What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
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 most cited work include:
- Fast Random Walk with Restart and Its Applications (800 citations)
- Graph based anomaly detection and description: a survey (625 citations)
- Manifold-ranking based image retrieval (339 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Theoretical computer science (27.96%)
- Artificial intelligence (22.37%)
- Graph (20.39%)
What were the highlights of his more recent work (between 2019-2021)?
- Graph (14.80%)
- Theoretical computer science (27.96%)
- Node (9.54%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Shifu2: A Network Representation Learning Based Model for Advisor-Advisee Relationship Mining (17 citations)
- Node Immunization with Non-backtracking Eigenvalues (6 citations)
- OnionGraph: Hierarchical topology+attribute multivariate network visualization (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
