Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Transductive Spiking Graph Neural Networks for Loihi
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural netwo…
Bayesian OptimizationGraph ClassificationHyperparameter OptimizationAre Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective
Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains. Current surveys on graph representation learning predominantly have an …
Graph ClassificationGraph EmbeddingGraph LearningGraph Neural Network+4Gradformer: Graph Transformer with Exponential Decay
Graph Transformers (GTs) have demonstrated their advantages across a wide range of tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases, particularly biases related to structure, whi…
Graph ClassificationGraph Neural NetworkInductive BiasOne Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion
Knowledge Graph Completion (KGC) has garnered massive research interest recently, and most existing methods are designed following a transductive setting where all entities are observed during training. Despite the great…
AllGraph ClassificationInductive knowledge graph completionKnowledge Graph CompletionSPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation learning through neighborhood aggregation a…
Graph ClassificationGraph LearningRepresentation LearningCKGConv: General Graph Convolution with Continuous Kernels
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph domain is challenging due to the lack of c…
Graph ClassificationGraph LearningGraph RegressionNode ClassificationA Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only
Graph Convolutional Networks (GCNs) have shown excellent performance in dealing with various graph structures such as node classification, graph classification and other tasks. However,recent studies have shown that GCNs…
Backdoor AttackGraph ClassificationNode ClassificationImproving the interpretability of GNN predictions through conformal-based graph sparsification
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks. However, most GNN architectures aggregate information from all nodes and edges in a graph, regardless of thei…
ClassificationGraph ClassificationAn Efficient Loop and Clique Coarsening Algorithm for Graph Classification
Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on nod…
Graph ClassificationGraph Representation LearningRepresentation LearningGraph data augmentation with Gromow-Wasserstein Barycenters
Graphs are ubiquitous in various fields, and deep learning methods have been successful applied in graph classification tasks. However, building large and diverse graph datasets for training can be expensive. While augme…
Data AugmentationGraph ClassificationGraphon EstimationLearning the mechanisms of network growth
We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random grap…
Graph ClassificationModel SelectionHarnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
Handling graph data is one of the most difficult tasks. Traditional techniques, such as those based on geometry and matrix factorization, rely on assumptions about the data relations that become inadequate when handling …
Graph ClassificationKnowledge Graph CompletionLanguage ModelingLanguage Modelling+2SSHPool: The Separated Subgraph-based Hierarchical Pooling
In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into differ…
Graph ClassificationAKBR: Learning Adaptive Kernel-based Representations for Graph Classification
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of is…
Graph ClassificationRepresentation LearningGTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data. The literature on GNN highlights the potential of this evolving research area and its widespread adopti…
Graph ClassificationMolecular Classification Using Hyperdimensional Graph Classification
Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained sign…
ClassificationGraph ClassificationGraph LearningGeneration is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
Graph-based anomaly detection is currently an important research topic in the field of graph neural networks (GNNs). We find that in graph anomaly detection, the homophily distribution differences between different class…
Anomaly DetectionGraph Anomaly DetectionGraph ClassificationGraph Neural Network+1A Differential Geometric View and Explainability of GNN on Evolving Graphs
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital to formally model and understand how a t…
Graph ClassificationLink PredictionNode ClassificationCooperative Classification and Rationalization for Graph Generalization
Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been propos…
ClassificationGraph ClassificationKnowledge DistillationHDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
In recent times, a plethora of hardware accelerators have been put forth for graph learning applications such as vertex classification and graph classification. However, previous works have paid little attention to Knowl…
GPUGraph ClassificationGraph LearningKnowledge Graph Completion