Sources and algorithms
Sources are values from the current graph that you can put in a channel or filter. They include fields, algorithm results, selections, saved sets, and transformation results. A transformation changes the working graph passed to later sources.
Algorithm families
Section titled “Algorithm families”| Family | Shipped examples | Typical result |
|---|---|---|
| Centrality | Degree, betweenness, closeness, PageRank, eigenvector, harmonic, Katz | Numeric node source |
| Community | Louvain, Leiden, label propagation, greedy modularity, K-clique | Categorical or set source |
| Spectral | Spectral bisection, Kernighan–Lin bisection | Partition source |
| Paths | Shortest path, ego network, reachable-from-source | Selection or set source |
| Structure | K-core, clustering coefficient, bridges, articulation points, largest component | Numeric, category, or set source |
| Edge analysis | Edge betweenness, Jaccard, Adamic–Adar, resource allocation, edge bridges | Numeric edge source |
| Sparsification | Spanning tree, disparity-filter backbone | Transformed working graph |
The exact command and parameter surface is available through the Automation API. Use the workbench to inspect each result in the graph and table.
Communities and node types
Section titled “Communities and node types”New graphs start with degree sizing and Louvain coloring on networks up to 10,000 nodes and 100,000 edges. Larger networks start with neutral colors; choose Color → Louvain or another community source to analyze them. Computation and subsequent resolution changes run automatically in a worker; there is no separate Run step. The Communities section on the right reads that same result, including its membership and colors. Algorithm and resolution controls stay on the left. Opening a summary does not run a different community algorithm.
The largest eight groups appear first. Expand Remaining groups to search or page through the complete partition. Each row shows its node count and share of all nodes; clicking selects those nodes. Disconnected components remain distinct.
Louvain and Leiden use eight deterministic restarts. Among results within 0.005 modularity of the best restart at the chosen resolution, Garphield prefers fewer groups. This reduces fragmentation without forcing arbitrary merges or changing your resolution. On networks up to 10,000 nodes and 100,000 edges, approximate group counts at the slider’s endpoints appear after foreground work settles. Each estimate uses one restart; the applied result still uses eight.
Explicit color bindings from users or saved documents are preserved on larger graphs.
For semantic categories such as Person or Organization, choose Color → Node type…, then select the field that defines the type. This treats even numeric codes as categories and preserves the original data. Missing values appear as Unspecified in the type summary. The field choice is saved with the binding and restored with the view.
Degree summary
Section titled “Degree summary”The right panel starts with the highest-degree nodes, showing names and incident edge counts for the top five nodes. Click a node to select it. Degree distribution shows degree bands, with node counts and percentages. A band containing one node shows its exact degree. Bars represent the share of all nodes in each range.
Degree counts incoming plus outgoing edges on directed graphs. Parallel edges count separately, and a self-loop counts twice. It is not a count of unique neighbors. Degree summaries appear independently of slower analysis.
Generators
Section titled “Generators”File → Generate creates seeded, reproducible test graphs such as random, small-world, scale-free, tree, grid, cycle, star, wheel, barbell, and bipartite networks. Use a generator when you need a controlled example rather than an imported dataset.
Transformations
Section titled “Transformations”Transformations form a non-destructive stack. They can extract the largest component, a spanning tree, a K-core, or a disparity-filter backbone. Reorder, toggle, and clear transformations from the filter stack; the original dataset and project history remain available.
A binding changes what appears on the canvas. A transformation changes the nodes and edges passed to later layout and analysis steps.
See Explore and analyze.
Result types
Section titled “Result types”- Numeric values can drive size, color, edge width, or a heatmap.
- Category values can drive color, shape, labels, or contours.
- Sets can drive borders, contours, selection emphasis, and annotations.
- Edge results stay attached to edges and can drive edge color, width, or labels where the channel accepts them.