Name
Abstract | ||
|---|---|---|
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on textual networks. Different from most existing methods that optimize a discriminative objective, we introduce Variational Homophilic Embedding (VHE), a fully generative model that learns network embeddings by modeling the semantic (textual) information with a variational autoencoder, while accounting for the structural (topology) information through a novel homophilic prior design. Homophilic vertex embeddings encourage similar embedding vectors for related (connected) vertices. The proposed VHE promises better generalization for downstream tasks, robustness to incomplete observations, and the ability to generalize to unseen vertices. Extensive experiments on real-world networks, for multiple tasks, demonstrate that the proposed method consistently achieves superior performance relative to competing state-of-the-art approaches. |
Year | Venue | Keywords |
|---|---|---|
2019 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | special focus |
Field | DocType | Volume |
Computer science,Artificial intelligence,Machine learning | Conference | 32 |
ISSN | Citations | PageRank |
1049-5258 | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenlin Wang | 1 | 2 | 2.05 |
| Chenyang Tao | 2 | 8 | 7.93 |
| Zhe Gan | 3 | 319 | 32.58 |
| Guoyin Wang | 4 | 3 | 2.06 |
| Liqun Chen | 5 | 28 | 4.77 |
| Xinyuan Zhang | 6 | 10 | 3.16 |
| Ruiyi Zhang | 7 | 21 | 10.04 |
| Yang, Qian | 8 | 0 | 0.68 |
| Ricardo Henao | 9 | 286 | 23.85 |
| Lawrence Carin | 10 | 137 | 11.38 |