Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks
In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph structure, and computing the Averaged Mixin…
Graph ClassificationDiffusing Graph Attention
The dominant paradigm for machine learning on graphs uses Message Passing Graph Neural Networks (MP-GNNs), in which node representations are updated by aggregating information in their local neighborhood. Recently, there…
Graph AttentionGraph ClassificationGraph RegressionLink PredictionA semantic backdoor attack against Graph Convolutional Networks
Graph convolutional networks (GCNs) have been very effective in addressing the issue of various graph-structured related tasks. However, recent research has shown that GCNs are vulnerable to a new type of threat called a…
Backdoor AttackClassificationGraph ClassificationNode ClassificationMasked Relation Learning for DeepFake Detection
Abstract— DeepFake detection aims to differentiate falsified faces from real ones. Most approaches formulate it as a binary classification problem by solely mining the local artifacts and inconsistencies of face forge…
Binary ClassificationDeepFake DetectionFace SwappingGraph Classification+1SGL-PT: A Strong Graph Learner with Graph Prompt Tuning
Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inh…
Graph ClassificationGraph LearningOn the Expressivity of Persistent Homology in Graph Learning
Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order top…
Graph ClassificationGraph LearningSearch to Capture Long-range Dependency with Stacking GNNs for Graph Classification
In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are…
Graph ClassificationGraph structure learningNeural Architecture SearchMultiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures
Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determine the molecular properties of macro…
Graph ClassificationGraph LearningGraph RegressionFrom Graph Generation to Graph Classification
This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and their class labels, I derive classificat…
ClassificationGraph ClassificationGraph GenerationEnergy Transformer
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. Attention is the power-house driving modern deep learning successes, b…
Anomaly DetectionGraph Anomaly DetectionGraph ClassificationUnnoticeable Backdoor Attacks on Graph Neural Networks
Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to adversarial attacks. However, effective …
Backdoor AttackGraph ClassificationNode ClassificationBi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
The construction of machine learning models involves many bi-level multi-objective optimization problems (BL-MOPs), where upper level (UL) candidate solutions must be evaluated via training weights of a model in the lowe…
Decision MakingGraph ClassificationGraph Neural NetworkLink Prediction+1Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar. This problem was rarely stu…
ClusteringGraph ClassificationNode ClusteringPseudo Label+1Structural Explanations for Graph Neural Networks using HSIC
Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. Recently, GNNs have been receiving increased attention in machine learning and data mining communities because …
Graph ClassificationLink PredictionSimplifying Subgraph Representation Learning for Scalable Link Prediction
Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-t…
Graph ClassificationGraph LearningGraph Representation LearningLink Prediction+2Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data
Graph-structured data can be found in numerous domains, yet the scarcity of labeled instances hinders its effective utilization of deep learning in many scenarios. Traditional unsupervised domain adaptation (UDA) strateg…
DenoisingDomain AdaptationGraph ClassificationNavigate+2Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
It has been increasingly demanding to develop reliable methods to evaluate the progress of Graph Neural Network (GNN) research for molecular representation learning. Existing GNN benchmarking methods for molecular repres…
BenchmarkingGraph ClassificationGraph Neural Networkmolecular representation+2On the Connection Between MPNN and Graph Transformer
Graph Transformer (GT) recently has emerged as a new paradigm of graph learning algorithms, outperforming the previously popular Message Passing Neural Network (MPNN) on multiple benchmarks. Previous work (Kim et al., 20…
Graph ClassificationGraph LearningGraph RegressionNode ClassificationGraph Scattering beyond Wavelet Shackles
This work develops a flexible and mathematically sound framework for the design and analysis of graph scattering networks with variable branching ratios and generic functional calculus filters. Spectrally-agnostic stabil…
Graph ClassificationEfficiently predicting high resolution mass spectra with graph neural networks
Identifying a small molecule from its mass spectrum is the primary open problem in computational metabolomics. This is typically cast as information retrieval: an unknown spectrum is matched against spectra predicted com…
Graph ClassificationInformation RetrievalPredictionRetrieval+1