Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Graph Kernels: A Survey
Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that …
Graph ClassificationSurveyDDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domain knowledge nor supervision (i.e.\ featu…
Feature EngineeringGraph AttentionGraph ClassificationGraph SimilarityPersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science. However, since the (me…
Graph ClassificationTopological Data AnalysisedGNN: a Simple and Powerful GNN for Directed Labeled Graphs
The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings. Building on previous work, we theoretically show that …
Graph ClassificationGraph Neural NetworkSelf-Attention Graph Pooling
Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which incl…
Graph ClassificationSemi-Supervised Graph Classification: A Hierarchical Graph Perspective
Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e…
ClassificationGeneral ClassificationGraph ClassificationGraph Embedding+2SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
A wide range of machine learning problems involve handling graph-structured data. Existing machine learning approaches for graphs, however, often imply computing expensive graph similarity measures, preprocessing input g…
BIG-bench Machine LearningDeep LearningGeneral ClassificationGraph Classification+1DeepGCNs: Can GCNs Go as Deep as CNNs?
Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to be reliably trained. Despite their meri…
3D Semantic SegmentationGraph ClassificationNode ClassificationSemantic SegmentationLearning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-…
General ClassificationGraph ClassificationDAGCN: Dual Attention Graph Convolutional Networks
Graph convolutional networks (GCNs) have recently become one of the most powerful tools for graph analytics tasks in numerous applications, ranging from social networks and natural language processing to bioinformatics a…
General ClassificationGraph ClassificationGraph EmbeddingRep the Set: Neural Networks for Learning Set Representations
In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventional machine learning algorithms are unable…
General ClassificationGraph ClassificationText CategorizationWeisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Graph kernels based on the $1$-dimensional Weisfeiler-Leman algorithm and corresponding neural architectures recently emerged as powerful tools for (supervised) learning with graphs. However, due to the purely local natu…
General ClassificationGraph ClassificationGraph LearningGraph RegressionUnsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGRAPHEMB…
General ClassificationGraph ClassificationGraph EmbeddingGraph Similarity+1Quantum-based subgraph convolutional neural networks
This paper proposes a new graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quantum walks, which we refer to as the quantum-based subgraph convolutiona…
General ClassificationGraph ClassificationNode ClassificationClique pooling for graph classification
We propose a novel graph pooling operation using cliques as the unit pool. As this approach is purely topological, rather than featural, it is more readily interpretable, a better analogue to image coarsening than filter…
ClassificationGeneral ClassificationGraph ClassificationA Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in t…
ClassificationGeneral ClassificationGraph ClassificationSurveySubgraph Networks with Application to Structural Feature Space Expansion
Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to chara…
General ClassificationGraph ClassificationNetwork EmbeddingGNNExplainer: Generating Explanations for Graph Neural Networks
Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. Howeve…
BIG-bench Machine LearningExplainable artificial intelligenceGraph ClassificationGraph Neural Network+1Relational Pooling for Graph Representations
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Pooling (RP), draws from the theory of fin…
General ClassificationGraph ClassificationFast Graph Representation Learning with PyTorch Geometric
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing…
GPUGraph ClassificationGraph Representation LearningNode Classification+2