REBORN: Transfer Learning Based Social Network Alignment

2021 ◽  
Author(s):  
Shuai Gao ◽  
Zhongbao Zhang ◽  
Sen Su ◽  
Philip S. Yu
Keyword(s):  
Social Network ◽  
Transfer Learning ◽  
Network Alignment

scholarly journals DNA: Dynamic Social Network Alignment

2019 ◽  
Author(s):  
Li Sun ◽  
Zhongbao Zhang ◽  
Pengxin Ji ◽  
Jian Wen ◽  
Sen Su ◽  
...  
Keyword(s):  
Social Network ◽  
Network Alignment ◽  
Dynamic Social Network

scholarly journals Joint Link Prediction and Network Alignment via Cross-graph Embedding

2019 ◽  
Author(s):  
Xingbo Du ◽  
Junchi Yan ◽  
Hongyuan Zha
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Graph Embedding ◽  
Network Alignment ◽  
Initial Vertex ◽  
Information Propagation ◽  
Graph Node ◽  
Embedding Technique

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.

Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding

2021 ◽  
Author(s):  
Zhehan Liang ◽  
Yu Rong ◽  
Chenxin Li ◽  
Yunlong Zhang ◽  
Yue Huang ◽  
...  
Keyword(s):  
Social Network ◽  
Large Scale ◽  
Network Alignment ◽  
Network Embedding

scholarly journals Adversarial Learning for Weakly-Supervised Social Network Alignment

2019 ◽  
Vol 33 ◽  
pp. 996-1003 ◽  
Author(s):  
Chaozhuo Li ◽  
Senzhang Wang ◽  
Yukun Wang ◽  
Philip Yu ◽  
Yanbo Liang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Alignment ◽  
Social Identities ◽  
Projection Function ◽  
Adversarial Learning ◽  
Learning Framework ◽  
Multiple Datasets ◽  
Research Challenges ◽  
Weakly Supervised

Nowadays, it is common for one natural person to join multiple social networks to enjoy different kinds of services. Linking identical users across multiple social networks, also known as social network alignment, is an important problem of great research challenges. Existing methods usually link social identities on the pairwise sample level, which may lead to undesirable performance when the number of available annotations is limited. Motivated by the isomorphism information, in this paper we consider all the identities in a social network as a whole and perform social network alignment from the distribution level. The insight is that we aim to learn a projection function to not only minimize the distance between the distributions of user identities in two social networks, but also incorporate the available annotations as the learning guidance. We propose three models SNNAu, SNNAb and SNNAo to learn the projection function under the weakly-supervised adversarial learning framework. Empirically, we evaluate the proposed models over multiple datasets, and the results demonstrate the superiority of our proposals.

MC2:Unsupervised Multiple Social Network Alignment

2019 ◽  
Author(s):  
Gen Li ◽  
Li Sun ◽  
Zhongbao Zhang ◽  
Pengxin Ji ◽  
Sen Su ◽  
...  
Keyword(s):  
Social Network ◽  
Network Alignment
Sign in / Sign up

Export Citation Format

Share Document