Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Benchmarking Graph Neural Networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have…
BenchmarkingGraph ClassificationGraph RegressionLink Prediction+1Ego-based Entropy Measures for Structural Representations
In complex networks, nodes that share similar structural characteristics often exhibit similar roles (e.g type of users in a social network or the hierarchical position of employees in a company). In order to leverage th…
General ClassificationGraph ClassificationAn End-to-End Graph Convolutional Kernel Support Vector Machine
A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation fo…
ClassificationFeature EngineeringGeneral ClassificationGraph ClassificationFew-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures
We propose to study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples. Despite several interesting GNN variants being proposed …
Active LearningClassificationFew-Shot LearningGeneral Classification+1Memory-Based Graph Networks
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and …
Graph ClassificationregressionDeep Graph Mapper: Seeing Graphs through the Neural Lens
Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph. However, even though these abstract representations are powerful for down…
Graph ClassificationGraph Representation LearningRepresentation LearningTopological Data AnalysisGraph Neural Distance Metric Learning with Graph-Bert
Graph distance metric learning serves as the foundation for many graph learning problems, e.g., graph clustering, graph classification and graph matching. Existing research works on graph distance metric (or graph kernel…
ClusteringGraph ClassificationGraph ClusteringGraph Learning+3Segmented Graph-Bert for Graph Instance Modeling
In graph instance representation learning, both the diverse graph instance sizes and the graph node orderless property have been the major obstacles that render existing representation learning models fail to work. In th…
Graph ClassificationRepresentation LearningLightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to rec…
Collaborative FilteringGraph ClassificationMulti-modal RecommendationRecommendation SystemsDistance Metric Learning for Graph Structured Data
Graphs are versatile tools for representing structured data. As a result, a variety of machine learning methods have been studied for graph data analysis. Although many such learning methods depend on the measurement of …
Computational EfficiencyGeneral ClassificationGraph ClassificationMetric LearningStructure-Feature based Graph Self-adaptive Pooling
Various methods to deal with graph data have been proposed in recent years. However, most of these methods focus on graph feature aggregation rather than graph pooling. Besides, the existing top-k selection graph pooling…
Graph ClassificationGL2vec: Graph Embedding Enriched by Line Graphs with Edge Features
Recently, several techniques to learn the embedding for a given graph dataset have been proposed. Among them, Graph2vec is significant in that it unsupervisedly learns the embedding of entire graphs which is useful for g…
General ClassificationGraph ClassificationGraph EmbeddingAdversarial Attack on Community Detection by Hiding Individuals
It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs t…
Adversarial AttackCommunity DetectionGraph ClassificationGraph Attentional Autoencoder for Anticancer Hyperfood Prediction
Recent research efforts have shown the possibility to discover anticancer drug-like molecules in food from their effect on protein-protein interaction networks, opening a potential pathway to disease-beating diet design.…
General ClassificationGraph ClassificationPredictionUnderstanding the Power of Persistence Pairing via Permutation Test
Recently many efforts have been made to incorporate persistence diagrams, one of the major tools in topological data analysis (TDA), into machine learning pipelines. To better understand the power and limitation of persi…
General ClassificationGraph ClassificationTopological Data AnalysisUnderstanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance imag…
Functional ConnectivityGeneral ClassificationGraph ClassificationUnsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structur…
Graph ClassificationA Fair Comparison of Graph Neural Networks for Graph Classification
Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the gr…
General ClassificationGraph ClassificationGraph LearningGraph Neural Network+2Multi-Channel Graph Convolutional Networks
Graph neural networks (GNN) has been demonstrated to be effective in classifying graph structures. To further improve the graph representation learning ability, hierarchical GNN has been explored. It leverages the differ…
ClusteringGraph ClassificationGraph Representation LearningRepresentation LearningZoom in to where it matters: a hierarchical graph based model for mammogram analysis
In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination. Inspired by this observation, we propose a hierar…
ClassificationGeneral ClassificationGraph AttentionGraph Classification+2