Abstract
Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected nodes of the huge network into feature representations is critical. As basic properties of the real-world networks, the local and global structure can be reflected by dynamical transfer behaviors from node to node. In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes. We first suggest a degree-weight biased random walk model to capture the transfer behaviors of the network. Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various real-world datasets including social networks and citation networks. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization and classification, and achieve promising performance compared with state-of-the-art models.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Mathematik, Informatik und Statistik > Informatik |
| Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik |
| Sprache: | Englisch |
| Dokumenten ID: | 82321 |
| Datum der Veröffentlichung auf Open Access LMU: | 15. Dez. 2021, 15:01 |
| Letzte Änderungen: | 15. Dez. 2021, 15:01 |
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