Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. 1 Learni ng Layers Contextuali zed versus Structural Overlapping Community Structures in Social Media Mohsen Shahriari Ying Li Ralf Klamma This slide deck is licensed under a Creative Commons Attribution- ShareAlike 3.0 Unported License . Contextualized versus Structural Overlapping Communities in Social Media Mohsen Shahriari, Sabrina Haefele, Ralf Klamma Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany {shahriari, haefele, klamma}@db is.rwth-aachen.de Chair of Computer Science 5 RWTH Aachen University
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Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke1
LearningLayers
Contextualized versus Structural Overlapping Community Structures in Social Media
Mohsen ShahriariYing LiRalf Klamma
This slide deck is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Contextualized versus Structural Overlapping Communities in Social Media
Mohsen Shahriari, Sabrina Haefele, Ralf KlammaAdvanced Community Information Systems (ACIS)
Contextualized versus Structural Overlapping Community Structures in Social Media
Mohsen ShahriariYing LiRalf Klamma
Challenges regarding Content-based OCD
Imperceptible knowledge regarding significance of content – Community events e.g., releases in open source developer network– Correlation of content and structural properties of the social media
Few of them detect overlapping community structures– Detecting only disjoint community structures
Most of the methods are not suitable for thread-based data structures– Needs huge tuning
Most of the approaches do not work on actual posts/contents– Use mainly attributes/tags
Contextualized versus Structural Overlapping Community Structures in Social Media
Mohsen ShahriariYing LiRalf Klamma
Structural/Content-Based OCD Approaches
First we introduce the baselines used in this work– Disassortative degree Mixing and Information Diffusion (DMID)– Speaker-listener Label Propagation Algorithm (SLPA)– Stanoev, Smikov and Kocarev (SSK)– Algorithm by Li, Zhang, Liu, Chen and Zhang (CLIZZ)
Then we introduce the proposed Content-based methods– Cost function optimization clustering algorithm (CFOCA)– Term community merging algorithm (TCMA)– Combining content and structural values
Contextualized versus Structural Overlapping Community Structures in Social Media
Mohsen ShahriariYing LiRalf Klamma
Conclusion & Future Works Conclusion & Message:
Content has significant effect on structural-based techniques– Changing in community sizes, number of overlapping nodes and modularity– Content-based methods detect bigger community sizes with bigger overlaps
Future Works:
Investigate local similarity costs Improving time complexity
Contextualized versus Structural Overlapping Community Structures in Social Media
Mohsen ShahriariYing LiRalf Klamma
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