Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs
Message passing has become the dominant framework in graph representation learning. The essential idea of the message-passing framework is to update node embeddings based on the information aggregated from local neighbou…
Graph Representation LearningRepresentation LearningVideoSAGE: Video Summarization with Graph Representation Learning
We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by…
Graph Representation LearningNode ClassificationRepresentation LearningVideo SummarizationGraph Neural Networks for Binary Programming
This paper investigates a link between Graph Neural Networks (GNNs) and Binary Programming (BP) problems, laying the groundwork for GNNs to approximate solutions for these computationally challenging problems. By analyzi…
Graph Representation LearningNode ClassificationRepresentation LearningHeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings in such graphs is a critical problem in …
Graph EmbeddingGraph Representation LearningLink PredictionNode Classification+1MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
Graph representation learning has rapidly emerged as a pivotal field of study. Despite its growing popularity, the majority of research has been confined to embedding single-layer graphs, which fall short in representing…
Graph AttentionGraph EmbeddingGraph Representation LearningLink Prediction+1Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach
Multimodal recommender systems work by augmenting the representation of the products in the catalogue through multimodal features extracted from images, textual descriptions, or audio tracks characterising such products.…
Graph Representation LearningMultimodal RecommendationRecommendation SystemsRepresentation LearningVariational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph st…
Graph Representation LearningInductive LearningNode ClassificationRepresentation LearningChebMixer: Efficient Graph Representation Learning with MLP Mixer
Graph neural networks have achieved remarkable success in learning graph representations, especially graph Transformer, which has recently shown superior performance on various graph mining tasks. However, graph Transfor…
Graph MiningGraph Representation LearningImage SegmentationMedical Image Segmentation+3Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spikin…
Graph LearningGraph Representation LearningRepresentation LearningInvestigating Similarities Across Decentralized Financial (DeFi) Services
We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum tran…
Graph Representation LearningRepresentation LearningGTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation
Graph Neural Networks (GNNs) have emerged as the most powerful weapon for various graph tasks due to the message-passing mechanism's great local information aggregation ability. However, over-smoothing has always hindere…
Contrastive LearningGraph Representation LearningRepresentation LearningExploring Task Unification in Graph Representation Learning via Generative Approach
Graphs are ubiquitous in real-world scenarios and encompass a diverse range of tasks, from node-, edge-, and graph-level tasks to transfer learning. However, designing specific tasks for each type of graph data is often …
Graph Representation LearningRepresentation LearningTransfer LearningSpatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction
Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in s…
DecoderGraph Representation LearningRepresentation LearningGraph Partial Label Learning with Potential Cause Discovering
Graph Neural Networks (GNNs) have garnered widespread attention for their potential to address the challenges posed by graph representation learning, which face complex graph-structured data across various domains. Howev…
Graph Representation LearningPartial Label LearningRepresentation LearningWeakly-supervised LearningComplete and Efficient Graph Transformers for Crystal Material Property Prediction
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometr…
ADP PredictionGraph Representation LearningProperty PredictionRepresentation LearningDynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations, emphasizing …
Graph Representation LearningRepresentation Learningwhole slide imagesSiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning
In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal …
Graph Neural NetworkGraph Representation LearningNode ClassificationRepresentation LearningControl-based Graph Embeddings with Data Augmentation for Contrastive Learning
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive le…
Contrastive LearningData AugmentationGraph Representation LearningRepresentation LearningRobust Graph Structure Learning under Heterophily
Graph is a fundamental mathematical structure in characterizing relations between different objects and has been widely used on various learning tasks. Most methods implicitly assume a given graph to be accurate and comp…
Graph Representation LearningGraph structure learningRepresentation LearningSemi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease
Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised le…
Feature ImportanceGraph Representation LearningRepresentation Learning