Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Multi-Scale Subgraph Contrastive Learning
Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usually simply assume that a graph and its …
Contrastive LearningGraph ClassificationMulti-hop Attention-based Graph Pooling: A Personalized PageRank Perspective
Over the past ten years, graph representation learning has garnered a lot of attention due to the variety of graph-structured data and its efficiency in both time and space. One essential method for obtaining effective g…
Graph ClassificationGraph Representation LearningRepresentation LearningGraph Parsing Networks
Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memor…
Graph ClassificationGraph ReconstructionNode ClassificationNode ClusteringVerifying message-passing neural networks via topology-based bounds tightening
Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them. We develop a computationally effective approach towards providing robust certificates for message-passing neural …
Graph ClassificationInductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
The "Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural Networks(GNNs) by capturing general knowledge without manual annotations for downstream tasks. However, due to the immense gap of …
General KnowledgeGraph ClassificationAn end-to-end attention-based approach for learning on graphs
There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersede handcrafted operators characteristic o…
Graph ClassificationGraph RegressionMolecular Property PredictionNode Classification+1Ising on the Graph: Task-specific Graph Subsampling via the Ising Model
Reducing a graph while preserving its overall structure is an important problem with many applications. Typically, reduction approaches either remove edges (sparsification) or merge nodes (coarsening) in an unsupervised …
Graph ClassificationGraph Neural NetworkImage SegmentationSemantic SegmentationClass-Balanced and Reinforced Active Learning on Graphs
Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samp…
Active LearningGraph ClassificationLink PredictionNode ClassificationSimMLP: Training MLPs on Graphs without Supervision
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive app…
Fraud DetectionGraph ClassificationGraph LearningInductive Bias+3G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user's questions, our method provides textua…
Common Sense ReasoningGraph ClassificationGraph Question AnsweringHallucination+4Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning
Graph learning is crucial in the fields of bioinformatics, social networks, and chemicals. Although high-order graphlets, such as cycles, are critical to achieving an informative graph representation for node classificat…
ClassificationGraph ClassificationGraph LearningNode ClassificationGeneralization Error of Graph Neural Networks in the Mean-field Regime
This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points. We explor…
Graph ClassificationLearning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute
The graph classification problem has been widely studied; however, achieving an interpretable model with high predictive performance remains a challenging issue. This paper proposes an interpretable classification algori…
AttributeGraph ClassificationGraph MiningFlexible infinite-width graph convolutional networks and the importance of representation learning
A common theoretical approach to understanding neural networks is to take an infinite-width limit, at which point the outputs become Gaussian process (GP) distributed. This is known as a neural network Gaussian process (…
ClassificationGraph ClassificationNode ClassificationRepresentation LearningGaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or p…
Graph ClassificationGraph RegressionregressionA Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge in graph modeling. Despite recent graph…
DecoderGraph ClassificationGraph GenerationGraph Representation Learning+1Towards Neural Scaling Laws on Graphs
Deep graph models (e.g., graph neural networks and graph transformers) have become important techniques for leveraging knowledge across various types of graphs. Yet, the neural scaling laws on graphs, i.e., how the perfo…
Graph ClassificationLink PredictionNode ClassificationTopology-Informed Graph Transformer
Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs). One key challenge in enhancing graph transformers is strengt…
Graph ClassificationGraph RegressionInductive BiasNode ClassificationGraph Transformers without Positional Encodings
Recently, Transformers for graph representation learning have become increasingly popular, achieving state-of-the-art performance on a wide-variety of graph datasets, either alone or in combination with message-passing g…
Graph ClassificationGraph RegressionGraph Representation LearningNode Classification+1Graph Contrastive Learning with Cohesive Subgraph Awareness
Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation,…
Contrastive LearningGraph ClassificationGraph LearningGraph Representation Learning+1