{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/78501"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/78501","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"On Theory of Joint Influence","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Huang, Ziyun"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Xu, Jinhui","Computer Science and Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-10-26T02:54:23Z","date_published":"2018-10-26T02:54:23Z","updated_at":"2026-07-27T19:05:09Z","subjects":["computer science"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/78501","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Xu, Jinhui","Computer Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Huang, Ziyun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-10-26T02:54:23Z","2018","2018-06-18 12:53:18"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/78501"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Joint Influence is a kind of phenomena that can be commonly observed in many areas of application. For example, in physics, a particle receives forces from a number of other particles, and such forces jointly effect the motion of the particle. This phenomenon also arises in areas like social networks, where a set of nodes in a community may have a joint influence on a newly added node. To capture the nature of such joint influence and design efficiently algorithms for related applications, we develop a novel and general theoretical framework for joint influence.In this work we are the first to extend the model of Voronoi Diagram, a very fundamental geometry data structure which was created to handle simple ``influence'' from single point, such as geometrical closeness, to support joint influence from a cluster of points to a single point in euclidean space. The generalized Voronoi Diagram is called Clustering Induced Voronoi Diagram (CIVD). Given a set of points, the CIVD partitions the euclidean space base on clusters of given points induced by maximum influence. We use influence function to formally measure joint influence. We discuss general conditions the influence functions should satisfy so that the influence functions admit small size CIVDs which can be built efficiently. We develop a common algorithm to build CIVD for any influence function satisfying the aforementioned conditions, without restricting the form of influence function.We also extend study of the joint influence beyond the CIVD model. First we propose a model called Influence based Voronoi Diagram (IVD) in addition to the CIVD model. The IVD model is different with CIVD model in that the input are given clusters, not input points. Such difference gives rise to possibility of more powerful technique for the IVD model. Then, we extend influence computation to high dimensional space. We develop an efficient sum query algorithm which allows us to efficiently handle joint influence in environments such as database system, where the data are represented as high dimension points. Furthermore, we extent techniques in the CIVD model to develop a novel method called the Range Cover, which is applied to solve the Truth Discovery problem in big data.Finally we provide a solution to the pattern search problem in computer vision by combining CIVD and other novel techniques."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["On Theory of Joint Influence"]}]}],"canonical_facts":{"dc:contributor":["Xu, Jinhui","Computer Science and Engineering"],"dc:creator":["Huang, Ziyun"],"dc:date":["2018-10-26T02:54:23Z","2018","2018-06-18 12:53:18"],"dc:description":["Ph.D.","Joint Influence is a kind of phenomena that can be commonly observed in many areas of application. For example, in physics, a particle receives forces from a number of other particles, and such forces jointly effect the motion of the particle. This phenomenon also arises in areas like social networks, where a set of nodes in a community may have a joint influence on a newly added node. To capture the nature of such joint influence and design efficiently algorithms for related applications, we develop a novel and general theoretical framework for joint influence.In this work we are the first to extend the model of Voronoi Diagram, a very fundamental geometry data structure which was created to handle simple ``influence'' from single point, such as geometrical closeness, to support joint influence from a cluster of points to a single point in euclidean space. The generalized Voronoi Diagram is called Clustering Induced Voronoi Diagram (CIVD). Given a set of points, the CIVD partitions the euclidean space base on clusters of given points induced by maximum influence. We use influence function to formally measure joint influence. We discuss general conditions the influence functions should satisfy so that the influence functions admit small size CIVDs which can be built efficiently. We develop a common algorithm to build CIVD for any influence function satisfying the aforementioned conditions, without restricting the form of influence function.We also extend study of the joint influence beyond the CIVD model. First we propose a model called Influence based Voronoi Diagram (IVD) in addition to the CIVD model. The IVD model is different with CIVD model in that the input are given clusters, not input points. Such difference gives rise to possibility of more powerful technique for the IVD model. Then, we extend influence computation to high dimensional space. We develop an efficient sum query algorithm which allows us to efficiently handle joint influence in environments such as database system, where the data are represented as high dimension points. Furthermore, we extent techniques in the CIVD model to develop a novel method called the Range Cover, which is applied to solve the Truth Discovery problem in big data.Finally we provide a solution to the pattern search problem in computer vision by combining CIVD and other novel techniques."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/78501"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computer science"],"dc:title":["On Theory of Joint Influence"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:09Z"}