Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Coloring graph neural networks for node disambiguation
In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks(MPNNs). More specifically, we introduce a graph neural network…
Graph ClassificationGraph Neural NetworkBayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
Deep learning models are full of hyperparameters, which are set manually before the learning process can start. To find the best configuration for these hyperparameters in such a high dimensional space, with time-consumi…
Bayesian OptimizationGaussian ProcessesGraph ClassificationHyperparameter OptimizationSparse Graph Attention Networks
Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many practical predictive tasks, such as node cla…
ClassificationGeneral ClassificationGraph AttentionGraph Classification+4Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Predicting a user's preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e-commerce and media stream. Recent resea…
Graph ClassificationRecommendation SystemsSession-Based RecommendationsIndependence Promoted Graph Disentangled Networks
We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node representation by describing its neighborhood …
ClusteringGraph ClassificationGraph ClusteringRepresentation LearningScalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global properties, suffer from significant structure i…
Graph ClassificationGraph EmbeddingASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress…
ClassificationGeneral ClassificationGraph ClassificationLink Prediction+1Hierarchical Graph Pooling with Structure Learning
Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing…
Graph ClassificationGraph Neural NetworkRepresentation LearningA Hierarchy of Graph Neural Networks Based on Learnable Local Features
Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been proposed, but there is limited understanding…
General ClassificationGraph ClassificationGraph RegressionNode ClassificationComposition-based Multi-Relational Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs. Multi-relational graphs are…
General ClassificationGraph ClassificationGraph EmbeddingKnowledge Graph Embedding+2Fundamental Limits of Deep Graph Convolutional Networks
Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate the capabilities and limitations of GCNs, we investigate their power, as a function of their number of layers, …
Graph ClassificationGraph Representation LearningRepresentation LearningBayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings. Despite …
Bayesian InferenceGeneral ClassificationGraph ClassificationGraph Learning+2Understanding Isomorphism Bias in Graph Data Sets
In recent years there has been a rapid increase in classification methods on graph structured data. Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models w…
General ClassificationGraph ClassificationHierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations. In t…
Graph ClassificationRepresentation LearningDeep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction
Tiered graph autoencoders provide the architecture and mechanisms for learning tiered latent representations and latent spaces for molecular graphs that explicitly represent and utilize groups (e.g., functional groups). …
General ClassificationGraph ClassificationPredictionTemporal Graph Kernels for Classifying Dissemination Processes
Many real-world graphs or networks are temporal, e.g., in a social network persons only interact at specific points in time. This information directs dissemination processes on the network, such as the spread of rumors, …
General ClassificationGraph ClassificationRethinking Kernel Methods for Node Representation Learning on Graphs
Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which is a related problem to graph represent…
ClassificationGeneral ClassificationGraph ClassificationGraph Representation Learning+4Evolution of Graph Classifiers
Architecture design and hyperparameter selection for deep neural networks often involves guesswork. The parameter space is too large to try all possibilities, meaning one often settles for a suboptimal solution. Some wor…
Graph ClassificationGraph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection
Neural networks are increasingly used for graph classification in a variety of contexts. Social media is a critical application area in this space, however the characteristics of social media graphs differ from those see…
General ClassificationGraph ClassificationAttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism
Graph convolutional networks (GCNs) are potentially short of the ability to learn hierarchical representation for graph embedding, which holds them back in the graph classification task. Here, we propose AttPool, which i…
General ClassificationGraph ClassificationGraph Embedding