Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
User Preference-aware Fake News Detection
Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing fake news detection algorithms focus on …
Fact CheckingFake News DetectionGraph ClassificationMisinformationPermutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification…
ClusteringGeneral ClassificationGraph ClassificationGraph Matching+3A Hyperbolic-to-Hyperbolic Graph Convolutional Network
Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent spaces to realize graph convolution on hy…
General ClassificationGraph ClassificationLink PredictionNode ClassificationQuadratic GCN for Graph Classification
Graph Convolutional Networks (GCNs) have been extensively used to classify vertices in graphs and have been shown to outperform other vertex classification methods. GCNs have been extended to graph classification tasks (…
ClassificationGeneral ClassificationGraph ClassificationKnowledge GraphsIdentity Inference on Blockchain using Graph Neural Network
The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently …
Graph ClassificationGraph MiningGraph Neural NetworkHierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning
Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. However, the graph pooling technique for learning expressive graph-le…
Graph ClassificationGraph MatchingGraph Representation LearningGraph Similarity+1Smart Vectorizations for Single and Multiparameter Persistence
The machinery of topological data analysis becomes increasingly popular in a broad range of machine learning tasks, ranging from anomaly detection and manifold learning to graph classification. Persistent homology is one…
Anomaly DetectionGraph ClassificationTopological Data AnalysisScaling up graph homomorphism for classification via sampling
Feature generation is an open topic of investigation in graph machine learning. In this paper, we study the use of graph homomorphism density features as a scalable alternative to homomorphism numbers which retain simila…
BIG-bench Machine LearningClassificationGeneral ClassificationGraph Classification+1GABO: Graph Augmentations with Bi-level Optimization
Data augmentation refers to a wide range of techniques for improving model generalization by augmenting training examples. Oftentimes such methods require domain knowledge about the dataset at hand, spawning a plethora o…
Bilevel OptimizationData AugmentationGraph ClassificationParameterized Hypercomplex Graph Neural Networks for Graph Classification
Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs. Motivated by the complex and quaternion algebras, which have been found in …
ClassificationGeneral ClassificationGraph ClassificationRepresentation LearningUnified Graph Structured Models for Video Understanding
Accurate video understanding involves reasoning about the relationships between actors, objects and their environment, often over long temporal intervals. In this paper, we propose a message passing graph neural network …
Action DetectionGraph ClassificationGraph Neural NetworkRelational Reasoning+2Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imbalanced. Recently, Graph Neural Network …
ClassificationGeneral ClassificationGraph ClassificationGraph Neural NetworkCatastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for f…
Continual LearningGeneral ClassificationGraph ClassificationGraph Representation Learning+1GraphDIVE: Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imbalanced. Recently, Graph Neural Network …
ClassificationGraph ClassificationGraph Neural NetworkLearning to Represent the Evolution of Dynamic Graphs with Recurrent Models
Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on …
DecoderGraph ClassificationGraph Representation LearningRepresentation LearningDiversified Multiscale Graph Learning with Graph Self-Correction
Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt repre…
DiversityEnsemble LearningGraph ClassificationGraph LearningShould Graph Neural Networks Use Features, Edges, Or Both?
Graph Neural Networks (GNNs) are the first choice for learning algorithms on graph data. GNNs promise to integrate (i) node features as well as (ii) edge information in an end-to-end learning algorithm. How does this pro…
ClassificationGeneral ClassificationGraph ClassificationSize-Invariant Graph Representations for Graph Classification Extrapolations
In general, graph representation learning methods assume that the train and test data come from the same distribution. In this work we consider an underexplored area of an otherwise rapidly developing field of graph repr…
ClassificationGeneral ClassificationGraph ClassificationGraph Representation Learning+1Sanity Check for Persistence Diagrams
Recently many efforts have been made to incorporate persistence diagrams, one of major tools in topological data analysis (TDA), into machine learning pipelines. To better understand the power and limitation of persisten…
Graph ClassificationTopological Data AnalysisScaling Up Graph Homomorphism Features with Efficient Data Structures
Typical datasets used in graph classification tasks only contain a few thousand graphs which rarely exceed hundreds of nodes. Graph homomorphism densities are permutation-invariant features that can be directly computed …
Graph Classification