Version 1
: Received: 21 December 2017 / Approved: 21 December 2017 / Online: 21 December 2017 (16:39:01 CET)
How to cite:
Fang, Y.; Zhao, X.; Tan, Z. Network Embedding via a Bi-Mode and Deep Neural Network Model. Preprints2017, 2017120156. https://doi.org/10.20944/preprints201712.0156.v1
Fang, Y.; Zhao, X.; Tan, Z. Network Embedding via a Bi-Mode and Deep Neural Network Model. Preprints 2017, 2017120156. https://doi.org/10.20944/preprints201712.0156.v1
Fang, Y.; Zhao, X.; Tan, Z. Network Embedding via a Bi-Mode and Deep Neural Network Model. Preprints2017, 2017120156. https://doi.org/10.20944/preprints201712.0156.v1
APA Style
Fang, Y., Zhao, X., & Tan, Z. (2017). Network Embedding via a Bi-Mode and Deep Neural Network Model. Preprints. https://doi.org/10.20944/preprints201712.0156.v1
Chicago/Turabian Style
Fang, Y., Xiang Zhao and Zhen Tan. 2017 "Network Embedding via a Bi-Mode and Deep Neural Network Model" Preprints. https://doi.org/10.20944/preprints201712.0156.v1
Abstract
Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
