Parallel PageRank Algorithm Using MapReduce

Improvement of PageRank algorithm by ant colony algorithm

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.02726 ◽

2009 ◽

Vol 29 (10) ◽

pp. 2726-2728

Author(s):

Yue-wei DING ◽

Hui GUO

Keyword(s):

Ant Colony Algorithm ◽

Ant Colony ◽

Pagerank Algorithm

Download Full-text

Key phrase Extraction by Improving TextRank with an Integration of Word Embedding and Syntactic Information

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200820155846 ◽

2020 ◽

Vol 13 ◽

Author(s):

Sheng Zhang ◽

Qi Luo ◽

Yukun Feng ◽

Ke Ding ◽

Daniela Gifu ◽

...

Keyword(s):

Semantic Information ◽

Performance Enhancement ◽

Word Embedding ◽

The Other ◽

Test Set ◽

Pagerank Algorithm ◽

Phrase Extraction ◽

Extraction Algorithm ◽

Syntactic Information ◽

Key Phrase Extraction

Background: As a known key phrase extraction algorithm, TextRank is an analogue of PageRank algorithm, which relied heavily on the statistics of term frequency in the manner of co-occurrence analysis. Objective: The frequency-based characteristic made it a neck-bottle for performance enhancement, and various improved TextRank algorithms were proposed in the recent years. Most of improvements incorporated semantic information into key phrase extraction algorithm and achieved improvement. Method: In this research, taking both syntactic and semantic information into consideration, we integrated syntactic tree algorithm and word embedding and put forward an algorithm of Word Embedding and Syntactic Information Algorithm (WESIA), which improved the accuracy of the TextRank algorithm. Results: By applying our method on a self-made test set and a public test set, the result implied that the proposed unsupervised key phrase extraction algorithm outperformed the other algorithms to some extent.

Download Full-text

PageRank Implemented with the MPI Paradigm Running on a Many-Core Neuromorphic Platform

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea11020025 ◽

2021 ◽

Vol 11 (2) ◽

pp. 25

Author(s):

Evelina Forno ◽

Alessandro Salvato ◽

Enrico Macii ◽

Gianvito Urgese

Keyword(s):

Parallel Execution ◽

Spiking Neural Networks ◽

Massively Parallel ◽

Current Version ◽

Hardware Platform ◽

Pagerank Algorithm ◽

Programming Paradigm ◽

Neuromorphic Hardware ◽

Efficient Communication ◽

Many Core

SpiNNaker is a neuromorphic hardware platform, especially designed for the simulation of Spiking Neural Networks (SNNs). To this end, the platform features massively parallel computation and an efficient communication infrastructure based on the transmission of small packets. The effectiveness of SpiNNaker in the parallel execution of the PageRank (PR) algorithm has been tested by the realization of a custom SNN implementation. In this work, we propose a PageRank implementation fully realized with the MPI programming paradigm ported to the SpiNNaker platform. We compare the scalability of the proposed program with the equivalent SNN implementation, and we leverage the characteristics of the PageRank algorithm to benchmark our implementation of MPI on SpiNNaker when faced with massive communication requirements. Experimental results show that the algorithm exhibits favorable scaling for a mid-sized execution context, while highlighting that the performance of MPI-PageRank on SpiNNaker is bounded by memory size and speed limitations on the current version of the hardware.

Download Full-text

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

Sustainability ◽

10.3390/su12083457 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3457 ◽

Cited By ~ 1

Author(s):

Ruoxin Zhu ◽

Diao Lin ◽

Yujing Wang ◽

Michael Jendryke ◽

Rui Xin ◽

...

Keyword(s):

Urban Development ◽

Regional Development ◽

Analytical Framework ◽

Pagerank Algorithm ◽

Rich Club ◽

Importance Index ◽

Spring Festival ◽

Spatiotemporal Behavior ◽

Urban And Regional Development ◽

Reform And Opening Up

Regional development differences are a universal problem in the economic development process of countries around the world. In recent decades, China has experienced rapid urban development since the implementation of the reform and opening-up policy. However, development differs across regions, triggering the migration of laborers from underdeveloped areas to developed areas. The interaction between regional development differences and Spring Festival has formed the world’s largest cyclical migration phenomenon, Spring Festival travel. Studying the migration pattern from public spatiotemporal behavior can contribute to understanding the differences in regional development. This paper proposes a geospatial network analytical framework to quantitatively characterize the imbalance of urban/regional development based on Spring Festival travel from the perspectives of complex network science and geospatial science. Firstly, the urban development difference is explored based on the intercity population flow difference ratio, PageRank algorithm, and attractiveness index. Secondly, the community detection method and rich-club coefficient are applied to further observe the spatial interactions between cities. Finally, the regional importance index and attractiveness index are used to reveal the regional development imbalance. The methods and findings can be used for urban planning, poverty alleviation, and population studies.

Download Full-text