Communities validity: methodical evaluation of community mining algorithms

2013 ◽  
Vol 3 (4) ◽  
pp. 1039-1062 ◽  
Author(s):  
Reihaneh Rabbany ◽  
Mansoureh Takaffoli ◽  
Justin Fagnan ◽  
Osmar R. Zaïane ◽  
Ricardo J. G. B. Campello
Keyword(s):  
Community Mining ◽  
Mining Algorithms

Relative Validity Criteria for Community Mining Algorithms

2018 ◽  
pp. 2202-2216
Author(s):  
Reihaneh Rabbany ◽  
Mansoreh Takaffoli ◽  
Justin Fagnan ◽  
Osmar R. Zaïane ◽  
Ricardo Campello
Keyword(s):  
Relative Validity ◽  
Community Mining ◽  
Validity Criteria ◽  
Mining Algorithms

scholarly journals Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jin Qi ◽  
Fei Jiang ◽  
Xiaojun Wang ◽  
Bin Xu ◽  
Yanfei Sun
Keyword(s):  
Complex Networks ◽  
Clustering Algorithm ◽  
Test Results ◽  
Solution Quality ◽  
Data Set ◽  
Community Mining ◽  
Effectiveness And Efficiency ◽  
Definition Of ◽  
Community Clustering ◽  
Mining Algorithms

With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and appliesExpansionto the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient andhas higher solution qualitycompared with other similar algorithms through the analysis of test results based on network data set.

scholarly journals Empirical Performace Determination on Community Detection Techniques in Social Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 200-204
Keyword(s):  
Community Detection ◽  
Detection Methods ◽  
Data Sets ◽  
Detection Techniques ◽  
Network Applications ◽  
Community Identification ◽  
Community Mining ◽  
Real World Datasets ◽  
Mining Algorithms

Community identification is the high common and extending field of interest in social and real-time network applications. In recent years, many community detection methods have been developed. This paper describes various community discovery methods such as InfoMap, Clique Guided, Louvain, Newman and Eigen Vector that have already been developed and also compares the experimental results of those proposed techniques. The proposed work in this paper experiments these community mining algorithms on the two real-world datasets Twitter and DBLP (Computer Science Bibliography) networks. The identified communities by all the community mining algorithms for these two data sets are described in this proposed work. The quality of the derived communities is evaluated by using standard Extended Modularity metric. The experiment results show that the InfoMap algorithm produces a good modularity score than other community mining algorithms for different sizes of communities on both data sets.

Relative Validity Criteria for Community Mining Algorithms

2014 ◽  
pp. 1562-1576
Author(s):  
Reihaneh Rabbany ◽  
Mansoreh Takaffoli ◽  
Justin Fagnan ◽  
Osmar R. Zaïane ◽  
Ricardo Campello
Keyword(s):  
Relative Validity ◽  
Community Mining ◽  
Validity Criteria ◽  
Mining Algorithms

Relative Validity Criteria for Community Mining Algorithms

2017 ◽  
pp. 1-15
Author(s):  
Reihaneh Rabbany ◽  
Mansoreh Takaffoli ◽  
Justin Fagnan ◽  
Osmar R. Zaïane ◽  
Ricardo Campello
Keyword(s):  
Relative Validity ◽  
Community Mining ◽  
Validity Criteria ◽  
Mining Algorithms

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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