DJ Watts

H Ebel

Marcello Trovati

Nik Bessis

O Hein

R Albert

R Feldman

TH Cormen

English

Trovati, Marcello

Bessis, Nik

Name not available

An influence assessment method based on co-occurrence for topologically reduced big data sets

The extraction of meaningful, accurate, and relevant information is at the core of Big Data research. Furthermore, the ability to obtain an insight is essential in any decision-making process, even though the diverse and complex nature of big data sets raises a multitude of challenges. In this paper, we propose a novel method to address the automated assessment of influence among concepts in big data sets. This is carried out by investigating their mutual co occurrence, which is determined via topologically reducing the corresponding network. The main motivation is to provide a toolbox to classify and analyse influence properties, which can be used to investigate their dynamical and statistical behavior, potentially leading to a better understanding and prediction of the properties of the system(s) they model. An evaluation was carried out on two real-world data sets, which were analysed to test the capabilities of our system. The results show the potential of our approach, indicating both accuracy and efficiency

Edge Hill University Research Information Repository

UDORA - University of Derby Online Research Archive

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Soft Computing

AND ARYA A Relevant ventricular septal defect caused by steam pop during ablation of premature ventricular contraction. Circulation

AND BAGDASAR O Influence Discovery in Semantic Networks: An Initial Approach.

AND BARABA´SI A L Statistical Mechanics of Complex Networks.

AND GETOOR L Relationship Identification for Social Network Discovery.

AND POESIO M Acquiring Bayesian Networks from Text.

ASIMAKOPOULOU E, AND BESSIS N An Analytical Tool to Map Big Data to Networks with Reduced Topologies .

Available from http://www.emsc-csem.org/. [1

Causal Relation Extraction.

Generating Typed Dependency Parses from Phrase Structure Parses.

Modern Graph Theory. Graduate Texts in

Reduced Topologically Real-World Networks: a Big-Data Approach

Sentiment Analysis and Opinion Mining.

The Brown Corpus: A Standard Corpus of Present-Day Edited American English.

The Text Mining Handbook.

to Algorithms.

Using Fuzzy Set Theory and Scale-free Network Properties to Relate MEDLINE Terms.

Why Social Networks Are Different From Other Types of Networks. Physical Review E.

ZELENKAUSKAITE A AND ASIMAKOPOULOU E Extraction, Identification and Ranking of Network Structures from Data Sets.

Zur allgemeinen Kurventheorie.

https://research.edgehill.ac.uk/ws/files/20179817/Soft_Computing_post_review.pdf

An influence assessment method based on co-occurrence for topologically reduced big data sets

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Edge Hill University Research Information Repository

UDORA - University of Derby Online Research Archive

Crossref