Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Graphlets correct for the topological information missed by random walks
Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding space, so that the nodes that tend to c…
Computational EfficiencyGraph Representation LearningRepresentation LearningA Structure-Aware Framework for Learning Device Placements on Computation Graphs
Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to id…
CPUgraph partitioningGraph Representation Learningreinforcement-learning+1Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each…
Anomaly DetectionData AugmentationGraph ClassificationGraph Neural Network+3Fair Graph Representation Learning via Sensitive Attribute Disentanglement
Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and gender), has gained considerable attenti…
AttributeDisentanglementFairnessGraph Representation Learning+1PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
While a number of knowledge graph representation learning (KGRL) methods have been proposed over the past decade, very few theoretical analyses have been conducted on them. In this paper, we present the first PAC-Bayesia…
DecoderGeneralization BoundsGraph Neural NetworkGraph Representation Learning+5Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and i…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+1Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
Graph Neural Networks (GNNs) have excelled in learning from graph-structured data, especially in understanding the relationships within a single graph, i.e., intra-graph relationships. Despite their successes, GNNs are l…
Graph Representation LearningRelationRepresentation LearningAnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers
Graph Transformers (GTs) have significantly advanced the field of graph representation learning by overcoming the limitations of message-passing graph neural networks (GNNs) and demonstrating promising performance and ex…
Graph Representation LearningRepresentation LearningTemporal Graph ODEs for Irregularly-Sampled Time Series
Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized…
Graph Representation LearningRepresentation LearningTime SeriesParameter-Efficient Tuning Large Language Models for Graph Representation Learning
Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) have demonstrated remarkable abilities in u…
Graph Neural NetworkGraph Representation LearningLink Predictionparameter-efficient fine-tuning+1Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to adversarial attacks. In this work, we theoreti…
Adversarial RobustnessGraph Representation LearningRepresentation LearningDeep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks
Understanding relations arising out of interactions among entities can be very difficult, and predicting them is even more challenging. This problem has many applications in various fields, such as financial networks and…
DecoderGraph Representation LearningLink PredictionRepresentation LearningUnleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework. Unlike traditional continuous GNNs that rely on integer-order differential equations, FROND…
Graph Neural NetworkGraph Representation LearningRepresentation LearningAre Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective
Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains. Current surveys on graph representation learning predominantly have an …
Graph ClassificationGraph EmbeddingGraph LearningGraph Neural Network+4Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks
Pre-training GNNs to extract transferable knowledge and apply it to downstream tasks has become the de facto standard of graph representation learning. Recent works focused on designing self-supervised pre-training tasks…
Graph Representation LearningRepresentation LearningGraphMatcher: A Graph Representation Learning Approach for Ontology Matching
Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain ontologies, semantically similar entiti…
Graph AttentionGraph Representation LearningOntology MatchingRepresentation LearningAn Efficient Loop and Clique Coarsening Algorithm for Graph Classification
Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on nod…
Graph ClassificationGraph Representation LearningRepresentation LearningCORE: Data Augmentation for Link Prediction via Information Bottleneck
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains. However, the generalizability of LP models is often compromised due to the presence of noisy or …
Data AugmentationGraph Representation LearningLink PredictionRepresentation LearningHiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expre…
Graph Representation Learningmolecular representationRepresentation LearningRandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks
Studies continually find that message-passing graph convolutional networks suffer from the over-smoothing issue. Basically, the issue of over-smoothing refers to the phenomenon that the learned embeddings for all nodes c…
Graph Representation LearningRepresentation Learning