English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Link prediction is a key aspect of graph machine learning, with applicationsas diverse as disease prediction, social network recommendations, and drugdiscovery. It involves predicting new links that may form between networknodes. Despite the clear importance of link prediction, existing models havesignificant shortcomings. Graph Convolutional Networks, for instance, have beenproven to be highly efficient for link prediction on a variety of datasets.However, they encounter severe limitations when applied to short-path networksand ego networks, resulting in poor performance. This presents a criticalproblem space that this work aims to address. In this paper, we present theNode Centrality and Similarity Based Parameterised Model (NCSM), a novel methodfor link prediction tasks. NCSM uniquely integrates node centrality andsimilarity measures as edge features in a customised Graph Neural Network (GNN)layer, effectively leveraging the topological information of large networks.This model represents the first parameterised GNN-based link prediction modelthat considers topological information. The proposed model was evaluated onfive benchmark graph datasets, each comprising thousands of nodes and edges.Experimental results highlight NCSM's superiority over existingstate-of-the-art models like Graph Convolutional Networks and Variational GraphAutoencoder, as it outperforms them across various metrics and datasets. Thisexceptional performance can be attributed to NCSM's innovative integration ofnode centrality, similarity measures, and its efficient use of topologicalinformation.

A parameterised model for link prediction using node centrality and similarity measure based on graph embedding