Community Detection Algorithm for Social Networking: FaNClust
We are thankful to Almighty Allah for his blessings for the successful completion of our thesis. Ourheartiestgratitude,profoundindebtednessanddeeprespectgotooursupervisor, Md. Mahboob Karim, Instructor Class-A, Department of Computer Science and Engineering, Military Institute of Science and Technol...
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Department of Computer Science and Engineering, Military Institute of Science and Technology
2015
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| Online-Zugang: | http://hdl.handle.net/123456789/150 |
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| author | Imraul Quyes Imrul, Md Rubaiyat Rahman, Syed Rijuan Bin Mosharraf, M. Nafeh Mahmud, Faisal |
| author_browse | Bin Mosharraf, M. Nafeh Imraul Quyes Imrul, Md Mahmud, Faisal Rubaiyat Rahman, Syed Rijuan |
| author_facet | Imraul Quyes Imrul, Md Rubaiyat Rahman, Syed Rijuan Bin Mosharraf, M. Nafeh Mahmud, Faisal |
| author_sort | Imraul Quyes Imrul, Md |
| collection | DSpace |
| description | We are thankful to Almighty Allah for his blessings for the successful completion of our thesis. Ourheartiestgratitude,profoundindebtednessanddeeprespectgotooursupervisor, Md. Mahboob Karim, Instructor Class-A, Department of Computer Science and Engineering, Military Institute of Science and Technology, for his constant supervision, affectionate guidance and great encouragement and motivation. His keen interest on the topic and valuable advices throughout the study was of great help in completing thesis.
We are thankful to our co-supervisor Md. Shamsur Rahman, Doctoral Student (Ph.D. Candidate), Clayton School of Information Technology, Monash University, Clayton, Victoria, Australia for his continuous guidance & support.
We are especially grateful to the Department of Computer Science and Engineering (CSE) of Military Institute of Science and Technology (MIST) for providing their all out support during the thesis work.
Finally, we would like to thank our families and our course mates for their appreciable assistance, patience and suggestions during the course of our thesis. |
| format | Thesis |
| id | oai:localhost:123456789-150 |
| institution | My University |
| language | English |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Computer Science and Engineering, Military Institute of Science and Technology |
| publisherStr | Department of Computer Science and Engineering, Military Institute of Science and Technology |
| record_format | dspace |
| spelling | oai:localhost:123456789-1502015-08-04T08:22:31Z Community Detection Algorithm for Social Networking: FaNClust Imraul Quyes Imrul, Md Rubaiyat Rahman, Syed Rijuan Bin Mosharraf, M. Nafeh Mahmud, Faisal Community, Detection, Algorithm, Social Networking, FaNClust We are thankful to Almighty Allah for his blessings for the successful completion of our thesis. Ourheartiestgratitude,profoundindebtednessanddeeprespectgotooursupervisor, Md. Mahboob Karim, Instructor Class-A, Department of Computer Science and Engineering, Military Institute of Science and Technology, for his constant supervision, affectionate guidance and great encouragement and motivation. His keen interest on the topic and valuable advices throughout the study was of great help in completing thesis. We are thankful to our co-supervisor Md. Shamsur Rahman, Doctoral Student (Ph.D. Candidate), Clayton School of Information Technology, Monash University, Clayton, Victoria, Australia for his continuous guidance & support. We are especially grateful to the Department of Computer Science and Engineering (CSE) of Military Institute of Science and Technology (MIST) for providing their all out support during the thesis work. Finally, we would like to thank our families and our course mates for their appreciable assistance, patience and suggestions during the course of our thesis. Theproblemofcommunitydetectioninsocialmediahasbeenwidelystudiedinthe social networking community in the context of the structure of the underlying graphs. Mostcommunitydetectionalgorithmsusethelinksbetweenthenodesinordertodeterminethedenseregionsinthegraph. Thesedenseregionsarethecommunitiesofsocial mediainthegraph. Suchmethodsaretypicallybasedpurelyonthelinkagestructureof the underlying social media network. Community detection algorithms are fundamentaltoolsthatallowustouncoverorganizationalprinciplesinnetworks. Whendetecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. Inthispaper,weexplorearangeofnetworkcommunitydetectionmethodsinorder tocomparethemandtounderstandtheirrelativeperformanceandthesystematicbiases in the clusters they identify. We evaluate several common objective functions that are usedtoformalizethenotionofanetworkcommunity,andweexamineseveraldifferent classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best community of any size, we consider a size-resolved version of the optimization problem. Consideringcommunityqualityasafunctionofitssizeprovidesamuchfiner lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior. And we propose a new algorithm Fast Network Clustering Algorithm (FaNClust) for better performance. Department of Computer Science and Engineering, Military Institute of Science and Technology 2015-07-05T05:26:00Z 2015-07-05T05:26:00Z 2014-12 Thesis http://hdl.handle.net/123456789/150 en B.Sc. in Computer Science and Engineering Thesis; application/pdf Department of Computer Science and Engineering, Military Institute of Science and Technology |
| spellingShingle | Community, Detection, Algorithm, Social Networking, FaNClust Imraul Quyes Imrul, Md Rubaiyat Rahman, Syed Rijuan Bin Mosharraf, M. Nafeh Mahmud, Faisal Community Detection Algorithm for Social Networking: FaNClust |
| title | Community Detection Algorithm for Social Networking: FaNClust |
| title_full | Community Detection Algorithm for Social Networking: FaNClust |
| title_fullStr | Community Detection Algorithm for Social Networking: FaNClust |
| title_full_unstemmed | Community Detection Algorithm for Social Networking: FaNClust |
| title_short | Community Detection Algorithm for Social Networking: FaNClust |
| title_sort | community detection algorithm for social networking fanclust |
| topic | Community, Detection, Algorithm, Social Networking, FaNClust |
| url | http://hdl.handle.net/123456789/150 |
| work_keys_str_mv | AT imraulquyesimrulmd communitydetectionalgorithmforsocialnetworkingfanclust AT rubaiyatrahmansyedrijuan communitydetectionalgorithmforsocialnetworkingfanclust AT binmosharrafmnafeh communitydetectionalgorithmforsocialnetworkingfanclust AT mahmudfaisal communitydetectionalgorithmforsocialnetworkingfanclust |