Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons
Graph kernels are powerful tools to bridge the gap between machine learning and data encoded as graphs. Most graph kernels are based on the decomposition of graphs into a set of patterns. The similarity between two graph…
BIG-bench Machine LearningGraph ClassificationLearning Vertex Convolutional Networks for Graph Classification
In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification. Our idea is to transform the graphs of arbitrary sizes into fixed-size…
ClassificationGeneral ClassificationGraph ClassificationCapsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations
Graph classification is a significant problem in many scientific domains. It addresses tasks such as the classification of proteins and chemical compounds into categories according to their functions, or chemical and str…
BenchmarkingClassificationGeneral ClassificationGraph Classification+1Fake News Detection on Social Media using Geometric Deep Learning
Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the cost of dubious trustworthiness and sig…
Common Sense ReasoningDeep LearningFact CheckingFake News Detection+1Variational Recurrent Neural Networks for Graph Classification
We address the problem of graph classification based only on structural information. Inspired by natural language processing techniques (NLP), our model sequentially embeds information to estimate class membership probab…
ClassificationGeneral ClassificationGraph ClassificationPropagation kernels: efficient graph kernels from propagated information
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given g…
Graph ClassificationGraph Neural Networks with convolutional ARMA filters
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (A…
ClassificationGeneral ClassificationGraph ClassificationGraph Neural Network+3Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise itself using the ID of a legitimate progr…
Graph ClassificationGraph EmbeddingGraph Neural NetworkIntrusion Detection+1Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation
Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State-of-the-art collective classification methods…
AttributeClassificationGeneral ClassificationGraph Classification+1Bayesian graph convolutional neural networks for semi-supervised classification
Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix comple…
ClassificationGeneral ClassificationGraph ClassificationMatrix CompletionSpectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where th…
ClassificationGeneral ClassificationGraph ClassificationNode ClassificationDiscriminative Graph Autoencoder
With the abundance of graph-structured data in various applications, graph representation learning has become an effective computational tool for seeking informative vector representations for graphs. Traditional graph k…
Graph ClassificationGraph Representation LearningRepresentation LearningGaussian-Induced Convolution for Graphs
Learning representation on graph plays a crucial role in numerous tasks of pattern recognition. Different from grid-shaped images/videos, on which local convolution kernels can be lattices, however, graphs are fully coor…
Graph ClassificationLearning Representation On GraphGraph Convolutional Neural Networks via Motif-based Attention
Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs. Different fr…
General ClassificationGraph ClassificationA simple yet effective baseline for non-attributed graph classification
Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and fo…
ClassificationComputational EfficiencyGeneral ClassificationGraph Classification+3Towards Sparse Hierarchical Graph Classifiers
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. While novel approaches to learning node em…
ClassificationGeneral ClassificationGraph ClassificationGraph Neural Network+3Community Detection with Graph Neural Networks
We study data-driven methods for community detection on graphs, an inverse problem that is typically solved in terms of the spectrum of certain operators or via posterior inference under certain probabilistic graphical m…
Community DetectionGraph ClassificationGraph Neural NetworkStochastic Block ModelStreaming Graph Neural Networks
Graphs are essential representations of many real-world data such as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models to graph-structured data. These methods, w…
Community DetectionGeneral ClassificationGraph ClassificationGraph Neural Network+2A Simple Baseline Algorithm for Graph Classification
Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with differe…
BIG-bench Machine LearningClassificationGeneral ClassificationGraph Classification+1Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts
Data-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how…
BIG-bench Machine LearningGraph Classification