Name
Title | ||
|---|---|---|
A Faster Converging Negative Sampling for the Graph Embedding Process in Community Detection and Link Prediction Tasks |
Abstract | ||
|---|---|---|
The graph embedding process aims to transform nodes and edges into a low dimensional vector space, while preserving the graph structure and topological properties. Random walk based methods are used to capture structural relationships between nodes, by performing truncated random walks. Afterwards, the SkipGram model with the negative sampling approach, is used to calculate the embedded nodes. In this paper, the proposed SkipGram model converges in fewer iterations than the standard one. Furthermore, the community detection and link prediction task is enhanced by the proposed method. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.5220/0011142000003277 | DELTA: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON DEEP LEARNING THEORY AND APPLICATIONS |
Keywords | DocType | Citations |
Skipgram Algorithm, Negative Sampling, Graph Embedding, Community Detection, Link Prediction | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kostas Loumponias | 1 | 0 | 0.68 |
| Andreas Kosmatopoulos | 2 | 0 | 0.34 |
| Theodora Tsikrika | 3 | 0 | 0.34 |
| Stefanos Vrochidis | 4 | 0 | 0.34 |
| Ioannis Kompatsiaris | 5 | 0 | 0.34 |