Publication: RaDE: A Rank-based Graph Embedding Approach
Loading...
Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Scitepress
Type
Work presented at event
Access right
Abstract
Due to possibility of capturing complex relationships existing between nodes, many application benefit of being modeled with graphs. However, performance issues can be observed on large scale networks, making it computationally unfeasible to process information in various scenarios. Graph Embedding methods are usually used for finding low-dimensional vector representations for graphs, preserving its original properties such as topological characteristics, affinity and shared neighborhood between nodes. In this way, retrieval and machine learning techniques can be exploited to execute tasks such as classification, clustering, and link prediction. In this work, we propose RaDE (Rank Diffusion Embedding), an efficient and effective approach that considers rank-based graphs for learning a low-dimensional vector. The proposed approach was evaluated on 7 network datasets such as a social, co-reference, textual and image networks, with different properties. Vector representations generated with RaDE achieved effective results in visualization and retrieval tasks when compared to vector representations generated by other recent related methods.
Description
Keywords
RaDE, Graph Embedding, Network Representation Learning, Ranking
Language
English
Citation
Proceedings Of The 15th International Joint Conference On Computer Vision, Imaging And Computer Graphics Theory And Applications, Vol 5: Visapp. Setubal: Scitepress, p. 142-152, 2020.
