## What is/are Graph Classification?

Graph Classification - Then, we deal with the Visual Place Recognition problem as a graph classification problem.^{[1]}Graph classification is an important problem with applications across many domains, like chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods.

^{[2]}A new way to solve the graph classification problem is addressed.

^{[3]}Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc.

^{[4]}Finally, we create a model by deploying the Weisfeiler Lehman graph kernel for graph classification on our labeled dataset.

^{[5]}Nevertheless, despite their rapid pace of development, much of the work on GNNs has focused on graph classification and embedding techniques, largely ignoring regression tasks over graph data.

^{[6]}Graph classification has been widely used for knowledge discovery in numerous practical application scenarios, such as social networks and protein-protein interaction networks.

^{[7]}We perform a spectral analysis to study the filtering effect of the proposed ARMA layer and report experiments on four downstream tasks: semi-supervised node classification, graph signal classification, graph classification, and graph regression.

^{[8]}Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models.

^{[9]}In this paper, we study the graph classification problem in vertex-labeled graphs.

^{[10]}Experimental results on two typical graph pattern-recognition tasks, including node classification and graph classification, demonstrate the necessity and effectiveness of the proposed strategies for graph message-passing neural networks.

^{[11]}For graph classification tasks, existing work on graph classification mainly focuses on two aspects of graph similarity: physical structure and practical property.

^{[12]}The framework of subgraph classification and mapping reduces the computation workload, and the classifier based on joint feature of gray level co-occurrence matrix, local binary pattern and support vector machine achieves a low detection false alarm.

^{[13]}Graph classification is a critical research problem in many applications from different domains.

^{[14]}Hence, we propose a supervised distance metric learning method for the graph classification problem.

^{[15]}In many graph classification applications, GCN-based approaches have outperformed traditional methods.

^{[16]}In this paper, we propose a method to learn the weights of subtree patterns in the framework of WWL kernels, the state of the art method for graph classification task [14].

^{[17]}For graph classification, different pooling techniques are introduced, but none of them has considered both neighborhood of the node and the long-range dependencies of the node.

^{[18]}This work uses a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input.

^{[19]}This paper focuses on two fundamental graph recognition tasks: node classification and graph classification.

^{[20]}The proposed framework combines contextual flow graphs and a deep graph convolution neural network, which leverages the latest and most popular techniques for code embedding and graph classification.

^{[21]}This study submits a new topology of a deep learning network for chest radiograph classification.

^{[22]}Recently, a subgraph network (SGN) model is proposed to study the potential structure among motifs, and it was found that the integration of SGN can enhance a series of graph classification methods.

^{[23]}In an empirical evaluation we show that it can be highly beneficial to focus on these stable parts of graphs during graph classification.

^{[24]}Extensive experiments are conducted on real-world datasets, which show that PGON outperforms other state-of-the-art methods on both graph classification and graph interaction prediction tasks.

^{[25]}It turned out that graph classification is much simpler than the group one.

^{[26]}We evaluate our ideas on fine-grained recognition, scene recognition, and material classification, as well as in few-shot learning and graph classification.

^{[27]}In recent years, many researchers have started to construct Graph Neural Networks (GNNs) to deal with graph classification task.

^{[28]}At present, some promising methods have been proposed to transform time series classification into graph classification by mapping time series to graphs.

^{[29]}txt and paragraph classification scheme based on the business curriculum.

^{[30]}Graph similarity learning, which measures the similarities between a pair of graph-structured objects, lies at the core of various machine learning tasks such as graph classification, similarity search, etc.

^{[31]}Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction.

^{[32]}Consequently, they have enhanced the performance of many graph-related tasks such as node classification and graph classification.

^{[33]}Filtration curves are highly efficient to compute and lead to expressive representations of graphs, which we demonstrate on graph classification benchmark datasets.

^{[34]}Extensive experiments on various benchmark datasets strongly demonstrate the effectiveness, leading to superior performance for graph classification and regression tasks.

^{[35]}For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs.

^{[36]}We apply our framework to graph classification problems and obtain performances competitive with other persistence-based architectures.

^{[37]}RESULTS Out of 63,780 radiograph classifications made by 264 physicians, 31.

^{[38]}Additionally, we employ attention mechanism to adaptively determine the contribution of subgraphs’ representations at varying levels to graph classification and integrate them to perform the cross-scale graph level representation.

^{[39]}In this paper, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification.

^{[40]}In order to give guidelines to make full use of the GNN for graph classification, this paper attempts to analyze the state-of-the-art methods of the GNN and provide practicable guidelines for applications.

^{[41]}Graph neural networks (GNNs) have achieved state-of-the-art performance on graph classification tasks.

^{[42]}Graph classification is a challenging problem, which attracts more and more attention.

^{[43]}Using TIG, we turn DApp fingerprinting into a graph classification problem and design a powerful GNN-based classifier.

^{[44]}In this work, we use a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input.

^{[45]}The proposed strategy achieves superior or competitive performance in graph classification on a collection of public graph benchmark data sets and superpixel-induced image graph data sets.

^{[46]}Research on graph classification tasks based on graph neural networks has attracted wide attention.

^{[47]}Further, a broad learning system (BLS) is introduced into graph classification, which fully utilizes the information provided by the S2GNs of different sampling strategies and thus can capture various aspects of the network structure more efficiently.

^{[48]}Computational results show that the proposed algorithm is able to outperform the vast majority of current approaches for graph classification, while at the same time returning a grey-box model, interpretable by field-experts.

^{[49]}Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc.

^{[50]}

## node classification link

Graph Neural Networks (GNNs) are powerful tools for modeling graph-structured data to solve the tasks such as node classification, link prediction along with graph classification.^{[1]}It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years.

^{[2]}; therefore, tf_geometric can be used for a variety of graph deep learning tasks, such as node classification, link prediction, and graph classification.

^{[3]}Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems.

^{[4]}Some of the interesting and useful applications on these graphs are graph classification, node classification, link prediction, etc.

^{[5]}, node classification, link prediction, community detection, graph classification and graph clustering.

^{[6]}

## graph convolutional network

Since Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points, we use them as a building block to achieve competitive results on standard semi-supervised graph classification tasks.^{[1]}We also evaluate a model-agnostic manner in our proposed method for graph classification tasks and prediction models such as graph convolutional networks (GCNs) or support vector machines (SVM) with graph kernels.

^{[2]}Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.

^{[3]}Our model is mainly composed of three parts: i) flexibility-aware graph construction; ii) overlapping subgraph clustering; iii) graph convolutional network-based subgraph classification.

^{[4]}Graph convolutional networks are widely used in graph-based applications such as graph classification and segmentation.

^{[5]}

## Several Graph Classification

We evaluate the proposed method on several graph classification datasets, and manage to demonstrate comparable accuracy with the state-of-the-art on MUTAG, PTC-MR and NCI1 datasets.^{[1]}The proposed kernel is evaluated on several graph classification datasets.

^{[2]}Experimental results on several graph classification datasets show that the proposed ML-GCN outperforms four state-of-the-art methods.

^{[3]}

## Binary Graph Classification

We evaluate the learned representations using several real-world datasets on the binary graph classification task.^{[1]}We formulate the predictive model as a binary graph classification problem with a set of graph kernels proposed to capture different aspects of graph structures through deep neural networks.

^{[2]}

## Challenging Graph Classification

In this work, we demonstrate that LRP models can be used on challenging graph classification tasks to provide both state-of-the-art performance and interpretability, through the detection of the relevant substructures used by the network to make its decisions.^{[1]}Our experimental results show that Deep Divergence Graph Kernels can learn an unsupervised alignment between graphs, and that the learned representations achieve competitive results when used as features on a number of challenging graph classification tasks.

^{[2]}

## Existing Graph Classification

In our experiments, we empirically verify superior or comparable prediction performance of IGML to other existing graph classification methods which do not have clear interpretability.^{[1]}Existing graph classification methods have focused on either graph kernels or frequent subgraph patterns for classification.

^{[2]}

## Brain Graph Classification

New method In this paper, we propose to build a unified brain graph classification model trained on unpaired multimodal brain graphs, which can classify any brain graph of any size.^{[1]}Hence, the connectome vectorization might cause losing its topological structure which can be leveraged to boost brain graph classification for diagnosis.

^{[2]}

## graph classification task

Since Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points, we use them as a building block to achieve competitive results on standard semi-supervised graph classification tasks.^{[1]}For graph classification tasks, existing work on graph classification mainly focuses on two aspects of graph similarity: physical structure and practical property.

^{[2]}In this paper, we propose a method to learn the weights of subtree patterns in the framework of WWL kernels, the state of the art method for graph classification task [14].

^{[3]}In this paper, we propose a novel quantum graph convolutional neural network (QGCN) model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning.

^{[4]}We also evaluate a model-agnostic manner in our proposed method for graph classification tasks and prediction models such as graph convolutional networks (GCNs) or support vector machines (SVM) with graph kernels.

^{[5]}In recent years, many researchers have started to construct Graph Neural Networks (GNNs) to deal with graph classification task.

^{[6]}For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs.

^{[7]}Graph neural networks (GNNs) have achieved state-of-the-art performance on graph classification tasks.

^{[8]}In this work, we demonstrate that LRP models can be used on challenging graph classification tasks to provide both state-of-the-art performance and interpretability, through the detection of the relevant substructures used by the network to make its decisions.

^{[9]}Experiments demonstrate that the PHD is an effective pre-training strategy that offers comparable or superior performance on 13 graph classification tasks compared with state-of-the-art strategies, and achieves notable improvements when combined with node-level strategies.

^{[10]}Research on graph classification tasks based on graph neural networks has attracted wide attention.

^{[11]}Graph neural networks (GNNs) have recently gained much attention for node and graph classification tasks on graph-structured data.

^{[12]}We demonstrate our model on different datasets in comparison with a comprehensive list of up-to-date state-of-the-art baselines, and experiments show that our work is superior in real-world graph classification tasks.

^{[13]}For graph classification tasks, many methods use a common strategy to aggregate information of vertex neighbors.

^{[14]}In this formalism, a link prediction problem is converted to a graph classification task.

^{[15]}Extensive experiments on eight benchmark datasets for node and graph classification tasks demonstrate the effectiveness of the proposed methods in comparison with the state of the art.

^{[16]}Results on graph classification tasks demonstrate that our methods achieve consistently better performance than previous methods.

^{[17]}In addition to the interpretability of the sampled nodes provided by our method, the experimental results both on stochastic block models and real-world dataset benchmarks show that our method achieves competitive results compared to the state-of-the-art in the graph classification task.

^{[18]}To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task.

^{[19]}In this paper, we present a general framework to estimate the network entropy that is represented by means of an undirected graph and subsequently employ this framework for graph classification tasks.

^{[20]}These approaches have two disadvantages in the graph classification task: (1)when only the largest sub-graph structure (k-hop neighbor) is used for neighborhood aggregation, a large amount of early-stage information is lost during the graph convolution step; (2) simple average/sum pooling or max pooling utilized, which loses the characteristics of each node and the topology between nodes.

^{[21]}We evaluate the learned representations using several real-world datasets on the binary graph classification task.

^{[22]}Our experimental results show that Deep Divergence Graph Kernels can learn an unsupervised alignment between graphs, and that the learned representations achieve competitive results when used as features on a number of challenging graph classification tasks.

^{[23]}Therefore, we propose to aggregate the role representations to describe whole graphs for graph classification tasks.

^{[24]}We demonstrate the efficacy of our approach with a graph classification task using two real-world datasets of animal behaviour and brain networks.

^{[25]}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.

^{[26]}We set up a graph classification task and we provide empirical evidence that: (1) our similarity measure can effectively incorporate the edge directionality information, leading to a significant improvement in classification accuracy; (2) the choice of the quantum walk Hamiltonian does not have a significant effect on the classification accuracy; (3) the addition of node-level topological information improves the classification accuracy in some but not all cases.

^{[27]}The attention module enriches our model in order to focus on the subgraphs that are crucial for the purpose of a graph classification task.

^{[28]}To apply graph neural networks for the graph classification task, approaches to generate thegraph representation from node representations are demanded.

^{[29]}

## graph classification problem

Then, we deal with the Visual Place Recognition problem as a graph classification problem.^{[1]}A new way to solve the graph classification problem is addressed.

^{[2]}In this paper, we study the graph classification problem in vertex-labeled graphs.

^{[3]}Hence, we propose a supervised distance metric learning method for the graph classification problem.

^{[4]}Even though numerous works focus on the few-shot learning issue by combining meta-learning, there are still limits to traditional graph classification problems.

^{[5]}We apply our framework to graph classification problems and obtain performances competitive with other persistence-based architectures.

^{[6]}Using TIG, we turn DApp fingerprinting into a graph classification problem and design a powerful GNN-based classifier.

^{[7]}In this preliminary work, we propose to approach the task of Named Entity Recognition, which is traditionally viewed as a Sequence Tagging problem, as a Graph Classification problem, where every word is represented as a node in a graph.

^{[8]}Many real-world problems can be abstracted into graph classification problems.

^{[9]}To alleviate this problem, we formulate self-localization as a graph classification problem and attempt to use the graph convolutional neural network (GCN) as a graph classification engine.

^{[10]}We formulate the next item recommendation within the session as a graph classification problem.

^{[11]}We formulate the next item recommendation within the session as a graph classification problem.

^{[12]}In the graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories.

^{[13]}Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously.

^{[14]}We formulate the predictive model as a binary graph classification problem with a set of graph kernels proposed to capture different aspects of graph structures through deep neural networks.

^{[15]}We formulate the program reidentification as a graph classification problem and develop an effective attentional heterogeneous graph embedding algorithm to solve it.

^{[16]}In recent years, Graph Convolutional Network (GCN) have been successfully applied to many graph classification problems.

^{[17]}

## graph classification method

This work uses a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input.^{[1]}Recently, a subgraph network (SGN) model is proposed to study the potential structure among motifs, and it was found that the integration of SGN can enhance a series of graph classification methods.

^{[2]}In this work, we use a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input.

^{[3]}Our alternate approach is a physics-based machine learning that uses classical ideas like optical flow for video analysis paired with linear mixture models such as non-negative matrix factorization along with PDE-based graph classification methods that parallel geometric equations from PDE such as motion by mean curvature.

^{[4]}Consequently, we apply the Deep Graph Convolutional Neural Network, which is a graph classification method, to predict whether local search will find a feasible shunt plan given an initial solution.

^{[5]}In our experiments, we empirically verify superior or comparable prediction performance of IGML to other existing graph classification methods which do not have clear interpretability.

^{[6]}Existing graph classification methods have focused on either graph kernels or frequent subgraph patterns for classification.

^{[7]}Finally, Gaussian kernel function and directed acyclic graph classification method are chosen to build multinuclear classifier to detect wheel out-of-roundness.

^{[8]}We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges.

^{[9]}

## graph classification dataset

We evaluate the proposed method on several graph classification datasets, and manage to demonstrate comparable accuracy with the state-of-the-art on MUTAG, PTC-MR and NCI1 datasets.^{[1]}The proposed kernel is evaluated on several graph classification datasets.

^{[2]}We confirm that graph pooling, especially DiffPool, improves classification accuracy on popular graph classification datasets and find that, on average, TAGCN achieves comparable or better accuracy than GCN and GraphSAGE, particularly for datasets with larger and sparser graph structures.

^{[3]}Experimental results on several graph classification datasets show that the proposed ML-GCN outperforms four state-of-the-art methods.

^{[4]}Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish,while being state-of-the-art on benchmark graph classification datasets.

^{[5]}

## graph classification benchmark

Experimental results demonstrate that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.^{[1]}In doing so, we are also able to build richer graph representations even in the absence of edge features, which is confirmed by the performance improvements on standard graph classification benchmarks.

^{[2]}Filtration curves are highly efficient to compute and lead to expressive representations of graphs, which we demonstrate on graph classification benchmark datasets.

^{[3]}The empirical analysis on graph classification benchmarks shows how such coarsening process yields significant improvements in the predictive performance of the model with respect to its non-pooled counterpart.

^{[4]}The experimental results on several node and graph classification benchmark data sets demonstrate the effectiveness and efficiency of our proposed DEMO-Net over state-of-the-art graph neural network models.

^{[5]}

## graph classification algorithm

Automated radiograph classification algorithms have enormous potential to support clinical assistant diagnosis.^{[1]}The experimental results on nine benchmark datasets demonstrate that RGE outperforms or matches twelve state-of-the-art graph classification algorithms.

^{[2]}

## graph classification approach

We address the identification of psychosis onset by: 1) manually annotating a corpus of mental health EHRs with disease onset mentions, 2) modelling the underlying NLP problem as a paragraph classification approach, and 3) combining multiple onset paragraphs at the patient level to generate a ranked list of likely disease onset dates.^{[1]}In this study, we propose a complex probabilistic graph classification approach to address the problem of opinion spam detection.

^{[2]}