Edge attribute-enhanced community discovery in social networks

Noha Mohamad S Alduaiji

Research output: ThesisDoctoral Thesis

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Abstract

This thesis investigates the problem of community discovery. We define and propose solutions for three sub-problems. First sub-problem is discovering temporal interaction biased communities. To solve this problem we proposed an influence propagation model and TIS-Community detection. Second sub-problem is partitioning a graph for overlapping community discovery. Our solution involves developing an objective function to partition the graph by decomposing data and distributing them evenly across the available processors. Third sub-problem is discovering positive-persistent communities. We developed two models to address this problem. We evaluate all proposed solutions over real social networks and compare our solutions with state-of-the-art methods.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Award date2 Apr 2019
DOIs
Publication statusUnpublished - 2019

Cite this

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title = "Edge attribute-enhanced community discovery in social networks",
abstract = "This thesis investigates the problem of community discovery. We define and propose solutions for three sub-problems. First sub-problem is discovering temporal interaction biased communities. To solve this problem we proposed an influence propagation model and TIS-Community detection. Second sub-problem is partitioning a graph for overlapping community discovery. Our solution involves developing an objective function to partition the graph by decomposing data and distributing them evenly across the available processors. Third sub-problem is discovering positive-persistent communities. We developed two models to address this problem. We evaluate all proposed solutions over real social networks and compare our solutions with state-of-the-art methods.",
keywords = "Community discovery, Social networks, Temporal interations, Data analysis, Partitioning algorithm, Influence propagation model, Twitter, Sentiment analysis",
author = "Alduaiji, {Noha Mohamad S}",
year = "2019",
doi = "10.26182/5cd10a3d6078d",
language = "English",
school = "The University of Western Australia",

}

Alduaiji, NMS 2019, 'Edge attribute-enhanced community discovery in social networks', Doctor of Philosophy, The University of Western Australia. https://doi.org/10.26182/5cd10a3d6078d

Edge attribute-enhanced community discovery in social networks. / Alduaiji, Noha Mohamad S.

2019.

Research output: ThesisDoctoral Thesis

TY - THES

T1 - Edge attribute-enhanced community discovery in social networks

AU - Alduaiji, Noha Mohamad S

PY - 2019

Y1 - 2019

N2 - This thesis investigates the problem of community discovery. We define and propose solutions for three sub-problems. First sub-problem is discovering temporal interaction biased communities. To solve this problem we proposed an influence propagation model and TIS-Community detection. Second sub-problem is partitioning a graph for overlapping community discovery. Our solution involves developing an objective function to partition the graph by decomposing data and distributing them evenly across the available processors. Third sub-problem is discovering positive-persistent communities. We developed two models to address this problem. We evaluate all proposed solutions over real social networks and compare our solutions with state-of-the-art methods.

AB - This thesis investigates the problem of community discovery. We define and propose solutions for three sub-problems. First sub-problem is discovering temporal interaction biased communities. To solve this problem we proposed an influence propagation model and TIS-Community detection. Second sub-problem is partitioning a graph for overlapping community discovery. Our solution involves developing an objective function to partition the graph by decomposing data and distributing them evenly across the available processors. Third sub-problem is discovering positive-persistent communities. We developed two models to address this problem. We evaluate all proposed solutions over real social networks and compare our solutions with state-of-the-art methods.

KW - Community discovery

KW - Social networks

KW - Temporal interations

KW - Data analysis

KW - Partitioning algorithm

KW - Influence propagation model

KW - Twitter

KW - Sentiment analysis

U2 - 10.26182/5cd10a3d6078d

DO - 10.26182/5cd10a3d6078d

M3 - Doctoral Thesis

ER -