Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
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.Aiming to handle the new classes with…
ClassificationGeneral ClassificationGraph ClassificationMeta-Learning+3Graph Autoencoder for Graph Compression and Representation Learning
We consider the problem of graph data compression and representation. Recent developments in graph neural networks (GNNs) focus on generalizing convolutional neural networks (CNNs) to graph data, which includes redesigni…
Data CompressionGraph ClassificationGraph Representation LearningRepresentation LearningMulti-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structured data and achieved better task performance than conventional methods. The foundation of GNNs is the message passing pro…
Graph ClassificationCogDL: A Comprehensive Library for Graph Deep Learning
Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social netwo…
Deep LearningGraph ClassificationGraph EmbeddingGraph Learning+5Graphfool: Targeted Label Adversarial Attack on Graph Embedding
Deep learning is effective in graph analysis. It is widely applied in many related areas, such as link prediction, node classification, community detection, and graph classification etc. Graph embedding, which learns low…
Adversarial AttackClassificationCommunity DetectionGeneral Classification+4Accurate Learning of Graph Representations with Graph Multiset Pooling
Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a …
Graph ClassificationGraph ClusteringGraph EmbeddingGraph Generation+3Generalized Equivariance and Preferential Labeling for GNN Node Classification
Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications…
ClassificationGeneral ClassificationGraph ClassificationNode ClassificationWalking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
We propose CRaWl, a novel neural network architecture for graph learning. Like graph neural networks, CRaWl layers update node features on a graph and thus can freely be combined or interleaved with GNN layers. Yet CRaWl…
Graph ClassificationGraph LearningGraph RegressionregressionEgo-based Entropy Measures for Structural Representations on Graphs
Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs). Most of the proposed GNNs are based on the node homophily, i.e neighboring nodes share …
Graph ClassificationOnline Graph Dictionary Learning
Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements. Yet, this analysis is not amenable in the context of graph learning, as graphs usually be…
Dictionary LearningGraph ClassificationGraph LearningRepresentation LearningReinforcement Learning For Data Poisoning on Graph Neural Networks
Adversarial Machine Learning has emerged as a substantial subfield of Computer Science due to a lack of robustness in the models we train along with crowdsourcing practices that enable attackers to tamper with data. In t…
BIG-bench Machine LearningClassificationData PoisoningGeneral Classification+4Improving Scene Graph Classification by Exploiting Knowledge from Texts
Training scene graph classification models requires a large amount of annotated image data. Meanwhile, scene graphs represent relational knowledge that can be modeled with symbolic data from texts or knowledge graphs. Wh…
ClassificationGeneral ClassificationGraph ClassificationKnowledge Graphs+7Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations
Graph neural networks are emerging as continuation of deep learning success w.r.t. graph data. Tens of different graph neural network variants have been proposed, most following a neighborhood aggregation scheme, where t…
Graph ClassificationGraph Neural NetworkLearning Graph Representations
Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of th…
General ClassificationGraph ClassificationKnowledge GraphsLink Prediction+1Graph Classification Based on Skeleton and Component Features
Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation. To address this weakness, we propose a novel graph embeddin…
ClassificationGeneral ClassificationGraph ClassificationGraph Embedding[Re] Parameterized Explainer for Graph Neural Network
Scope of Reproducibility In this work we perform a replication study of the paper Parameterized Explainer for Graph Neural Network. The replication experiment focuses on three main claims: (1) Is it possible to reimple…
Graph ClassificationGraph Neural NetworkEfficient Graph Deep Learning in TensorFlow with tf_geometric
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs)…
Deep LearningGeneral ClassificationGraph ClassificationGraph Sampling+4Identity-aware Graph Neural Networks
Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism …
Graph ClassificationGraph Property PredictionLink PredictionNode Clustering+1Learning Parametrised Graph Shift Operators
In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represent…
Graph ClassificationGraph Neural NetworkSUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching view of graphs, neglecting finer substr…
Graph ClassificationGraph Neural NetworkGraph Representation LearningRepresentation Learning