Node proximity preserved dynamic network embedding via matrix perturbation

2020 ◽  
Vol 196 ◽  
pp. 105822
Author(s):  
Bin Yu ◽  
Bing Lu ◽  
Chen Zhang ◽  
Chunyi Li ◽  
Ke Pan
Keyword(s):  
Dynamic Network ◽  
Matrix Perturbation ◽  
Network Embedding

Exploring Temporal Information for Dynamic Network Embedding

2020 ◽  
pp. 1-1
Author(s):  
Maoguo Gong ◽  
Shunfei Ji ◽  
Yu Xie ◽  
Yuan Gao ◽  
A. K. Qin
Keyword(s):  
Dynamic Network ◽  
Temporal Information ◽  
Network Embedding

D2NE: Deep Dynamic Network Embedding

2020 ◽  
pp. 168-175
Author(s):  
Chao Kong ◽  
Baoxiang Chen ◽  
Shaoying Li ◽  
Qi Zhou ◽  
Dongfang Wang ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Network Embedding

scholarly journals Deep Dynamic Network Embedding for Link Prediction

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 29219-29230 ◽  
Author(s):  
Taisong Li ◽  
Jiawei Zhang ◽  
Philip S. Yu ◽  
Yan Zhang ◽  
Yonghong Yan
Keyword(s):  
Link Prediction ◽  
Dynamic Network ◽  
Network Embedding

Dynamic Network Embedding by Time-Relaxed Temporal Random Walk

2021 ◽  
pp. 426-437
Author(s):  
Yifan Song ◽  
Darong Lai ◽  
Zhihong Chong ◽  
Zeyuan Pan
Keyword(s):  
Random Walk ◽  
Dynamic Network ◽  
Network Embedding

scholarly journals Network Embedding under Partial Monitoring for Evolving Networks

2019 ◽  
Author(s):  
Yu Han ◽  
Jie Tang ◽  
Qian Chen
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Actual Situation ◽  
Challenging Problem ◽  
Network Embedding ◽  
Strong Assumption ◽  
Evolving Networks ◽  
Real World Datasets ◽  
The Web

Network embedding has been extensively studied in recent years. In addition to the works on static networks, some researchers try to propose new models for evolving networks. However, sometimes most of these dynamic network embedding models are still not in line with the actual situation, since these models have a strong assumption that we can achieve all the changes in the whole network, while in fact we cannot do this in some real world networks, such as the web networks and some large social networks. So in this paper, we study a novel and challenging problem, i.e., network embedding under partial monitoring for evolving networks. We propose a model on dynamic networks in which we cannot perceive all the changes of the structure. We analyze our model theoretically, and give a bound to the error between the results of our model and the potential optimal cases. We evaluate the performance of our model from two aspects. The experimental results on real world datasets show that our model outperforms the baseline models by a large margin.

scholarly journals Dynamic Network Embedding Survey

2021 ◽  
Author(s):  
Guotong Xue ◽  
Ming Zhong ◽  
Jianxin Li ◽  
Jia Chen ◽  
Chengshuai Zhai ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Network Embedding

scholarly journals Towards Robust Dynamic Network Embedding

2021 ◽  
Author(s):  
Chengbin Hou ◽  
Ke Tang
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Embedding ◽  
Dynamic Nature ◽  
Unique Character

Dynamic Network Embedding (DNE) has recently drawn much attention due to the dynamic nature of many real-world networks. Comparing to a static network, a dynamic network has a unique character called the degree of changes, which can be defined as the average number of the changed edges between consecutive snapshots spanning a dynamic network. The degree of changes could be quite different even for the dynamic networks generated from the same dataset. It is natural to ask whether existing DNE methods are effective and robust w.r.t. the degree of changes. Towards robust DNE, we suggest two important scenarios. One is to investigate the robustness w.r.t. different slicing settings that are used to generate different dynamic networks with different degree of changes, while another focuses more on the robustness w.r.t. different number of changed edges over timesteps.

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