D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 52 Citations 11,742 234 World Ranking 2595 National Ranking 1381

Research.com Recognitions

Awards & Achievements

2020 - ACM Distinguished Member

Overview

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.

Best Publications

Fast Random Walk with Restart and Its Applications

Hanghang Tong;Christos Faloutsos;Jia-yu Pan.
international conference on data mining (2006)

957 Citations

Graph based anomaly detection and description: a survey

Leman Akoglu;Hanghang Tong;Danai Koutra.
Data Mining and Knowledge Discovery (2015)

932 Citations

Activity recognition with smartphone sensors

Xing Su;Hanghang Tong;Ping Ji.
Tsinghua Science & Technology (2014)

448 Citations

Manifold-ranking based image retrieval

Jingrui He;Mingjing Li;Hong-Jiang Zhang;Hanghang Tong.
acm multimedia (2004)

438 Citations

RolX: structural role extraction & mining in large graphs

Keith Henderson;Brian Gallagher;Tina Eliassi-Rad;Hanghang Tong.
knowledge discovery and data mining (2012)

378 Citations

Blur detection for digital images using wavelet transform

Hanghang Tong;Mingjing Li;Hongjiang Zhang;Changshui Zhang.
international conference on multimedia and expo (2004)

375 Citations

Center-piece subgraphs: problem definition and fast solutions

Hanghang Tong;Christos Faloutsos.
knowledge discovery and data mining (2006)

354 Citations

Random walk with restart: fast solutions and applications

Hanghang Tong;Christos Faloutsos;Jia-Yu Pan.
Knowledge and Information Systems (2008)

330 Citations

Fast best-effort pattern matching in large attributed graphs

Hanghang Tong;Christos Faloutsos;Brian Gallagher;Tina Eliassi-Rad.
knowledge discovery and data mining (2007)

313 Citations

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)

255 Citations

Best Scientists Citing Hanghang Tong

Christos Faloutsos

Christos Faloutsos

Carnegie Mellon University

Publications: 83

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 65

U Kang

U Kang

Seoul National University

Publications: 41

Huan Liu

Huan Liu

Arizona State University

Publications: 37

Feng Xia

Feng Xia

Federation University Australia

Publications: 35

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 34

Leman Akoglu

Leman Akoglu

Carnegie Mellon University

Publications: 34

Jeffrey Xu Yu

Jeffrey Xu Yu

Chinese University of Hong Kong

Publications: 29

Tina Eliassi-Rad

Tina Eliassi-Rad

Northeastern University

Publications: 28

Xian-Sheng Hua

Xian-Sheng Hua

Microsoft (United States)

Publications: 26

Xuemin Lin

Xuemin Lin

UNSW Sydney

Publications: 24

James Cheng

James Cheng

Chinese University of Hong Kong

Publications: 24

Xueqi Cheng

Xueqi Cheng

Chinese Academy of Sciences

Publications: 22

Jie Tang

Jie Tang

Tsinghua University

Publications: 21

Aristides Gionis

Aristides Gionis

Royal Institute of Technology

Publications: 21

Xiangjie Kong

Xiangjie Kong

Zhejiang University of Technology

Publications: 21

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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