Name
Abstract | ||
|---|---|---|
Graph representation learning has become a topic of great interest and many works focus on the generation of high-level, task-independent node embeddings for complex networks. However, the existing methods consider only few aspects of networks at a time. In this paper, we propose a novel framework, named Co-MLHAN, to learn node embeddings for networks that are simultaneously multilayer, heterogeneous and attributed. We leverage contrastive learning as a self-supervised and task-independent machine learning paradigm and define a cross-view mechanism between two views of the original graph which collaboratively supervise each other. We evaluate our framework on the entity classification task. Experimental results demonstrate the effectiveness of Co-MLHAN and its variant Co-MLHAN-SA, showing their capability of exploiting across-layer information in addition to other types of knowledge. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s41109-022-00504-9 | Applied Network Science |
Keywords | DocType | Volume |
Graph representation learning, Contrastive learning, Multilayer networks, Heterogeneous networks, Attributed networks, Entity classification | Journal | 7 |
Issue | ISSN | Citations |
1 | 2364-8228 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martirano Liliana | 1 | 0 | 0.34 |
| Zangari Lorenzo | 2 | 0 | 0.34 |
| Andrea Tagarelli | 3 | 475 | 52.29 |