.png){kind=icon}
返回
Multi-Path Relationship Preserved Social Network Embedding
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2900920.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Social network embedding, namely, embedding social network nodes into a low-dimensional space, is the foundation of social network analysis, such as node classification and link prediction. Although many existing methods attempt to address this task, most of them only consider the shallow relationship between two nodes in the network, which ignore capturing multiple and semantic-rich social relationships between users. To this end, we define such multiple and semantic-rich relationships as multi-path relationships, and propose a multi-path relationship preserved social network embedding method named MPR-SNE, which is based on the recurrent neural network framework that incorporates both social network structure and node profile information. Specifically, we first utilize random walks to explore the multiple social relationship paths between nodes. Then, a new recurrent unit called bi-directional multi-path relationship unit is proposed to better capture the properties of multi-path relationships. Finally, two objective functions are designed to seamlessly integrate social network structure and node profile information into node representation. The experimental results on two real-world networks show that MPR-SNE outperforms the state-of-the-art baselines on node classification task and link prediction task.
Keyword:
Social network embedding
multi-path relationship
node profile information
RNN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Insurance activity and economic performance: Fresh evidence from asymmetric panel causality testsOAAI
OAAI分享收藏Passive signal propagation and membrane properties in median photoreceptors of the giant barnacleOAAI
OAAI分享收藏Unipolar memristive Switching in Bulk Negative Temperature Coefficient Thermosensitive Ceramics.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏
查看更多内容