16_centrality_analytics

Example 16 — Centrality analytics

What it demonstrates

Two complementary graph-analytics metrics over an immutable CSR snapshot: exact betweenness centrality via Brandes' algorithm (search/centrality.Betweenness), which scores each node by how many shortest paths run through it, and label propagation (search/community.LabelPropagation), which partitions the graph into communities. The example also shows how to make analytics output deterministic in the face of structural ties.

Domain / scenario

A small undirected social network of six people arranged as two triangles joined by a single bridge:

Cluster 1            Cluster 2
  marie               jose
  /   \      bridge   /   \
pierre-anne  marie--jose  ana-luis

Edges: marie--pierre, marie--anne, anne--pierre (cluster 1); jose--ana, jose--luis, luis--ana (cluster 2); and the single marie--jose bridge that links them.

marie and jose are the two bridge endpoints, so every shortest path that crosses between the clusters passes through both — they share the highest betweenness score (12.00) and are structurally symmetric. The remaining four nodes lie on no inter-cluster shortest path and score 0.00. Label propagation recovers the two original triangles as communities.

How to run

go run ./examples/16_centrality_analytics

Expected output

Betweenness (higher = more critical):
  jose    12.00
  marie   12.00
  ana     0.00
  anne    0.00
  luis    0.00
  pierre  0.00

Label propagation clusters:
  community 0: [ana jose luis]
  community 1: [anne marie pierre]

The output is fully deterministic. Because marie and jose tie at 12.00 (and four nodes tie at 0.00), the ranking breaks score ties by node name; the cluster listing sorts both the community IDs and the member names. Without those tie-breakers the order would depend on sort.Slice instability and Go map-iteration order.

Key APIs

• graph/adjlist.New / AdjList.AddEdge — build the mutable undirected network.
• graph/csr.BuildFromAdjList — freeze the builder into an immutable CSR snapshot for analytics.
• graph/adjlist.AdjList.Mapper / graph.Mapper.Resolve — translate compact NodeIDs back to person names.
• search/centrality.Betweenness — exact Brandes betweenness centrality, returned as a NodeID-indexed []float64.
• search/community.LabelPropagation / DefaultLabelPropagationOptions — community detection; Partition.Community is a NodeID-indexed slice of community IDs.

Further reading

• search/centrality — centrality metrics package documentation
• search/community — community-detection package documentation
• graph/csr — the immutable CSR snapshot used as the analytics surface
• Example 08 — PageRank — a related node-importance metric
• docs/examples-standard.md — the standard every example follows