--- name: network-analysis-guide-9e5af1635334 description: "Social network analysis methods, metrics, and visualization tools" metadata: openclaw: emoji: "🌐" category: "domains" subcategory: "social-science" keywords: ["social network analysis", "graph theory", "centrality", "community detection", "network visualization", "SNA"] source: "wentor-research-plugins" --- # Network Analysis Guide A skill for conducting social network analysis (SNA) in research contexts. Covers network data collection and representation, key structural metrics (centrality, density, clustering), community detection algorithms, ego network analysis, longitudinal network models, and visualization best practices using Python NetworkX, igraph, and Gephi. ## Network Data Fundamentals ### Representing Network Data Networks consist of nodes (actors) and edges (relationships). The first decision in any SNA project is how to represent the data. ``` Network data formats: Edge List (simplest): source, target, weight Alice, Bob, 3 Alice, Carol, 1 Bob, David, 5 Adjacency Matrix (for small networks): Alice Bob Carol David Alice 0 3 1 0 Bob 3 0 0 5 Carol 1 0 0 0 David 0 5 0 0 Network types: Undirected: friendship, co-authorship, physical contact Directed: email, citation, following on social media Weighted: frequency of interaction, strength of tie Bipartite: two types of nodes (e.g., people and events) Multiplex: multiple types of edges between same nodes Temporal: edges have timestamps or time windows ``` ### Data Collection Methods ``` Common SNA data collection approaches: Survey-based (name generators): "List up to 5 people you go to for work advice." Advantages: captures subjective relationship perception Limitations: recall bias, boundary specification problem Best for: organizational networks, personal networks Archival data: Email logs, collaboration records, co-authorship Advantages: objective, complete within data boundaries Limitations: may not reflect relationship quality Best for: large-scale communication networks Observation: Systematic recording of interactions Advantages: captures actual behavior Limitations: time-intensive, observer effects Best for: small groups, classroom networks Digital trace data: Social media follows, retweets, mentions Advantages: large-scale, timestamped Limitations: platform-specific behavior, not generalizable Best for: online community studies Important considerations: - Boundary specification: who is included in the network? - Complete vs sampled networks require different methods - IRB/ethics approval needed for human subjects research - Node anonymization required for publication ``` ## Core Network Metrics ### Node-Level Centrality ```python import networkx as nx def compute_centrality_measures(G): """ Compute the four classic centrality measures for all nodes. Each captures a different dimension of node importance: - Degree: connectivity (popular nodes) - Betweenness: brokerage (bridge nodes) - Closeness: reachability (efficient nodes) - Eigenvector: prestige (connected to important nodes) """ centralities = {} # Degree centrality: proportion of nodes connected to centralities["degree"] = nx.degree_centrality(G) # Betweenness: proportion of shortest paths through node centralities["betweenness"] = nx.betweenness_centrality( G, weight="weight", normalized=True ) # Closeness: inverse of average shortest path to all others centralities["closeness"] = nx.closeness_centrality(G) # Eigenvector: connected to other high-centrality nodes try: centralities["eigenvector"] = nx.eigenvector_centrality( G, max_iter=1000, weight="weight" ) except nx.PowerIterationFailedConvergence: centralities["eigenvector"] = {} return centralities ``` ### Network-Level Metrics ```python def compute_network_metrics(G): """ Compute network-level structural properties. """ metrics = {} n = G.number_of_nodes() m = G.number_of_edges() metrics["nodes"] = n metrics["edges"] = m # Density: actual edges / possible edges metrics["density"] = nx.density(G) # Average clustering coefficient: transitivity tendency metrics["avg_clustering"] = nx.average_clustering(G) # Global clustering (transitivity) metrics["transitivity"] = nx.transitivity(G) # Connected components if G.is_directed(): metrics["weakly_connected_components"] = ( nx.number_weakly_connected_components(G) ) else: metrics["connected_components"] = ( nx.number_connected_components(G) ) if nx.is_connected(G): metrics["diameter"] = nx.diameter(G) metrics["avg_shortest_path"] = ( nx.average_shortest_path_length(G) ) # Degree distribution statistics degrees = [d for n, d in G.degree()] metrics["avg_degree"] = sum(degrees) / len(degrees) metrics["max_degree"] = max(degrees) return metrics def interpret_metrics(metrics): """ Provide interpretive context for network metrics. """ interpretations = [] if metrics["density"] > 0.5: interpretations.append( "High density: most actors are connected. " "Information spreads quickly but network is " "resource-intensive to maintain." ) elif metrics["density"] < 0.1: interpretations.append( "Low density: sparse connections. Network " "may have structural holes and brokerage " "opportunities." ) if metrics["avg_clustering"] > 0.5: interpretations.append( "High clustering: strong tendency to form " "closed triads. Indicates group cohesion " "and potential echo chambers." ) return interpretations ``` ## Community Detection ### Algorithms for Finding Groups ```python import community as community_louvain def detect_communities_multiple(G): """ Apply multiple community detection algorithms and compare. Different algorithms may reveal different structural patterns. """ results = {} # Louvain method (modularity optimization) results["louvain"] = community_louvain.best_partition( G, weight="weight" ) results["louvain_modularity"] = ( community_louvain.modularity(results["louvain"], G) ) # Label Propagation (fast, non-deterministic) lp_communities = nx.community.label_propagation_communities(G) lp_partition = {} for i, comm in enumerate(lp_communities): for node in comm: lp_partition[node] = i results["label_propagation"] = lp_partition # Girvan-Newman (edge betweenness, slow but interpretable) # Only practical for small networks (< 1000 nodes) if G.number_of_nodes() < 500: gn_communities = nx.community.girvan_newman(G) top_level = next(gn_communities) gn_partition = {} for i, comm in enumerate(top_level): for node in comm: gn_partition[node] = i results["girvan_newman"] = gn_partition return results ``` ## Ego Network Analysis ### Analyzing Individual Networks ``` Ego network concepts: Ego: the focal actor Alters: ego's direct contacts Ties: connections between alters (not through ego) Key ego network measures: - Size: number of alters - Density: proportion of possible alter-alter ties that exist - Constraint: Burt's measure of structural holes - Low constraint = access to diverse information - High constraint = redundant contacts - Effective size: size minus redundancy of contacts - Ego betweenness: brokerage within the ego network Research applications: - Social support and health outcomes - Innovation diffusion and adoption - Career success and social capital - Information access and decision-making ``` ## Visualization Best Practices ### Layout and Design ``` Network visualization guidelines: Layout algorithms: - Force-directed (Fruchterman-Reingold, ForceAtlas2): Best for: showing clusters, general structure Use when: exploring data, presenting to general audience - Circular: Best for: showing connectivity patterns Use when: comparing density across groups - Hierarchical (Sugiyama): Best for: directed acyclic graphs Use when: showing flow or hierarchy Visual encoding: - Node size: proportional to centrality or attribute value - Node color: community membership or categorical attribute - Edge width: relationship strength or frequency - Edge color: relationship type (in multiplex networks) Publication standards: - Use colorblind-friendly palettes - Include a legend for all visual encodings - Report the layout algorithm used - State N (nodes) and M (edges) in the caption - For large networks, consider filtering to top-k nodes - Provide the network data in supplementary materials Tools: - Gephi: interactive exploration, ForceAtlas2 layout - Python pyvis: interactive HTML visualizations - R igraph: publication-quality static figures - Cytoscape: biological networks, rich plugin ecosystem ``` Social network analysis provides a structural perspective on social phenomena that complements traditional individual-level analyses. By examining patterns of relationships rather than attributes of individuals, SNA reveals how position in a social structure shapes behavior, information access, influence, and outcomes. --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/social-science/network-analysis-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/social-science/network-analysis-guide/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Marketing Roles: Data Analyst, Data Scientist, Marketing Analyst, Researcher, Social Media Manager Specialties: API Tools: Gephi, Igraph, Networkx Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: e0836286bebb326048c131f7a1fe583aabc41ad14666d8133814fac01c71e694