RaDE: A Rank-based Graph Embedding Approach

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.