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 38 Citations 5,154 128 World Ranking 5102 National Ranking 2519

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Algorithm
  • Machine learning

His primary areas of investigation include Theoretical computer science, Data mining, Graph, Vertex and Reachability. His work deals with themes such as Metric, Core, Set, Maximal set and Range, which intersect with Theoretical computer science. His work on ID3 algorithm, Fractal tree index, Decision tree learning and FSA-Red Algorithm as part of general Data mining study is frequently linked to Hoeffding's inequality, bridging the gap between disciplines.

His Graph study combines topics in areas such as Transitive closure, Set cover problem, Directed acyclic graph and Bounded function. His Vertex research is multidisciplinary, incorporating perspectives in Link analysis, Dense graph, Bipartite graph and Vertex. His Transitive reduction study integrates concerns from other disciplines, such as Graph database, Minimum degree spanning tree, Spanning tree and Directed graph.

His most cited work include:

  • 3-HOP: a high-compression indexing scheme for reachability query (169 citations)
  • A Survey of Algorithms for Dense Subgraph Discovery (159 citations)
  • Efficient decision tree construction on streaming data (139 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Data mining, Theoretical computer science, Graph, Artificial intelligence and Parallel computing. Ruoming Jin has included themes like Cluster analysis, Graph, Set and Automatic summarization in his Data mining study. His Theoretical computer science study combines topics from a wide range of disciplines, such as Metric, Mathematical optimization, Approximation algorithm, Scale and Robustness.

His Vertex study in the realm of Graph interacts with subjects such as Shortest distance. His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Social network. The various areas that he examines in his Parallel computing study include Scalability and Distributed shared memory.

He most often published in these fields:

  • Data mining (31.82%)
  • Theoretical computer science (27.27%)
  • Graph (18.18%)

What were the highlights of his more recent work (between 2016-2021)?

  • Theoretical computer science (27.27%)
  • Artificial intelligence (13.64%)
  • Robustness (5.19%)

In recent papers he was focusing on the following fields of study:

Ruoming Jin mostly deals with Theoretical computer science, Artificial intelligence, Robustness, Adversarial system and Differential privacy. The concepts of his Theoretical computer science study are interwoven with issues in Data modeling, Autoencoder, Graph, Pairwise comparison and Graph. Ruoming Jin has researched Graph in several fields, including Multi-label classification and Degree.

His Artificial intelligence study combines topics in areas such as Social media, Machine learning and Metric. While the research belongs to areas of Metric, Ruoming Jin spends his time largely on the problem of Recommender system, intersecting his research to questions surrounding Data mining. His work carried out in the field of Adversarial system brings together such families of science as Scalability and Leverage.

Between 2016 and 2021, his most popular works were:

  • Privacy-aware smart city: A case study in collaborative filtering recommender systems (20 citations)
  • Density-Adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification (11 citations)
  • Heterogeneous Gaussian mechanism: Preserving differential privacy in deep learning with provable robustness (10 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Algorithm
  • Machine learning

His scientific interests lie mostly in Theoretical computer science, Differential privacy, Robustness, Adversarial system and Noise. His studies in Theoretical computer science integrate themes in fields like Autoencoder, Matching, Consistency, Pairwise comparison and Homophily. His Autoencoder research integrates issues from Data modeling, Feature learning, Generative adversarial network and Graph.

Ruoming Jin has researched Differential privacy in several fields, including Property, Deep learning, Artificial intelligence and Scale. His Robustness study frequently intersects with other fields, such as Gaussian noise. His research integrates issues of Scalability, Distributed computing and Leverage in his study of Adversarial system.

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

Efficient decision tree construction on streaming data

Ruoming Jin;Gagan Agrawal.
knowledge discovery and data mining (2003)

238 Citations

3-HOP: a high-compression indexing scheme for reachability query

Ruoming Jin;Yang Xiang;Ning Ruan;David Fuhry.
international conference on management of data (2009)

236 Citations

Shared Memory Paraellization of Data Mining Algorithms: Techniques, Programming Interface, and Performance.

Ruoming Jin;Gagan Agrawal.
siam international conference on data mining (2002)

221 Citations

A Survey of Algorithms for Dense Subgraph Discovery

Victor E. Lee;Ning Ruan;Ruoming Jin;Charu C. Aggarwal.
Managing and Mining Graph Data (2010)

210 Citations

Efficiently answering reachability queries on very large directed graphs

Ruoming Jin;Yang Xiang;Ning Ruan;Haixun Wang.
international conference on management of data (2008)

203 Citations

Shared memory parallelization of data mining algorithms: techniques, programming interface, and performance

Ruoming Jin;Ge Yang;G. Agrawal.
IEEE Transactions on Knowledge and Data Engineering (2005)

201 Citations

A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search

Jie Tang;Ruoming Jin;Jing Zhang.
international conference on data mining (2008)

194 Citations

Distance-constraint reachability computation in uncertain graphs

Ruoming Jin;Lin Liu;Bolin Ding;Haixun Wang.
very large data bases (2011)

175 Citations

An algorithm for in-core frequent itemset mining on streaming data

Ruoming Jin;G. Agrawal.
international conference on data mining (2005)

159 Citations

Fast and exact out-of-core and distributed k -means clustering

Ruoming Jin;Anjan Goswami;Gagan Agrawal.
Knowledge and Information Systems (2006)

131 Citations

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