What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- The Internet
Linyuan Lü mainly focuses on Complex network, Data mining, Probability and statistics, Centrality and Link.
His work carried out in the field of Complex network brings together such families of science as Function, Topology, Asynchronous communication and Data science.
In general Data mining, his work in Network model is often linked to CPU time, Similarity, Path and Index linking many areas of study.
His Probability and statistics study incorporates themes from Theoretical computer science, Cluster analysis, Structure, Network size and Randomness.
The various areas that Linyuan Lü examines in his Centrality study include Spite, Message passing, State and Identification.
Linyuan Lü incorporates Link and Mechanism in his research.
His most cited work include:
- Link prediction in complex networks: A survey (1693 citations)
- Link prediction in complex networks: A survey (1693 citations)
- Link prediction in complex networks: A survey (1693 citations)
What are the main themes of his work throughout his whole career to date?
Linyuan Lü mainly investigates Complex network, Data mining, Theoretical computer science, Data science and Recommender system.
His research integrates issues of Node, Centrality, Robustness and Link in his study of Complex network.
While working in this field, Linyuan Lü studies both Link and Mechanism.
His studies in Data mining integrate themes in fields like Probability and statistics, Similarity, Link and Relevance.
His Data science research includes themes of Learning to rank, Ranking, Popularity and Identification.
His work on Collaborative filtering as part of general Recommender system research is often related to Process, thus linking different fields of science.
He most often published in these fields:
- Complex network (41.43%)
- Data mining (32.86%)
- Theoretical computer science (20.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Ranking (17.86%)
- Network science (15.71%)
- Theoretical computer science (20.00%)
In recent papers he was focusing on the following fields of study:
Ranking, Network science, Theoretical computer science, Complex network and Node are his primary areas of study.
Linyuan Lü combines subjects such as Citation data, Citation and PageRank with his study of Ranking.
His Network science research integrates issues from Learning to rank, Ranking, Recommender system, Social system and Data science.
Linyuan Lü merges Theoretical computer science with Synchronization networks in his research.
He has included themes like Preference and Venture capital in his Complex network study.
The concepts of his Node study are interwoven with issues in Probability and statistics, Network complexity, Robustness and Benchmark.
Between 2019 and 2021, his most popular works were:
- Deep Collaborative Filtering for Prediction of Disease Genes (11 citations)
- History-dependent percolation on multiplex networks (8 citations)
- History-dependent percolation on multiplex networks (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- The Internet
The scientist’s investigation covers issues in Network science, Ranking, Artificial intelligence, Structure and Functional correlation.
His Network science research is multidisciplinary, incorporating elements of Learning to rank, Ranking, Identification and Social system.
His Ranking study combines topics in areas such as Citation data, Citation and PageRank.
His work deals with themes such as Collaborative filtering and Data mining, which intersect with Artificial intelligence.
Linyuan Lü integrates many fields in his works, including Structure, Percolation process, Critical phenomena, Network structure and Statistical physics.
With his scientific publications, his incorporates both Functional correlation and Monte Carlo method.
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