SYNAPSE-4C (SYNthetic Academic Professional Social Ecosystem - 4 Connections)

Description

This dataset is a synthetic academic multiplex social network consisting of approximately 3000 researcher nodes represented across multiple relational layers, including co-authorship, project collaboration, grant collaboration, and academic interaction. The dataset includes interconnected files containing researcher profiles, publication records, project details, grant information, and academic interaction events. The dataset incorporates academic realism through role-based constraints, domain-aware collaboration, temporal consistency, weighted relationships, interdisciplinarity scores, topic vectors, and heterogeneous researcher participation. It is suitable for community detection, overlapping community detection, multiplex fusion, link prediction, graph-based deep learning, collaborator recommendation, and interdisciplinary academic network analysis. Description and Usage of SYNAPSE-4C: 1. Fully synthetic - no real personal data, privacy-safe for open sharing and benchmarking 2. Researcher nodes are shared identically across all four layers, enabling true multiplex analysis 3. Four distinct relational layers capture different collaboration mechanisms: co-authorship, project, grant, and interaction 4. Five clean tabular data files - profiles, publications, projects, grants, interactions - each independently usable or joinable 5. Edge weights encode relationship strength or frequency, not just binary presence or absence 6. Temporal fields on every relationship support snapshot analysis, time-slicing, and dynamic graph modeling 7. Topic vectors per researcher node and explicit interdisciplinarity scores enable semantic and cross-domain analysis 8. Role-based and domain-aware generation rules produce graph behavior that mirrors real academic networks 9. Isolated and low-activity nodes are deliberately kept, preserving natural sparsity and making benchmarks more realistic 10.Purpose-built for community detection, link prediction, multiplex layer fusion, and graph neural network experiments

Institutions

• Pondicherry University

  Puducherry, Puducherry

Categories

Licence