[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117145-en":3,"doc-seo-117145-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117145,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning for Graph Algorithms and Representations","This PhD thesis investigates graph-theoretic problems through a machine learning lens, focusing on practical tasks such as distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction. The work builds a unified perspective on learning from graphs via graph embeddings and graph representations. Empirical studies and method summaries are provided for each problem, including datasets, algorithmic components, and experimental results across synthetic and real-world network settings.","Dartmouth College  \nDartmouth Digital Commons  \n\n| Dartmouth College Ph. D Dissertations | Theses and Dissertations |\n| --- | --- |\n| Spring 5-15-2024\u003Cbr>Machine Learning for Graph Algorithms and Representations\u003Cbr>Allison Gunby-Mann\u003Cbr>Dartmouth College, [allison.mann.th@dartmouth.edu](allison.mann.th@dartmouth.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.dartmouth.edu/dissertations](https://digitalcommons.dartmouth.edu/dissertations)\u003Cbr> Part of the Other Electrical and Computer Engineering Commons |  |\n\nRecommended Citation  \nGunby-Mann, Allison, \"Machine Learning for Graph Algorithms and Representations\" (2024) . Dartmouth College Ph. D Dissertations. 283.  \n[https://digitalcommons.dartmouth.edu/dissertations/283](https://digitalcommons.dartmouth.edu/dissertations/283)  \nThis Thesis (Ph. D.) is brought to you for free and open access by the Theses and Dissertations at Dartmouth Digital Commons. It has been accepted for inclusion in Dartmouth College Ph. D Dissertations by an authorized administrator of Dartmouth Digital Commons. For more information, please contact [dartmouthdigitalcommons@groups.dartmouth.edu](dartmouthdigitalcommons@groups.dartmouth.edu).  \nMachine Learning for Graph Algorithms and Representations  \nA Thesis  \nSubmitted to the Faculty in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nEngineering Sciences  \nby Allison Mann  \nThayer School of Engineering  \nGuarini School of Graduate and Advanced Studies  \nDartmouth College  \nHanover, New Hampshire  \nMAY 2024  \nExamining Committee:  \nChairman   Peter Chin  \nMember   Vikrant Vaze  \nMember   Colin Meyer  \nMember   Wayne Snyder  \nF. Jon Kull, Ph.D.  \nDean of Guarini School of Graduate and Advanced Studies  \nABSTRACT  \nThis thesis explores a variety of common graph theoretic problems from a machine learning perspective. The topics covered include fundamental network problems such as distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction. These projects are unified by the theme of machine learning on graphs, graph embeddings, and representations of graphs.  \nAcknowledgements  \nI am deeply indebted to my husband Benjamin for his patience, understanding, and constant encouragement. His belief in me has been a source of strength and motivation.  \nI would like to express my heartfelt gratitude to my family, especially my mom, dad, brother, and my grandparents for their unwavering support and encouragement throughout this journey. My friends Chelsea, Wendy, Polina, and Scott also were a bastion for me during the entirety of my PhD.  \nI also want to extend my sincere appreciation to my wonderful advisor, Peter Chin, for his tireless and unwavering support. Words cannot express everything he has done for mein the past few years, from personally to professionally.  \nI would like to thank my committee members Vikrant Vaze, Colin Meyer, and Wayne Snyder for their guidance, feedback, and invaluable insights, which have been instrumental in shaping this work.  \nI am also grateful to my dedicated labmates both at Dartmouth College and at Boston University for their camaraderie, collaboration, and intellectual exchange. Their enthusiasm and companionship have enriched my research experience.  \nLastly, I would like to acknowledge the contributions of all those who have collaborated with me in this endeavor. Especially my co-authors Ike Abioye, Davin Jeong, Sarel Cohen, Felicia Schenkelberg, and many others. Your encouragement and assistance have been integral to the completion of this thesis.  \nContents  \n1 Introduction and Purpose 1  \n1.1 Graph Applications .............................. 2  \n1.2 Node Embeddings and Graph Representations ................ 3  \n1.3 Graph Theory and Machine Learning ..................... 4  \n1.4 Overview ................................... 5  \n2 Distance Labeling 8  \n2.1 Abstract ............................","cbCaidPRZMbGPbuQ","https://ap.wps.com/l/cbCaidPRZMbGPbuQ","pdf",2865876,1,115,"English","en",105,"# Introduction and Purpose\n## Graph Applications\n## Node Embeddings and Graph Representations\n## Graph Theory and Machine Learning\n## Overview\n# Distance Labeling\n## Abstract\n## Background\n## Datasets\n## Method Summary\n## Experimental Results\n## Conclusion\n# Shortest Path Distance Approximation\n## Abstract\n## Background\n## Datasets\n## Method Summary\n## Experimental Results\n## Conclusion\n# Distance Sensitivity Oracle\n## Abstract\n## Background\n## Theoretical Results\n## Datasets\n## Method Summary\n## Experimental Results\n## Conclusion\n# Stochastic Block Model\n## Abstract\n## Background\n## Overview of the Spectral Algorithm for the Standard SBM","[{\"question\":\"What core problems does the thesis address?\",\"answer\":\"It covers distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction from a machine learning perspective.\"},{\"question\":\"How does the thesis connect different topics?\",\"answer\":\"The projects share a common theme of machine learning on graphs, using graph embeddings and representations of graphs as the unifying approach.\"},{\"question\":\"What is included in the methodology for each problem?\",\"answer\":\"Each topic is organized with background, dataset descriptions, method summaries, and experimental results leading to conclusions.\"}]","Machine Learning for Graph Algorithms and Representations | 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