[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127503-en":3,"doc-seo-127503-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127503,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning methods for community detection in networks using known community information","In a network, community detection identifies groups of nodes and edges that form meaningful “communities,” enabling downstream tasks such as summarizing large graphs into community-level representations. This dissertation focuses on mining protein complexes, where communities of interacting proteins accelerate biological discovery and support characterization of structures whose disruption can cause disease. Prior work often relies on unsupervised dense-subgraph clustering and does not scale to large graphs. The study builds supervised and reinforcement learning approaches that leverage known community information, improving accuracy with parallel scalability.","Copyright by  \nMeghana Venkata Palukuri 2022  \nThe Dissertation Committee for Meghana Venkata Palukuri Certifies that this is the approved version of the following Dissertation:  \nMachine learning methods for community detection in networks using  \nknown community information  \nCommittee:  \nEdward M. Marcotte, Supervisor  \nRachel Ward  \nKarl Schulz  \nRon Elber  \nClaus Wilke  \nMachine learning methods for community detection in networks using  \nknown community information  \nby  \nMeghana Venkata Palukuri  \nDissertation  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDoctor of Philosophy  \nThe University of Texas at Austin  \nAugust 2022  \nDedication  \nTo Amma, Naanna and Kalyani.  \nAcknowledgments  \nI am extremely grateful to Edward, for being an amazing and compassionate guide during my Ph.D., giving me the freedom to learn and explore several research ideas of my interest, and for supporting me through this journey.  \nI also want to acknowledge my undergraduate advisors and alma mater, IIT Madras for encouraging me to pursue a Ph.D., and my parents for being supportive of my decision to do so, especially in moving to a different country (after 23 years of living in the same city as them) .  \nI am grateful to my parents, sister, roommates, colleagues, friends, relatives, the Marcotte lab members, the Oden Institute members, UT, and Austin, for providing a wonderful social environment that helped me work on my Ph.D. over the last 5 years of my life.  \nA special mention to Claire McWhite and Kevin Drew who introduced me to computational work in the lab, Ridhi Patil for being an amazing mentee on my second project on reinforcement learning, and Eric Verbeke and Caitie McCafferty for helpful discussions on my third project on projection clustering. I am grateful to all the professors who have taught me different courses at UT and IITM; my internship mentors at Schlumberger and Amazon, and my Ph.D. committee members who have all contributed to my holistic development as a researcher.  \nAbstract  \nMachine learning methods for community detection in networks using  \nknown community information  \nMeghana Venkata Palukuri, Ph.D.  \nThe University of Texas at Austin, 2022  \nSupervisor: Edward M. Marcotte  \nIn a network, the problem of community detection refers to finding groups of nodes and edges that form ‘communities’ relevant to the field, such as groups of people with common interests in social networks and fraudulent websites linked to each other on the web. Community detection also yields downstream use-cases such as the summarization of massive networks into smaller networks of communities. We are most interested in mining protein complexes, i.e., communities of interacting proteins, accelerating biological experiments by providing candidates for previously unknown protein complexes. Characterization of protein complexes is important, as they play essential roles in cellular functions and their disruption often leads to disease. Previous methods in community detection comprise a majority of unsupervised graph clustering strategies, which work on the assumption that communities are dense subgraphs in a network-which is not always true. Also, many community detection algorithms are in-memory and serial and do not scale to large networks. In this dissertation, we use knowledge from communities, including rich features from graph nodes, with supervised and reinforcement learning, improving on accuracies, with parallel algorithms ensuring high performance and  \nscalability. Specifically, we work on (1) learning a community fitness function using supervised machine learning methods with AutoML; (2) a distributed algorithm for finding candidate communities using multiple heuristics; (3) learning to walk trajectories on a network leading to communities with reinforcement learning and (4) feature augmentation with graph node informati","cbCaihsIu9xpw0YE","https://ap.wps.com/l/cbCaihsIu9xpw0YE","pdf",6518718,1,291,"English","en",105,"# Chapter 1. Background\n## Community embeddings\n## Fitness functions with supervised methods\n## Candidate community search methods\n## Existing supervised methods","[{\"question\":\"What problem does community detection address in networks?\",\"answer\":\"Community detection finds groups of nodes and edges that form relevant communities, such as people with shared interests in social networks or related fraudulent sites on the web.\"},{\"question\":\"Why does the dissertation focus on protein complexes?\",\"answer\":\"Protein complexes are communities of interacting proteins, and identifying them provides candidates for previously unknown complexes, accelerating biological experiments and supporting disease-related biological understanding.\"},{\"question\":\"How does the dissertation improve on earlier community detection methods?\",\"answer\":\"It uses known community information with supervised learning and reinforcement learning, adds parallel algorithms for scalability, and works beyond assumptions that communities are always dense subgraphs.\"}]","Machine learning methods for community detection in networks using known community information | 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problem does community detection address in networks?","Question",{"text":76,"@type":77},"Community detection finds groups of nodes and edges that form relevant communities, such as people with shared interests in social networks or related fraudulent sites on the web.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why does the dissertation focus on protein complexes?",{"text":81,"@type":77},"Protein complexes are communities of interacting proteins, and identifying them provides candidates for previously unknown complexes, accelerating biological experiments and supporting disease-related biological understanding.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the dissertation improve on earlier community detection methods?",{"text":85,"@type":77},"It uses known community information with supervised learning and reinforcement learning, adds parallel algorithms for scalability, and works beyond assumptions that communities are always dense 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