Sadamori Kojaku
Biographic Data
| ID | 9458128 |
|---|---|
| NAME | Sadamori Kojaku |
| GIVEN NAMES | Sadamori |
| FAMILY NAME | Kojaku |
| SIGNATURE | KOJAKU S |
| AFFILIATIONS | Binghamton University |
| ORCID | 0000-0002-9414-6814 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Measuring research interest similarity with transition probabilities
We introduce a family of paper and author similarity measures based on the concept that papers are more similar if they are more likely to be retrieved during a literature search following backward and forward citations. As this browsing process resembles a walk in a citation network, we operationalize the concept using the transition probability (TP) of random walkers. The proposed measures are continuous and symmetric, and can be implemented on…
Representing the disciplinary structure of physics
Recent advances in machine learning offer new ways to represent and study scholarly works and the space of knowledge. Graph and text embeddings provide a convenient vector representation of scholarly works based on citations and text. Yet, it is unclear whether their representations are consistent or provide different views of the structure of science. Here, we compare graph and text embedding by testing their ability to capture the hierarchical …
No prominent works on this page.
Measuring research interest similarity with transition probabilities
We introduce a family of paper and author similarity measures based on the concept that papers are more similar if they are more likely to be retrieved during a literature search following backward and forward citations. As this browsing process resembles a walk in a citation network, we operationalize the concept using the transition probability (TP) of random walkers. The proposed measures are continuous and symmetric, and can be implemented on…
Representing the disciplinary structure of physics
Recent advances in machine learning offer new ways to represent and study scholarly works and the space of knowledge. Graph and text embeddings provide a convenient vector representation of scholarly works based on citations and text. Yet, it is unclear whether their representations are consistent or provide different views of the structure of science. Here, we compare graph and text embedding by testing their ability to capture the hierarchical …
Artificial Intelligence (2 works) · Computer Science (2 works) · Physics (2 works) · Advanced Text Analysis Techniques (1 work) · Biomedical Text Mining and Ontologies (1 work) · Chemistry (1 work) · Discipline (1 work) · Econometrics (1 work) · Embedding (1 work) · Graph (1 work)