Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning
Recent advancements in graph neural networks (GNNs) and heterogeneous GNNs (HGNNs) have advanced node embeddings and relationship learning for various tasks. However, existing methods often rely on domain-specific predefined meta-paths, which are coarse-grained and focus solely on aspects like node type, limiting their ability to capture complex interactions. We introduce MF2Vec, a model that uses multi-faceted (fine-grained) paths instead of predefined meta-paths. MF2Vec extracts paths via random walks and generates multi-faceted vectors, ignoring predefined schemas. This method learns diverse aspects of nodes and their relationships, constructs a homogeneous network, and creates node embeddings for classification, link prediction, and clustering. Extensive experiments show that MF2Vec outperforms existing methods, offering a more flexible and comprehensive framework for analyzing complex networks. The code is available at https://anonymous.4open.science/r/MF2Vec-6ABC.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Representation LearningLink PredictionRepresentation LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
IMPA-HGAE:Intra-Meta-Path Augmented Heterogeneous Graph Autoencoder
Self-supervised learning (SSL) methods have been increasingly applied to diverse downstream tasks due to their superior generalization capabilities and low annotation costs. However, most existing heterogeneous graph SSL…
Representation LearningSelf-Supervised LearningCHAT: Beyond Contrastive Graph Transformer for Link Prediction in Heterogeneous Networks
Link prediction in heterogeneous networks is crucial for understanding the intricacies of network structures and forecasting their future developments. Traditional methodologies often face significant obstacles, includin…
Link PredictionPredictionFaceted Search of Heterogeneous Geographic Information for Dynamic Map Projection
This paper proposes a faceted information exploration model that supports coarse-grained and fine-grained focusing of geographic maps by offering a graphical representation of data attributes within interactive widgets. …
Data VisualizationLearning on heterogeneous graphs using high-order relations
A heterogeneous graph consists of different vertices and edges types. Learning on heterogeneous graphs typically employs meta-paths to deal with the heterogeneity by reducing the graph to a homogeneous network, guide ran…
RelationVocal Bursts Intensity PredictionThe canonical facets of multi-separator polytopes
We initiate a polyhedral study of the graph multi-separator problem proposed by Irmai et al. (2024) as an alternative to the lifted multicut problem for application to the task of image segmentation. Starting with an int…
Image Segmentation