Name
Title | ||
|---|---|---|
MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes |
Abstract | ||
|---|---|---|
Network embedding aims at learning an effective vector transformation for entities in a network. We observe that there are two diverse branches of network embedding: for homogeneous graphs and for multi-relational graphs. This paper then proposes MARINE, a unified embedding framework for both homogeneous and multi-relational networks to preserve both the proximity and relation information. We also extend the framework to incorporate existing features of nodes in a graph, which can further be exploited for the ensemble of embedding. Our solution possesses complexity linear to the number of edges, which is suitable for large-scale network applications. Experiments conducted on several real-world network datasets, along with applications in link prediction and multi-label classification, exhibit the superiority of our proposed MARINE.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308558.3313715 | WWW '19: The Web Conference on The World Wide Web Conference WWW 2019 |
Keywords | DocType | ISBN |
Homogeneous Network Embedding, Knowledge Graph embedding, Multi-relational Network Embedding | Conference | 978-1-4503-6674-8 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noriaki Kawamae | 1 | 119 | 10.96 |