2020 - ACM Distinguished Member
Hanghang Tong spends much of his time researching Theoretical computer science, Scalability, Graph, Artificial intelligence and Data mining. His research in Theoretical computer science intersects with topics in Graph, Recommender system, Graph property, Line graph and Bipartite graph. His Scalability study integrates concerns from other disciplines, such as Transfer of learning, Network topology, Anomaly detection and Ranking SVM.
In his study, which falls under the umbrella issue of Graph, Graph partition, PageRank and Algorithm is strongly linked to Graph theory. His biological study spans a wide range of topics, including Machine learning and Pattern recognition. The concepts of his Data mining study are interwoven with issues in Active learning, Image, Image retrieval, Relevance feedback and Ranking.
His scientific interests lie mostly in Theoretical computer science, Artificial intelligence, Graph, Data mining and Scalability. His study in Theoretical computer science is interdisciplinary in nature, drawing from both Node, Graph theory, Graph drawing and Key. His studies examine the connections between Key and genetics, as well as such issues in Information retrieval, with regards to Ranking.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. In Graph, Hanghang Tong works on issues like Graph, which are connected to Embedding. His Data mining research includes elements of Boosting, Collaborative filtering and Cluster analysis.
Hanghang Tong mainly focuses on Graph, Theoretical computer science, Node, Graph and Key. Hanghang Tong interconnects Centrality, Network science, Data mining and Information retrieval in the investigation of issues within Graph. Hanghang Tong performs integrative study on Theoretical computer science and Set.
Hanghang Tong has researched Graph in several fields, including Upper and lower bounds and Recurrent neural network. His Key research is multidisciplinary, incorporating perspectives in Variety, Field, Data science and Big data. His work is dedicated to discovering how Complement, Pattern recognition are connected with Artificial intelligence and other disciplines.
His main research concerns Graph, Theoretical computer science, Graph, Node and Information retrieval. His Graph study incorporates themes from Perspective, Algorithm, Data mining and Matrix completion. His Data mining research is multidisciplinary, relying on both Social network analysis, Network element and Leverage.
His Theoretical computer science study combines topics in areas such as Recurrent neural network and Upper and lower bounds. His Graph research is multidisciplinary, incorporating perspectives in Centrality, Mutual information and Network dynamics. Hanghang Tong has included themes like Embedding, Topology, Graph drawing and Network topology in his Node study.
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Fast Random Walk with Restart and Its Applications
Hanghang Tong;Christos Faloutsos;Jia-yu Pan.
international conference on data mining (2006)
Graph based anomaly detection and description: a survey
Leman Akoglu;Hanghang Tong;Danai Koutra.
Data Mining and Knowledge Discovery (2015)
Activity recognition with smartphone sensors
Xing Su;Hanghang Tong;Ping Ji.
Tsinghua Science & Technology (2014)
Manifold-ranking based image retrieval
Jingrui He;Mingjing Li;Hong-Jiang Zhang;Hanghang Tong.
acm multimedia (2004)
RolX: structural role extraction & mining in large graphs
Keith Henderson;Brian Gallagher;Tina Eliassi-Rad;Hanghang Tong.
knowledge discovery and data mining (2012)
Blur detection for digital images using wavelet transform
Hanghang Tong;Mingjing Li;Hongjiang Zhang;Changshui Zhang.
international conference on multimedia and expo (2004)
Random walk with restart: fast solutions and applications
Hanghang Tong;Christos Faloutsos;Jia-Yu Pan.
Knowledge and Information Systems (2008)
Center-piece subgraphs: problem definition and fast solutions
Hanghang Tong;Christos Faloutsos.
knowledge discovery and data mining (2006)
Fast best-effort pattern matching in large attributed graphs
Hanghang Tong;Christos Faloutsos;Brian Gallagher;Tina Eliassi-Rad.
knowledge discovery and data mining (2007)
It's who you know: graph mining using recursive structural features
Keith Henderson;Brian Gallagher;Lei Li;Leman Akoglu.
knowledge discovery and data mining (2011)
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