.png){kind=icon}
返回
Latent Patterns Detection and Interpretation in Multi-Layer Temporal Network
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3010693.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As a time-varying evolution graph, a Multi-Layer Temporal Network (MLTN) contains the temporal dynamic of multiple subgraphs connected by various types of interactions. Compared to a single-layer network, MLTN implicates more information and complicates relationships, which leads the way that the interaction patterns in MLTN are too complicated to be extracted. In this work, we proposed a compatible MLTN encoding method for converting diverse MLTNs into tensors. Next, based on the tensor factoring technique, a tensor can be factored to main components to help us obtain the main interaction patterns in MLTN. However, the factoring result is hard to understand. Therefore, we built an MLTN visual analytic system called MuNeEye to help system users understand the factoring result of MLTN and further explore the latent patterns of MLTN. Three cases show that the method proposed by us is effective in pattern detection from various types of MLTN, such as social networks, conflict relationships between countries, and computer networks.
Keyword:
Tensile stress
Data visualization
Visualization
Electronic mail
Social network services
Image edge detection
Encoding
Tensor factoring
latent pattern detection
multi-layer temporal network
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
.png){kind=icon}
Heavy metal tolerance of marine phytoplankton. IV. Combined effect of zinc and cadmium on growth and uptake in some marine diatoms海洋浮游植物对重金属的耐受性。四。锌和镉对某些海洋硅藻生长和吸收的综合影响
PREAI
分享收藏分享收藏
查看更多内容