Graph Clustering using Random-Walk Similarity | ML with Graphs (Research Paper Walkthrough)

Aug 27, 2021

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prakharmishra137

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#graphclustering #randomwalk #machinelearning What is community detection or graph clustering? - Community Detection or Graph Clustering helps us discover cohesive groups or clusters that share similar properties. This paper uses random-walk similarity to detect such subgraphs and also overcomes the limitation of existing techniques that use modularity optimization. ⏩ Abstract: The technology used to detect community structures in graphs, or graph clustering technology, is important in a wide range of disciplines, such as sociology, biology, and computer science. Previously, many successful community detection methods have relied on the optimization of a quantity referred to as modularity, which is a quality index for the partition of a graph into communities. However, such methods suffer from a key drawback, namely, the inability to identify relatively small communities. To overcome this drawback, we propose a novel community detection method that can detect small communities. This is based on the property that a random walker will not readily leave a community even if it is small. The work presented in this paper demonstrates that our method detects both small and large communities in the practical application of clustering tourist attraction images obtained from Flickr. Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1 ⏩ OUTLINE: 0:00 - Abstract 01:34 - Match Graph and Algorithm Intuition 04:29 - Algorithm Pseudo Code ⏩ Paper Title: Community Detection Using Random-walk Similarity and Application to Image Clustering ⏩ Paper: https://sigport.org/documents/community-detection-using-random-walk-similarity ⏩ Author: Makoto Okuda, Shinichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato ⏩ Organisation: National Institute of Information and Communications Technology, National Institute of Informatics, The University of Tokyo ⏩ IMPORTANT LINKS Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu ********************************************** If you want to support me financially which totally optional and voluntary ❤️ You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee ********************************************** ⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy ⏩ LinkedIn - https://linkedin.com/in/prakhar21 ⏩ Medium - https://medium.com/@prakhar.mishra ⏩ GitHub - https://github.com/prakhar21 ⏩ Twitter - https://twitter.com/rattller ********************************************* Tools I use for making videos :) ⏩ iPad - https://tinyurl.com/y39p6pwc ⏩ Apple Pencil - https://tinyurl.com/y5rk8txn ⏩ GoodNotes - https://tinyurl.com/y627cfsa #techviz #datascienceguy #nlproc #ai #graph #mlwithgraphs

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