| _version_ | 1866902227905413120 |
|---|---|
| author | Saxena, Diwesh |
| author_facet | Saxena, Diwesh |
| contents | <p><strong>Title:</strong> <em>From Talent Pools to Talent Graphs: Rethinking Discoverability in Closed Consultant Networks</em></p> <p>This paper explores the limitations of traditional talent discovery methods in closed consultant ecosystems and introduces a graph-based approach to improve discoverability, contextuality, and matching accuracy. It presents a practical framework for converting consultant databases into traversable talent graphs using real-world examples, system architecture diagrams, and search workflows.</p> <p>The proposed Talent Graph model enables intelligent talent matching, skill inference, and subgraph traversal — providing richer insights beyond static filtering. We compare linear database querying with graph-enabled search to highlight efficiency gains. Practical considerations around data privacy, access control, and cross-platform integration are also discussed.</p> <p>This work is especially relevant for staffing platforms, HR tech startups, and enterprise recruitment systems managing large but closed consultant pools. It lays the foundation for more dynamic, connected, and explainable consultant discovery systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15854209 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Talent Pools to Talent Graphs: Rethinking Discoverability in Closed Consultant Networks Saxena, Diwesh <p><strong>Title:</strong> <em>From Talent Pools to Talent Graphs: Rethinking Discoverability in Closed Consultant Networks</em></p> <p>This paper explores the limitations of traditional talent discovery methods in closed consultant ecosystems and introduces a graph-based approach to improve discoverability, contextuality, and matching accuracy. It presents a practical framework for converting consultant databases into traversable talent graphs using real-world examples, system architecture diagrams, and search workflows.</p> <p>The proposed Talent Graph model enables intelligent talent matching, skill inference, and subgraph traversal — providing richer insights beyond static filtering. We compare linear database querying with graph-enabled search to highlight efficiency gains. Practical considerations around data privacy, access control, and cross-platform integration are also discussed.</p> <p>This work is especially relevant for staffing platforms, HR tech startups, and enterprise recruitment systems managing large but closed consultant pools. It lays the foundation for more dynamic, connected, and explainable consultant discovery systems.</p> |
| title | From Talent Pools to Talent Graphs: Rethinking Discoverability in Closed Consultant Networks |
| url | https://doi.org/10.5281/zenodo.15854209 |