Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Universal Graph Transformer Self-Attention Networks
The transformer self-attention network has been extensively used in research domains such as computer vision, image processing, and natural language processing. But it has not been actively used in graph neural networks …
General ClassificationGraph ClassificationGraph EmbeddingGraph Representation LearningGraph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs
Graph convolutional networks (GCNs) suffer from the irregularity of graphs, while more widely-used convolutional neural networks (CNNs) benefit from regular grids. To bridge the gap between GCN and CNN, in contrast to pr…
Data AugmentationGeneral ClassificationGraph ClassificationHaar Graph Pooling
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs adapt to input graphs of varying size an…
General ClassificationGraph ClassificationregressionMincut Pooling in Graph Neural Networks
The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of …
Graph ClassificationEmpowering Graph Representation Learning with Paired Training and Graph Co-Attention
Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.…
Graph ClassificationGraph Neural NetworkGraph RegressionGraph Representation Learning+3Unsupervised Universal Self-Attention Network for Graph Classification
Existing graph embedding models often have weaknesses in exploiting graph structure similarities, potential dependencies among nodes and global network properties. To this end, we present U2GAN, a novel unsupervised mode…
ClassificationGraph ClassificationGraph EmbeddingOrdinary differential equations on graph networks
Recently various neural networks have been proposed for irregularly structured data such as graphs and manifolds. To our knowledge, all existing graph networks have discrete depth. Inspired by neural ordinary differentia…
Graph ClassificationGraph Neural NetworkNode ClassificationUnsupervised Hierarchical Graph Representation Learning with Variational Bayes
Hierarchical graph representation learning is an emerging subject owing to the increasingly popular adoption of graph neural networks in machine learning and applications. Loosely speaking, work under this umbrella falls…
Graph ClassificationGraph Representation LearningRepresentation LearningTopology-Aware Pooling via Graph Attention
Pooling operations have shown to be effective on various tasks in computer vision and natural language processing. One challenge of performing pooling operations on graph data is the lack of locality that is not well-def…
Graph AttentionGraph ClassificationDiagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation
Graph convolutional networks (GCNs) and their variants have generalized deep learning methods into non-Euclidean graph data, bringing a substantial improvement on many graph mining tasks. In this paper, we revisit the ma…
Graph AttentionGraph ClassificationGraph MiningSubgraph Attention for Node Classification and Hierarchical Graph Pooling
Graph neural networks have gained significant interest from the research community for both node classification within a graph and graph classification within a set of graphs. Attention mechanism applied on the neighborh…
ClassificationGraph ClassificationNode ClassificationHow can we generalise learning distributed representations of graphs?
We propose a general framework to construct unsupervised models capable of learning distributed representations of discrete structures such as graphs based on R-Convolution kernels and distributed semantics research. Our…
Binary ClassificationClassificationGraph ClassificationMulti-class ClassificationHaarPooling: Graph Pooling with Compressive Haar Basis
Deep Graph Neural Networks (GNNs) are instrumental in graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs adapt to input graphs of varying size and …
Graph ClassificationLearning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
Learning powerful data embeddings has become a center piece in machine learning, especially in natural language processing and computer vision domains. The crux of these embeddings is that they are pretrained on huge cor…
DecoderGraph ClassificationGraph EmbeddingGraph Learning+2A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks
The paper discusses a pooling mechanism to induce subsampling in graph structured data and introduces it as a component of a graph convolutional neural network. The pooling mechanism builds on the Non-Negative Matrix Fac…
Graph ClassificationNEAR: Neighborhood Edge AggregatoR for Graph Classification
Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis and data mi…
ClassificationGeneral ClassificationGraph ClassificationDeep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning
Kernels for structured data are commonly obtained by decomposing objects into their parts and adding up the similarities between all pairs of parts measured by a base kernel. Assignment kernels are based on an optimal bi…
General ClassificationGraph ClassificationvalidInvariant embedding for graph classification
Learning on graphs requires a graph feature representation able to discriminate among different graphs while being amenable to fast computation. The graph isomorphism problem tells us that fast representation of graphs i…
ClassificationGeneral ClassificationGraph ClassificationGraph EmbeddingCensNet: Convolution with Edge-Node Switching in Graph Neural Networks
In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a gene…
Graph ClassificationGraph EmbeddingGraph Neural NetworkGraph Regression+2HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing int…
Graph AttentionGraph ClassificationNode ClassificationPrediction+2