.png){kind=icon}
返回
Community aware random walk for network embedding
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2018.02.028.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally expensive because of feature learning for different applications. In recent years, many researches have focused on feature learning methods in social networks. Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the network. In this paper, we introduce a novel algorithm named CARE for network embedding that can be used for different types of networks including weighted, directed and complex. Current methods try to preserve local neighborhood information of nodes, whereas the proposed method utilizes local neighborhood and community information of network nodes to cover both local and global structure of social networks. CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes. Subsequently, stochastic gradient descent is applied to optimize our objective function and learn the final representation of nodes. Our method can be scalable when new nodes are appended to network without information loss. Parallelize generation of customized random walks is also used for speeding up CARE. We evaluate the performance of CARE on multi label classification and link prediction tasks. Experimental results on various networks indicate that the proposed method outperforms others in both Micro and Macro-fl measures for different size of training data. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Representation learning
Network embedding
Community detection
Skip-gram model
Link prediction
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
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
skn-1, a maternally expressed gene required to specify the fate of ventral blastomeres in the early C. elegans embryo.png)
Cell
CellIF0
PREAI
分享收藏A large-scale evaluation of computational protein function prediction计算蛋白质功能预测的大规模评估NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏
查看更多内容