What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Tina Eliassi-Rad mostly deals with Graph, Theoretical computer science, Artificial intelligence, Data mining and Transfer of learning.
The concepts of her Graph study are interwoven with issues in Topic model, Hierarchical Dirichlet process, Latent class model, Text corpus and Bayesian probability.
Her research investigates the connection between Theoretical computer science and topics such as Graph theory that intersect with problems in Network security, Robustness and Graph.
Her research in Artificial intelligence intersects with topics in Hyperlink, Social network, Hypertext, Telecommunications network and Machine learning.
The study incorporates disciplines such as Electronic document, Statistical classification and Biological network in addition to Social network.
Her work on Data stream mining and Knowledge extraction as part of general Data mining research is frequently linked to Tensor and Value, thereby connecting diverse disciplines of science.
Her most cited work include:
- Collective Classification in Network Data (1279 citations)
- RolX: structural role extraction & mining in large graphs (283 citations)
- Fast best-effort pattern matching in large attributed graphs (229 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Theoretical computer science, Graph, Data mining, Artificial intelligence and Machine learning.
The various areas that she examines in her Theoretical computer science study include Transfer of learning, Algorithm, Graph and Complex network.
Her research ties Robustness and Graph together.
Her study in the fields of Knowledge extraction under the domain of Data mining overlaps with other disciplines such as Data stream.
Her studies in Artificial intelligence integrate themes in fields like Social network and Pattern recognition.
Tina Eliassi-Rad has researched Machine learning in several fields, including Relational database and Inference.
She most often published in these fields:
- Theoretical computer science (36.15%)
- Graph (26.15%)
- Data mining (23.85%)
What were the highlights of her more recent work (between 2018-2021)?
- Theoretical computer science (36.15%)
- Graph (16.15%)
- Graph (26.15%)
In recent papers she was focusing on the following fields of study:
Tina Eliassi-Rad mainly focuses on Theoretical computer science, Graph, Graph, Complex network and Artificial intelligence.
Tina Eliassi-Rad combines subjects such as Node, Social media, Backtracking and Degree distribution with her study of Theoretical computer science.
Her work deals with themes such as Path, Anomaly detection, Laplacian matrix and Embedding, which intersect with Graph.
Her work on Topological graph theory is typically connected to Adversarial machine learning and Network analysis as part of general Graph study, connecting several disciplines of science.
Her Complex network study integrates concerns from other disciplines, such as Approximation algorithm, Shortest path problem and Flow network.
Her Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
Between 2018 and 2021, her most popular works were:
- Multilevel Network Alignment (19 citations)
- The why, how, and when of representations for complex systems (16 citations)
- Non-backtracking cycles: length spectrum theory and graph mining applications (16 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Tina Eliassi-Rad focuses on Theoretical computer science, Graph, Complex system, Graph embedding and Embedding.
Her study in Theoretical computer science is interdisciplinary in nature, drawing from both Social network analysis, Distance, Key and Backtracking.
Her research combines Graph and Graph.
Specifically, her work in Graph is concerned with the study of Topological graph theory.
In her research, Anomaly detection is intimately related to Topological data analysis, which falls under the overarching field of Graph embedding.
Her Embedding research includes themes of Heuristics and Complex network.
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