Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Multi-view Tensor Graph Neural Networks Through Reinforced Aggregation
Graph Neural Networks (GNNs) have yielded fruitful results in learning multi-view graph data. However, it is challenging for existing GNNs to capture the potential correlation information (PCI) among the graph structure …
Graph Representation LearningReinforcement Learning (RL)Representation LearningTensor DecompositionA Survey on Malware Detection with Graph Representation Learning
Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, the…
Graph Representation LearningMalware DetectionRepresentation LearningSurveyTopological Pooling on Graphs
Graph neural networks (GNNs) have demonstrated a significant success in various graph learning tasks, from graph classification to anomaly detection. There recently has emerged a number of approaches adopting a graph poo…
Anomaly DetectionGraph ClassificationGraph LearningGraph Representation Learning+1Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering
Community detection is a critical challenge in analysing real graphs, including social, transportation, citation, cybersecurity, and many other networks. This article proposes three new, general, hierarchical frameworks …
Community DetectionGraph Representation LearningRepresentation LearningStochastic Block ModelSpatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection
This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial repres…
Graph LearningGraph Representation LearningRepresentation LearningEfficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges …
Graph Representation LearningRepresentation LearningCategory-Level Multi-Part Multi-Joint 3D Shape Assembly
Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the …
Graph LearningGraph Representation LearningRepresentation LearningStructure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework
Albeit having gained significant progress lately, large-scale graph representation learning remains expensive to train and deploy for two main reasons: (i) the repetitive computation of multi-hop message passing and non-…
Contrastive LearningGraph Representation LearningRepresentation LearningSUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability. Most previous SGRL …
Graph Representation LearningPredictionRepresentation LearningTowards Improved Illicit Node Detection with Positive-Unlabelled Learning
Detecting illicit nodes on blockchain networks is a valuable task for strengthening future regulation. Recent machine learning-based methods proposed to tackle the tasks are using some blockchain transaction datasets wit…
Graph Representation LearningRepresentation LearningPrior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition
Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, …
Graph Representation LearningMicro Expression RecognitionMicro-Expression RecognitionRepresentation LearningA Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail
We present a novel dataset collected by ASOS (a major online fashion retailer) to address the challenge of predicting customer returns in a fashion retail ecosystem. With the release of this substantial dataset we hope t…
Graph Neural NetworkGraph Representation LearningRecommendation SystemsRepresentation LearningFTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning
Learning representations for graph-structured data is essential for graph analytical tasks. While remarkable progress has been made on static graphs, researches on temporal graphs are still in its beginning stage. The bo…
Graph Representation LearningRepresentation LearningLearning Dynamic Graph Embeddings with Neural Controlled Differential Equations
This paper focuses on representation learning for dynamic graphs with temporal interactions. A fundamental issue is that both the graph structure and the nodes own their own dynamics, and their blending induces intractab…
Graph Representation LearningRepresentation LearningDrop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks
The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a subst…
FairnessGraph Representation LearningLink PredictionPrediction+1A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Node classification is a classical graph machine learning task on which Graph Neural Networks (GNNs) have recently achieved strong results. However, it is often believed that standard GNNs only work well for homophilous …
Graph Representation LearningNode ClassificationRepresentation LearningA General-Purpose Transferable Predictor for Neural Architecture Search
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and achieve high ranking correlations betwee…
Contrastive LearningGraph Representation LearningNeural Architecture SearchRepresentation LearningEfficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
As privacy protection receives much attention, unlearning the effect of a specific node from a pre-trained graph learning model has become equally important. However, due to the node dependency in the graph-structured da…
Graph LearningGraph Representation LearningRepresentation LearningCreating generalizable downstream graph models with random projections
We investigate graph representation learning approaches that enable models to generalize across graphs: given a model trained using the representations from one graph, our goal is to apply inference using those same mode…
Computational EfficiencyGraph Representation LearningRepresentation LearningLearnable Topological Features for Phylogenetic Inference via Graph Neural Networks
Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires signi…
Graph Representation LearningRepresentation Learning