Community Identification Based on a New Approximate Personalized PageRank Algorithm

2012 ◽  
Vol 4 (20) ◽  
pp. 649-657
Author(s):  
Min Han ◽  
Xianchao Zhang
Keyword(s):  
Personalized Pagerank ◽  
Pagerank Algorithm ◽  
Community Identification

The Micro-Blogging Network Leading Group Recognition Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 164-169
Author(s):  
Li Li Dong ◽  
Ke Yang ◽  
Xiang Zhang
Keyword(s):  
Network Model ◽  
Information Dissemination ◽  
Recognition Algorithm ◽  
Personalized Pagerank ◽  
Network Information ◽  
Pagerank Algorithm ◽  
User Influence ◽  
Transmission Of Information ◽  
Network Transmission ◽  
Ranking Order

Based on the analysis and research of micro-blogging network transmission of information, the transmission of information model is constructed. By studying the network model, a small group of core users of the network information dissemination play a guiding role. To solve the problem that the research of micro-blogging user influence ranking can only ranking order given its influence, but not determine which user play a guiding role in transmission of information, LeadersRank algorithm based on the idea of personalized PageRank algorithm is proposed, and the algorithm is applied to the real micro-blogging data to identify the leading group, the experimental results prove the feasibility and effectiveness of the algorithm.

A Microblogging Opinion Leader Recognition Algorithm Based on MapReduce

2014 ◽  
Vol 571-572 ◽  
pp. 410-415
Author(s):  
Li Li Dong ◽  
Yu Jie Zhu ◽  
Xiang Zhang
Keyword(s):  
Parallel Algorithm ◽  
Programming Model ◽  
Opinion Leader ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Massive Data ◽  
Cloud Platform ◽  
Personalized Pagerank ◽  
Pagerank Algorithm

Related researches on the influence of microblogging users are only given users’ influence ranking, while cannot determine the problem that which user plays a guiding role in the dissemination of information in the microblogging network. This paper proposed a microblogging opinion leader recognition algorithm called LeadersRank based on personalized PageRank algorithm. On the basis of LeadersRank algorithm research, since the problem of that current microblogging information has been massive data, using the idea of MapReduce programming model to improve the LeadersRank algorithm, so that designed a LeadersRank distributed parallel algorithm based on MapReduce algorithm running in the cloud platform hadoop environment. Finally, experiments verified the effectiveness of the two methods, and made analysis of experimental results.

scholarly journals Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings

2018 ◽  
Author(s):  
Debanjan Mahata ◽  
John Kuriakose ◽  
Rajiv Ratn Shah ◽  
Roger Zimmermann ◽  
John R. Talburt
Keyword(s):  
State Of The Art ◽  
Keyword Extraction ◽  
Personalized Pagerank ◽  
Text Documents ◽  
Pagerank Algorithm ◽  
Scientific Papers ◽  
Benchmark Datasets ◽  
Evaluation Dataset ◽  
And Training ◽  
Better Than

Keyword extraction is a fundamental task in naturallanguage processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases. Keywords from text documents are primarily extracted using supervised and unsupervised approaches. In this paper, we present an unsupervised technique that uses a combination of theme-weighted personalized PageRank algorithm and neural phrase embeddings for extracting and ranking keywords. Wealso introduce an efficient way of processing text documents and training phrase embeddings using existing techniques. We share an evaluation dataset derived from an existing dataset that is used for choosing the underlying embedding model. The evaluations for ranked keyword extraction are performed on two benchmark datasets comprising of short abstracts (Inspec), and long scientific papers (SemEval 2010), and is shown to produce results better than the state-of-the-art systems.

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

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.

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

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.

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