Knowledge Graph
A visual map of concepts in your research area. See clusters, gaps, and connections at a glance.
What It Is
The Knowledge Graph is an interactive 2D force-directed graph where:
- Nodes = key concepts (e.g., "transformer", "attention", "time series")
- Edges = relationships (co-occurrence in your papers)
- Size = frequency of the concept
Think of it as a social network for ideas.
When to Use It
- After collecting 5+ papers with insights
- To identify sub-fields within your topic
- To find isolated clusters (unexplored intersections)
- To decide where your research could make a novel contribution
How to Generate
- Go to Insights
- Ensure you have several papers with insights
- Click Generate Knowledge Graph
The graph loads in a few seconds. You can pan, zoom, and drag nodes.
Reading the Graph
- Tightly connected cluster: Well-studied area; hard to contribute something new
- Sparse nodes: Concepts that appear alone—maybe a niche worth exploring
- Bridge nodes: Concepts that connect clusters are good candidates for interdisciplinary work
Hover a node to see which papers mention it.
Customization
Currently, the graph is auto-generated from your paper insights. You cannot manually add/remove nodes (future versions may allow curation).
Switch projects to see different graphs.
Example
Suppose you search for "deep learning for weather forecasting". The graph might show clusters around:
- RNNs and LSTMs (older methods)
- Transformers (newer)
- Numerical weather prediction (physics-based)
If you notice a small bridge between transformers and numerical methods, that could be your research gap.
Limitations
- Depends on quality of AI-extracted concepts
- Works best with at least 10 papers
- No filtering yet (coming soon)
Next
Put your graph to work by staying organized with Kanban and Grants.