Name
Title | ||
|---|---|---|
Measuring Graph Reconstruction Precisions: How Well Do Embeddings Preserve the Graph Proximity Structure? |
Abstract | ||
|---|---|---|
Graph embedding aims at learning representations of nodes in a low dimensional vector space. Good embeddings should preserve proximity structure of the original graph and thus are expected to accurately reconstruct the graph. We propose a reconstruction procedure such that the reconstructed graph keeps the total number of weights of the original one. Then we assess the reconstruction precision using a global view based graph similarity metric called DeltaCon. Based on this metric, we found that the embeddings by the state-of-the-art techniques can only preserve part of the proximity structure and is insufficient to achieve high reconstruction accuracy. |
Year | Venue | Field |
|---|---|---|
2018 | WIMS | Data mining,Graph,Vector space,Graph similarity,Dimensionality reduction,Graph embedding,Computer science,View based,Theoretical computer science,Reconstruction procedure,Network representation learning |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Liu | 1 | 3 | 1.76 |
| Tsuyoshi Murata | 2 | 37 | 14.01 |
| Kyoung-Sook Kim | 3 | 24 | 14.07 |