MIFAS : Multi‐source heterogeneous information fusion with adaptive importance sampling for link prediction

2021 ◽  
Author(s):  
Tingting Jiang ◽  
Hao Wang ◽  
Xiangfeng Luo ◽  
Shaorong Xie ◽  
Jingchao Wang
Keyword(s):  
Importance Sampling ◽  
Information Fusion ◽  
Link Prediction ◽  
Adaptive Importance Sampling ◽  
Heterogeneous Information

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

Adaptive Importance Sampling: The past, the present, and the future

2017 ◽  
Vol 34 (4) ◽  
pp. 60-79 ◽  
Author(s):  
Monica F. Bugallo ◽  
Victor Elvira ◽  
Luca Martino ◽  
David Luengo ◽  
Joaquin Miguez ◽  
...  
Keyword(s):  
Importance Sampling ◽  
Adaptive Importance Sampling ◽  
The Past ◽  
The Future
Sign in / Sign up

Export Citation Format

Share Document