Search: Link prediction — Academic Articles

LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks

Chunning Wang, Fengqin Tang, Xuejing Zhao • Mathematics (2023)

The individuals of real-world networks participate in various types of connections, each forming a layer in multiplex networks. Link prediction is an important problem in multiplex network analysis owing to its wide range of practical appli...

DOAJ Open Access (DOAJ) DOI: 10.3390/math11143256

View Article » Original Source ↗

Heterogeneous Graph Neural Network

Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami et al. • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2019)

Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...

OPENALEX Open Access DOI: https://doi.org/10.1145/3292500.3330961

View Article » Original Source ↗

Link Predictions with Bi-Level Routing Attention

Yu Wang, Shu Xu, Zenghui Ding, Cong Liu et al. • AI (2025)

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing a...

DOAJ Open Access (DOAJ) DOI: 10.3390/ai6070156

View Article » Original Source ↗

A Topical Review of Graph Embedding in Graph Neural Networks

Willian Borges De Lemos, Lucas De Angelo Martins Ribeiro, Vanessa Telles Da Silva, Alessandro De Lima Bicho et al. • IEEE Access (2025)

Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support tasks such as node classification, link prediction ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3643796

View Article » Original Source ↗

MCoGCN-motif high-order feature-guided embedding learning framework for social link prediction

Nan Xiang, Wenjing Yang, Xindi Rao • Scientific Reports (2024)

Abstract Traditional social link prediction models primarily concentrate on the adjacency features of the network, overlooking the rich high-order structural information within. Therefore, the study of effective extraction and encoding of t...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-80509-9

View Article » Original Source ↗

Academic Articles & Research Papers

LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks

Heterogeneous Graph Neural Network

Link Predictions with Bi-Level Routing Attention

A Topical Review of Graph Embedding in Graph Neural Networks

MCoGCN-motif high-order feature-guided embedding learning framework for social link prediction