What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Social network, Artificial intelligence, Information retrieval, Machine learning and Social influence are his primary areas of study.
The Social network study combines topics in areas such as Data science, Maximization, Dynamic network analysis and Mood.
His work on Statistical model as part of general Artificial intelligence research is often related to Factor graph, thus linking different fields of science.
Jie Tang interconnects The Internet and Support vector machine in the investigation of issues within Information retrieval.
The study incorporates disciplines such as Probabilistic logic, Baseline, Data mining and Empirical research in addition to Machine learning.
His Social influence research includes themes of Social media, Microblogging, Cognitive psychology and Graphical model.
His most cited work include:
- ArnetMiner: extraction and mining of academic social networks (1349 citations)
- Social influence analysis in large-scale networks (672 citations)
- Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (375 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Information retrieval, Social network, Machine learning and Data mining.
His Artificial intelligence research incorporates themes from Graph and Natural language processing.
His studies deal with areas such as Matching and Cluster analysis as well as Information retrieval.
He has included themes like Social influence, Social media, Theoretical computer science and Data science in his Social network study.
He has researched Theoretical computer science in several fields, including Embedding and Graph.
As part of the same scientific family, he usually focuses on Machine learning, concentrating on Dynamic network analysis and intersecting with Network science.
He most often published in these fields:
- Artificial intelligence (33.63%)
- Information retrieval (22.72%)
- Social network (21.16%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (33.63%)
- Information retrieval (22.72%)
- Graph (8.46%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Information retrieval, Graph, Machine learning and Theoretical computer science.
His Artificial intelligence study often links to related topics such as Natural language processing.
His study in Information retrieval is interdisciplinary in nature, drawing from both Quality, Representation, Preference and Leverage.
His Graph course of study focuses on Social influence and Social network.
Jie Tang combines topics linked to Matching with his work on Machine learning.
His Theoretical computer science research includes elements of Embedding, Transduction, Laplacian matrix, Graph and Node.
Between 2017 and 2021, his most popular works were:
- Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (375 citations)
- DeepInf: Social Influence Prediction with Deep Learning (133 citations)
- Representation Learning for Attributed Multiplex Heterogeneous Network (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Theoretical computer science, Machine learning, Graph and Feature learning.
His Artificial intelligence research is multidisciplinary, relying on both Recommendation model and Selection.
His research in Machine learning intersects with topics in Self attention, Paragraph and Social media.
His work focuses on many connections between Graph and other disciplines, such as Representation, that overlap with his field of interest in Vulnerability, Social representation, Social network, Social influence and Feature engineering.
Jie Tang usually deals with Deep learning and limits it to topics linked to Ranking and Information retrieval.
His Information retrieval study incorporates themes from Model selection and Profiling.
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