---
esco_uri: http://data.europa.eu/esco/skill/33e584f9-ff7a-4979-bfc0-5a3a374a145a
esco_label: "social network analysis"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# social network analysis — Data Scientist

As a Data Scientist, 'Social Network Analysis' (SNA) isn’t about Facebook likes; it's about understanding *relationships* within data as first-class citizens. You'll frequently encounter this when dealing with customer interactions (who influences whom?), fraud detection (identifying collusion rings), recommendation systems (leveraging social connections for better suggestions), or even supply chain optimization (mapping dependencies between suppliers).  Typical tasks involve building network graphs from relational datasets – think transaction logs, communication records, co-authorship data – and then calculating metrics like centrality (who's most important?), community detection (finding groups with strong internal ties), and path length (how efficiently does information flow?).

Tools you’ll use go beyond standard Python/R.  `NetworkX` is a core library for graph manipulation, but you might also leverage `igraph`, specialized graph databases like Neo4j (for very large networks), or visualization tools like Gephi to explore patterns. A key skill isn't just *running* the algorithms, but framing business problems as network problems – recognizing when relationships are crucial to understanding the outcome.  For example, instead of predicting churn based on individual customer attributes, you might predict it based on how connected a customer is to other active users.

A common pitfall is treating all connections equally. A 'friend' relationship on one platform means something different than a financial transaction. Careful feature engineering and understanding the *meaning* of edges are vital. Another is scalability – network analysis can be computationally expensive, so efficient data structures and algorithms (and potentially distributed computing) become necessary for large datasets.  Ignoring directionality (A influences B vs. B influences A) or edge weights (strength of relationship) also leads to misleading results.

Good SNA work for a Data Scientist delivers *actionable* insights. It’s not enough to say “this person is central.” You need to translate that into, “targeting this influencer could increase adoption by X%” or “this cluster of fraudulent accounts shares common characteristics we can use to improve detection rules”.  It's about moving beyond descriptive statistics and using network structure to drive predictive modeling and strategic decision-making.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
