Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Cross-Domain Few-Shot Graph Classification
We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. We also propose an att…
ClassificationCross-Domain Few-ShotGraph ClassificationMeta-Learning+1KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted combinatorial features of graphs. In recent…
Graph ClassificationCausal Attention for Interpretable and Generalizable Graph Classification
In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract the critical features from the input graph and support the prediction. They mostly follow the paradigm of learning to a…
ClassificationGraph AttentionGraph ClassificationPredictionMotif Graph Neural Network
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to le…
Graph ClassificationGraph EmbeddingGraph LearningGraph Neural Network+1GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification and recommendations tasks. To enable lea…
Graph ClassificationGraph Representation LearningRepresentation LearningImproving Subgraph Recognition with Variational Graph Information Bottleneck
Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property. It can be formulated by optimizing Graph Information Bottleneck (GIB) with a mutual informatio…
Graph ClassificationFlowPool: Pooling Graph Representations with Wasserstein Gradient Flows
In several machine learning tasks for graph structured data, the graphs under consideration may be composed of a varying number of nodes. Therefore, it is necessary to design pooling methods that aggregate the graph repr…
Graph ClassificationA New Perspective on the Effects of Spectrum in Graph Neural Networks
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and their effects on GNN performance. By gene…
Graph ClassificationGraph Property PredictionGraph RegressionGraph Kernel Neural Networks
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, …
Graph ClassificationMulti-scale Graph Convolutional Networks with Self-Attention
Graph convolutional networks (GCNs) have achieved remarkable learning ability for dealing with various graph structural data recently. In general, deep GCNs do not work well since graph convolution in conventional GCNs i…
Computational EfficiencyGraph ClassificationNode ClassificationControversy Detection: a Text and Graph Neural Network Based Approach
Controversial content refers to any content that attracts both positive and negative feedback. Its automatic identification, especially on social media, is a challenging task as it should be done on a large number of con…
Graph ClassificationGraph EmbeddingGraph Neural NetworkGraph Representation Learning+1AutoGEL: An Automated Graph Neural Network with Explicit Link Information
Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of real-world scenarios. Despite the great success, the architecture design of GNNs heavily relies on manual labor. Thus, automated graph neural …
Graph ClassificationGraph Neural NetworkLink PredictionNode ClassificationAdversarial Attacks on Graph Classifiers via Bayesian Optimisation
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vuln…
Adversarial RobustnessBayesian OptimisationClassificationGraph ClassificationGraph Adversarial Self-Supervised Learning
This paper studies a long-standing problem of learning the representations of a whole graph without human supervision. The recent self-supervised learning methods train models to be invariant to the transformations (view…
Graph ClassificationSelf-Supervised LearningImbalanced Graph Classification via Graph-of-Graph Neural Networks
Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification problems with GNNs follow the protocol of balanced data splitting,…
ClassificationGraph ClassificationNode ClassificationAutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications. However, extensive manual work and domain knowledge are required to design effective archite…
Graph ClassificationNode ClassificationGraph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification
We focus on graph classification using a graph neural network (GNN) model that precomputes the node features using a bank of neighborhood aggregation graph operators arranged in parallel. These GNN models have a natural …
ClassificationGraph ClassificationGraph Neural NetworkLearnable Structural Semantic Readout for Graph Classification
With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node …
ClassificationGraph ClassificationPositionIV-GNN : Interval Valued Data Handling Using Graph Neural Network
Graph Neural Network (GNN) is a powerful tool to perform standard machine learning on graphs. To have a Euclidean representation of every node in the Non-Euclidean graph-like data, GNN follows neighbourhood aggregation a…
Graph ClassificationGraph Neural NetworkStructure Representation Learning by Jointly Learning to Pool and Represent
Structure representation learning is a task to provide an overall representation for a given structure (e.g., sequential text, non-sequential graph). This representation characterizes the property of that structure. Prev…
ClassificationGraph ClassificationRepresentation LearningSentence+1