Online social network, has become an important platform for people to communicate and get information. Micro-blog, the convenient and quick platform showed strong development momentum. The paper ...

The PageRank algorithm places a hypothetical Web surfer at a Web site (or node in a network) and assumes that the surfer will move on to a site linked at the original with a probability of 1-d.

In this paper, we propose an innovative Graph Neural Network (GNN) model that combines PageRank, genetic algorithm, and Graph Convolutional neural Network (GCN) to solve the problem of influence ...

Social Network Analysis and Mining (2022). [4] HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge . Micromachines (2023).

A literature professor has developed software using Google's PageRank algorithm that has identified Jane Austen and Walter Scott as the most influential authors of the 1800s.. Matthew Jockers of ...

Twitter is learning first-hand about the challenges of eliminating racial bias in algorithms. The social network’s Liz Kelley said the company had “more analysis” to do after cryptographic ...

Google’s PageRank algorithm is the idea that the importance of a webpage can be measured by the number of important papers that point towards it. Sergey Brin and Larry Page applied the process ...

Ultimately, this molecular PageRank algorithm might even help with drug design, understanding protein misfolding (which is believed to cause some degenerative diseases), and the analysis of ...

The final ranking results were compared and analyzed with the traditional PageRank algorithm and SRank ranking algorithm, ... and at the same time solves the problem that sentiment analysis cannot be ...

By refining the classic PageRank concept to account for individual node relevance, these algorithms facilitate tailored information retrieval, recommendation systems, and anomaly detection.