Improving seasonal forecasting accuracy for El Niño-Southern Oscillation.
method Designing a novel graph connectivity learning module to model large-scale spatial interactions with ENSO forecasting.
result Our model \graphino outperforms state-of-the-art models for forecasts up to six months ahead.
Infinitely wide GCNs perform as GPs for graph semi-supervised learning.
problem Graph-based semi-supervised classification with limited labeled data.
method Proposes GPGC model combining GCNs and GPs for semi-supervised learning.
result GPGC outperforms state-of-the-art methods on various datasets.
A new graph pooling method using self-attention improves graph classification performance.
problem Challenges in applying downsampling to graphs in graph neural networks.
method Proposes a self-attention-based graph pooling method.
result Our method achieves superior graph classification performance on benchmark datasets.
Deep GCNII tackles over-smoothing problem in graph convolutional networks.
problem Over-smoothing problem in shallow graph convolutional networks.
method Proposes GCNII with initial residual and identity mapping techniques.
result Deep GCNII outperforms state-of-the-art methods on various tasks.
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications …
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.
Graph neural networks generalize well under certain conditions, explained by learning theory.
problem Understanding why graph neural networks generalize well in transductive inference.
method Analysis of transductive Rademacher complexity to explain generalization properties of graph convolutional networks.
result Transductive Rademacher complexity can explain the generalization of graph convolutional networks for node classification in stochastic block models.
A new method for neural network initialization using graph degeneracy.
problem Improving neural network performance through better initialization.
method Adapted k-hypercore decomposition for neural network initialization.
result k-hypercore outperforms state-of-the-art initialization methods.
A grid layout method for graph classification using CNNs.
problem How to project graphs onto grids for CNNs to work effectively.
method Proposes a novel graph-preserving grid layout (GPGL) using integer programming to minimize topological loss, and solves it approximately with a regularized Kamada-Kawai algorithm.
result Demonstrates the success of the method for graph classification using multi-scale maxout CNNs.
Deep model learns protein interfaces from high-order interactions.
problem Predicting protein interfaces from amino acid pairs.
method Graph neural networks and convolutional neural networks for 2D dense predictions.
result Our method consistently improves interface prediction performance.
Combines BERT and graph CNN for improved text classification.
problem Text classification problems
method Combining BERT embedding and graph convolutional neural network
result Graph CNN model performs better than classical models combined with BERT
Hybrid model combines neural networks and fluid dynamics for efficient, generalized simulations.
problem Inefficient and poor generalization of deep learning approximations of fluid dynamics.
method Combines graph neural networks with a differentiable PDE solver inside a neural network.
result Hybrid model generalizes well to new scenarios and outperforms both neural network and traditional methods.
Graph transformers outperform graph convolutions by preserving community information.
problem Understanding why graph transformers perform well in node-level prediction tasks.
method Analyzing the Gaussian process limits of graph transformers with infinite width and infinite heads.
result Graph transformers maintain discriminative node representations even in deep layers, preventing oversmoothing.
Unified graph scattering transforms improve theoretical properties of graph neural networks.
problem Improving theoretical guarantees for graph neural networks.
method Introducing windowed and non-windowed geometric scattering transforms for graphs.
result Unified family of graph scattering transforms with provable stability and invariance.
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…