Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
An Experimental Study of the Transferability of Spectral Graph Networks
Spectral graph convolutional networks are generalizations of standard convolutional networks for graph-structured data using the Laplacian operator. A common misconception is the instability of spectral filters, i.e. the…
BenchmarkingGeneral ClassificationGraph ClassificationGraph Regression+1Hierarchical Graph Capsule Network
Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data. However, existing GNNs suffer from limited capability in capturing the hierarchical graph represen…
Graph ClassificationBlindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queries to it. These assumptions are, however…
Adversarial AttackGraph ClassificationNode ClassificationDecimated Framelet System on Graphs and Fast G-Framelet Transforms
Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis. An adequate representation of graph…
Graph ClassificationGraph Representation LearningRepresentation LearningSuper-ResolutionCommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classifica…
Graph ClassificationGraph Representation LearningRepresentation LearningLCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space
For graph learning tasks, many existing methods utilize a message-passing mechanism where vertex features are updated iteratively by aggregation of neighbor information. This strategy provides an efficient means for grap…
Graph ClassificationGraph LearningA graph similarity for deep learning
Graph neural networks (GNNs) have been successful in learning representations from graphs. Many popular GNNs follow the pattern of aggregate-transform: they aggregate the neighbors' attributes and then transform the resu…
Deep LearningGraph ClassificationGraph GenerationGraph Regression+2Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Graph convolution networks (GCNs) have become effective models for graph classification. Similar to many deep networks, GCNs are vulnerable to adversarial attacks on graph topology and node attributes. Recently, a numb…
General ClassificationGraph ClassificationRandom Walk Graph Neural Networks
In recent years, graph neural networks (GNNs) have become the de facto tool for performing machine learning tasks on graphs. Most GNNs belong to the family of message passing neural networks (MPNNs). These models employ …
Graph ClassificationGraph Neural NetworkCOPT: Coordinated Optimal Transport on Graphs
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 ClassificationA Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while pr…
ClusteringGraph ClassificationGraph EmbeddingGraph Learning+4Classification by Attention: Scene Graph Classification with Prior Knowledge
A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another. Previous works have addressed this by relational reasoning over al…
ClassificationGeneral ClassificationGraph ClassificationInductive Bias+4Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data. Additionally, multi-task learning can further improv…
Auxiliary LearningGraph ClassificationMulti-Task LearningRepresentation Learning+1Two-stage Training of Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) have received massive attention in the field of machine learning on graphs. Inspired by the success of neural networks, a line of research has been conducted to train GNNs to deal with variou…
ClassificationGeneral ClassificationGraph ClassificationLink Prediction+3Parameterized Explainer for Graph Neural Network
Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the local explanations (i.e., important subgraph…
Graph ClassificationGraph Neural NetworkSampling and Recovery of Graph Signals based on Graph Neural Networks
We propose interpretable graph neural networks for sampling and recovery of graph signals, respectively. To take informative measurements, we propose a new graph neural sampling module, which aims to select those vertice…
Graph ClassificationGraph Neural NetworkRolling Shutter CorrectionDark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer
In visual robot self-localization, graph-based scene representation and matching have recently attracted research interest as robust and discriminative methods for selflocalization. Although effective, their computationa…
Graph ClassificationTransfer LearningComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present Gapformer, which effectively classifies content as informative or not. It reform…
Graph ClassificationLanguage ModelingLanguage ModellingTask 2Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link). We kno…
General ClassificationGraph ClassificationLink PredictionLink Property Prediction+2Graph embedding using multi-layer adjacent point merging model
For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomorphism problems. However, in some classi…
Classificationfeature selectionGeneral ClassificationGraph Classification+1