Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection
Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty o…
Graph ClassificationMarketingRing Reservoir Neural Networks for Graphs
Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highligh…
Graph ClassificationGraph Homomorphism Convolution
In this paper, we study the graph classification problem from the graph homomorphism perspective. We consider the homomorphisms from $F$ to $G$, where $G$ is a graph of interest (e.g. molecules or social networks) and $F…
General ClassificationGraph ClassificationMER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
Micro-Expression (ME) is the spontaneous, involuntary movement of a face that can reveal the true feeling. Recently, increasing researches have paid attention to this field combing deep learning techniques. Action units …
Graph ClassificationMicro Expression RecognitionMicro-Expression RecognitionRelationMxPool: Multiplex Pooling for Hierarchical Graph Representation Learning
How to utilize deep learning methods for graph classification tasks has attracted considerable research attention in the past few years. Regarding graph classification tasks, the graphs to be classified may have various …
ClassificationClusteringGeneral ClassificationGraph Classification+4Principal Neighbourhood Aggregation for Graph Nets
Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data. Recent work on their expressive power has focused on isomorphism tasks and countable feature sp…
Graph ClassificationGraph RegressionMolecular Property PredictionNode ClassificationPooling in Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods for GCNNs, and combinations of those gra…
ClassificationGeneral ClassificationGraph ClassificationGeometrically Principled Connections in Graph Neural Networks
Graph convolution operators bring the advantages of deep learning to a variety of graph and mesh processing tasks previously deemed out of reach. With their continued success comes the desire to design more powerful arch…
Deep LearningGraph ClassificationLearning Deep Graph Representations via Convolutional Neural Networks
Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. R-convolution graph kernels are positive-semidefinite functions that decomp…
General ClassificationGraph ClassificationBridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some equivalence of the graph convolution proc…
Graph ClassificationGraph LearningNode ClassificationA Collective Learning Framework to Boost GNN Expressiveness
Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighboring nodes rather than dependencies among…
ClassificationGeneral ClassificationGraph ClassificationInductive Learning+2Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
Graph Neural Networks (GNNs) have recently received significant research attention due to their superior performance on a variety of graph-related learning tasks. Most of the current works focus on either static or dynam…
Graph ClassificationGraph LearningLink PredictionAdaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been…
ClassificationFew-Shot LearningGeneral ClassificationGraph Classification+4Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the general GNN models do not aggregate node in…
General ClassificationGraph ClassificationGraph Representation LearningRepresentation LearningUniversal Function Approximation on Graphs
In this work we produce a framework for constructing universal function approximators on graph isomorphism classes. We prove how this framework comes with a collection of theoretically desirable properties and enables no…
Dependency ParsingGraph ClassificationGraph LearningLearning distributed representations of graphs with Geo2DR
We present Geo2DR (Geometric to Distributed Representations), a GPU ready Python library for unsupervised learning on graph-structured data using discrete substructure patterns and neural language models. It contains eff…
GPUGraph ClassificationLanguage ModellingWasserstein-based Graph Alignment
We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, we cas…
Graph ClassificationConvolutional Kernel Networks for Graph-Structured Data
We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional kernel networks to graph-structured data, b…
Graph ClassificationKarate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks. The primary goal of the package is to make community detectio…
BIG-bench Machine LearningClusteringCommunity DetectionGraph Classification+3COPT: Coordinated Optimal Transport for Graph Sketching
We introduce COPT, a novel distance metric between graphs defined via an optimization routine, computing a coordinated pair of optimal transport maps simultaneously. This gives an unsupervised way to learn general-purpos…
Graph Classification