Professor Aven

Tutorial Overview

This tutorial guides you through analyzing your position in the class social networks (trust, advice, and communication) to understand how you’re perceived as a leader, where you have influence, and what your structural position reveals about your network style.

Module Context

Module 1 of the final project: Class Network Position Analysis

This is the first of five diagnostic modules that build toward your comprehensive leadership profile:

Class Network Analysis ← You are here
Professional Network Analysis (LinkedIn)
Organizational Structure Diagnostic
Cultural Environment Audit
Strategic Leadership Synthesis

Quick Facts

Data Source: Survey-based social network analysis of class relationships in trust, advice, and communication

Tools Required:

Your SNA ID (provided by instructor)
Access to class network visualizations and data tables (provided in this document)
Understanding of network centrality measures

Deliverable: Analysis + interpretation + visuals for Section 1 of final project

Learning Objectives

By completing this analysis, you will be able to:

Interpret centrality metrics (degree, betweenness, closeness, eigenvector) in directed and undirected networks
Diagnose your structural position across trust, advice, and communication networks
Identify leadership patterns revealed by your network positioning
Assess gaps and opportunities in how peers perceive and rely on you

Theoretical Foundation

Why Class Networks Matter for Leadership

“The first and most overlooked leadership skill is knowing how others experience you.”

Social network analysis reveals your position in the informal structure of power, trust, and information flow—dimensions that predict real influence far more than titles or formal authority.

The Hidden Architecture of Influence

In any organization or team, there are two structures:

Formal structure: Org charts, titles, reporting lines
Informal structure: Trust networks, advice-seeking patterns, communication flows

The informal structure is where:

Real decisions get made
Information actually flows
Influence is gained or lost
Reputations are built

This analysis makes the invisible visible.

Three Critical Networks

1. Trust Network (Directed)

Question: “Who do you trust?”

What it reveals:

Moral credibility and psychological safety
Who is seen as having integrity and empathy
Foundations for long-term collaboration

High trust in-degree = Others see you as trustworthy, creating space for difficult conversations and authentic relationships

High trust out-degree = You extend trust broadly, signaling openness and vulnerability

2. Advice Network (Directed)

Question: “Who do you go to for advice?”

What it reveals:

Expertise and competence in your domain
Knowledge brokerage potential
Influence without formal authority

High advice in-degree = You’re seen as an expert; people seek your input

High advice betweenness = You bridge disconnected groups, translating between different knowledge domains

3. Communication Network (Undirected)

Question: “Who do you communicate with regularly?”

What it reveals:

Operational relationships and day-to-day coordination
Information access and social integration
Foundation for informal influence

High communication degree = You’re well-connected and socially embedded

High communication eigenvector = Your connections are themselves well-connected, giving you access to informal power

Key Concepts to Apply

Centrality Measures: What They Mean for Leadership

Centrality Measures and Leadership Implications
Measure	Definition	Implication
Degree Centrality	Number of connections (undirected networks)	Social integration; visibility; access to diverse perspectives
In-Degree	Number of incoming ties (directed networks)	Reputation; sought-after expertise or trustworthiness
Out-Degree	Number of outgoing ties (directed networks)	Proactivity; willingness to seek help or extend trust
Betweenness Centrality	Frequency of lying on shortest paths between others	Brokerage potential; control over information flow; structural power
Closeness Centrality	Average distance to all other nodes	Efficiency of information access; speed of reaching others
Eigenvector Centrality	Quality of connections (connected to well-connected others)	Access to informal power; influence through association

Structural Holes Theory (Ronald Burt)

Structural Positions in Networks (Burt, 1992)
Position	Characteristics	Advantages
Broker	High betweenness; spans structural holes	Novel information; control over flow; arbitrage opportunities
Within Dense Cluster	High degree within group; low betweenness	Trust; cohesion; redundant information
Isolate	Low degree; low betweenness	Independence; limited obligations

In your class network:

High betweenness = You’re a broker between study groups, functional backgrounds, or social clusters
Low betweenness + high degree = You’re embedded in a tight-knit group
Low on all metrics = You may be at the periphery (opportunity for intentional reconnection)

Understanding Centrality Distributions

The tables below show class-level distribution statistics for each centrality measure. These help you understand where you stand relative to your peers.

How to use these tables:

Find the measure (e.g., in-degree, betweenness)
Note the mean and median (typical values)
Note the min and max (range)
Compare your score (found in next section) to these benchmarks

Centrality Statistics for the Class

Degree Centrality

Degree centrality measures the number of ties a node has.

Undirected networks (Communication): Degree = total connections
Directed networks (Trust, Advice):
- In-degree = incoming ties (others choose you)
- Out-degree = outgoing ties (you choose others)

In-degree Centrality Distribution Across Networks (Normalized)
	Trust	Advice
Min	0.000	0.000
Q1.25%	0.019	0.032
Median	0.077	0.090
Mean	0.152	0.133
Q3.75%	0.174	0.161
Max	1.000	1.000

Out-degree Centrality Distribution Across Networks (Normalized)
	Trust	Advice
Min	0.039	0.032
Q1.25%	0.097	0.082
Median	0.145	0.116
Mean	0.152	0.133
Q3.75%	0.194	0.174
Max	0.381	0.419

Communication Network Degree Centrality Distribution (Undirected, Normalized)
	Communication
Min	0.116
Q1.25%	0.431
Median	0.590
Mean	0.566
Q3.75%	0.684
Max	1.000

Interpretation Guide:

Your Score vs. Class	Trust In-Degree	Advice In-Degree	Communication Degree
Top 25% (> Q3)	High trust from peers; moral authority	Recognized expert; go-to advisor	Socially central; well-integrated
Around Median	Moderate trust; typical standing	Some expertise recognized	Average connectivity
Bottom 25% (< Q1)	Less trusted (may be new or reserved)	Expertise not yet visible	Peripheral; opportunity to engage more

Out-Degree Interpretation:

High out-degree: Proactive; seeks input; willing to be vulnerable (trust) or admit knowledge gaps (advice)
Low out-degree: Self-reliant or isolated; may benefit from seeking more input

Betweenness Centrality

Betweenness centrality measures how often you lie on the shortest path between other pairs of people.

Translation: Are you a bridge or broker connecting otherwise disconnected people?

Betweenness Centrality Distribution Across Networks (Normalized)
	Trust	Advice	Communication
Min	0.000	0.000	0.000
Q1.25%	0.000	0.000	0.000
Median	0.001	0.002	0.001
Mean	0.007	0.006	0.007
Q3.75%	0.006	0.007	0.005
Max	0.105	0.131	0.208

Interpretation Guide:

Your Score vs. Class	Leadership Archetype	Implications
Top 10% (High)	Broker / Bridge	You connect disconnected groups (e.g., different study groups, backgrounds). You have structural power—information flows through you. Risk: You may become a bottleneck.
Around Median	Embedded Member	You’re part of the network but not a critical bridge. Balanced position.
Bottom 25% (Low)	Within Dense Cluster or Peripheral	Either you’re in a tight-knit group (many redundant paths) or you’re on the edge. Low brokerage potential.

Burt’s Structural Holes: High betweenness = You span structural holes (gaps between groups). This gives you:

✅ Access to diverse information
✅ Control over information flow
✅ Ability to translate between groups

Eigenvector Centrality

Eigenvector centrality measures how well-connected your connections are.

Translation: Are you connected to influential people?

Eigenvector Centrality Distribution Across Networks (Normalized)
	Trust	Advice	Communication
Min	0.000	0.000	0.133
Q1.25%	0.016	0.026	0.488
Median	0.091	0.112	0.659
Mean	0.172	0.167	0.625
Q3.75%	0.220	0.204	0.752
Max	1.000	1.000	1.000

Interpretation Guide:

Your Score vs. Class	Trust Network	Advice Network	Communication Network
Top 25% (High)	Trusted by trusted people (high status)	Sought by experts (elite knowledge circle)	Connected to influencers (access to informal power)
Around Median	Moderate status position	Mixed connections	Typical social embedding
Bottom 25% (Low)	Peripheral or connected to peripheral others	Outside expert circles	Limited access to influential networks

Why This Matters:

High eigenvector = Your connections give you reflected status and access to power
This measure captures “power by association”—even if your degree isn’t highest, being connected to high-degree people matters

Strategic Insight: You can have low degree but high eigenvector (few connections, but to key people) or high degree but low eigenvector (many connections to peripheral people). Quality vs. quantity.

Closeness Centrality

Closeness centrality measures how close you are to all other nodes (inverse of average path length).

Translation: How quickly can you reach everyone else in the network?

Closeness Centrality Distribution Across Networks (Normalized)
	Trust	Advice	Communication
Min	0.512	0.508	0.531
Q1.25%	0.542	0.531	0.637
Median	0.560	0.550	0.709
Mean	0.588	0.567	0.710
Q3.75%	0.594	0.583	0.760
Max	1.000	1.000	1.000

Interpretation Guide:

Your Score vs. Class	Implication
Top 25% (High)	Fast access to information and people. You’re structurally efficient—can mobilize the network quickly. Good for crisis situations or coordination.
Around Median	Typical distance to others.
Bottom 25% (Low)	Structurally distant from others. Information reaches you slowly; harder to mobilize support. May indicate peripheral position or being in a distant subgroup.

Leadership Insight:

High closeness is valuable for coordination roles (project manager, team lead)
Low closeness doesn’t mean you’re unimportant—brokers (high betweenness) often have lower closeness because they’re positioned between groups, not within a central group

Finding Your Position in the Networks

Now that you understand the class-level structure and centrality distributions, it’s time to find your individual scores.

Instructions:

Use the filter boxes at the top of each table to search for your SNA ID
Compare your scores to the class distributions (tables above)
Note where you’re high, medium, or low relative to peers
Look for patterns across networks (are you central in one but peripheral in another?)

Your Individual Network Measures

Advice Network Scores

What this network reveals: Who is seen as having expertise and competence? Who bridges knowledge domains?

Key Questions to Ask:

High advice in-degree? → You’re seen as an expert; people seek your input
High advice betweenness? → You bridge different knowledge groups (e.g., tech + finance backgrounds)
Low advice in-degree? → Expertise not yet visible to classmates (opportunity to share more)

Trust Network Scores

What this network reveals: Who is seen as having integrity, empathy, and psychological safety? Who are the moral anchors?

Key Questions to Ask:

High trust in-degree? → Others confide in you; you’re seen as trustworthy and empathetic
High trust betweenness? → You bridge trust across different social groups (rare and valuable)
Low trust in-degree? → May be new to the group, or haven’t had opportunities to demonstrate trustworthiness

Important: Trust is earned over time. Low trust scores early in a class are normal.

Communication Network Scores

What this network reveals: Who is socially integrated and has access to informal information?

Key Questions to Ask:

High communication degree? → You’re socially central and well-integrated
High communication eigenvector? → You’re connected to other well-connected people (access to informal power)
Low communication degree? → You may be working independently or missing out on informal information flows

Strategic Insight: Communication networks are the foundation for trust and advice networks. If you’re peripheral in communication, it’s hard to be central in trust or advice.

Visualizing Your Position: Interactive Network Graphs

The visualizations below allow you to see your position in the network structure.

How to use these graphs:

Use the dropdown menu to select your SNA ID
Click on your node to highlight your connections
Hover over nodes to see their centrality scores
Observe:
- Are you in the center or periphery?
- Who are your immediate neighbors?
- Do you bridge clusters (betweenness) or sit within a cluster (degree)?

Note: You can drag nodes to reorganize the layout and zoom in/out.

Advice Network Graph

Visualization

Question: Where are you in the class advice network?

Large node = High betweenness (you’re a broker)
Your connections = Who you seek advice from and who seeks advice from you

# add node network attributes from network graph stats data from generated above
V(advice_graph)$betweenness <- advice_stats$betweenness
V(advice_graph)$indegree <- advice_stats$indegree
V(advice_graph)$outdegree <- advice_stats$outdegree
V(advice_graph)$eigenvector <- advice_stats$eigenvector
V(advice_graph)$closeness <- advice_stats$closeness
V(advice_graph)$value <- advice_stats$between

advice_graph<-set_graph_attr(advice_graph, "layout", layout_with_fr)

data <- toVisNetworkData(advice_graph)
node_df <- as.data.frame(data$nodes)
node_df <- node_df %>%
  mutate(id = as.numeric(id)) %>%  
  arrange(id)
node_df$title = paste0("node id:",node_df$id,"</b><br>betweenness_centrality:", node_df$betweenness, "</b><br>eigenvector_centrality:",node_df$eigenvector,"</b><br>indegree_centrality:", node_df$indegree, "</b><br>outdegree_centrality:", node_df$outdegree, "</b><br>closeness_centrality:", node_df$closeness)

vn = visNetwork(node_df, edges = data$edges,height = "1500px", width = "150%") %>%
  visConfigure(enabled = FALSE) %>%
  visPhysics(enabled = FALSE, stabilization = FALSE) %>%
  visIgraphLayout(layout = "layout.fruchterman.reingold") %>%
  visLayout(randomSeed = 123)%>%
  visOptions(
             highlightNearest = list(enabled = TRUE, degree = 2, hover = TRUE),
             nodesIdSelection = list(enabled = TRUE,
                                    style = 'width: 200px; height: 26px;background: #f8f8f8; 
                                            color: darkblue;
                                            border:none;
                                            outline:none;'), 
        
             )
        
vn = visNodes(vn, id = data$nodes$id, 
                  borderWidthSelected = 15,
                  color = list(highlight ="red"))
vn <- visEdges(vn, id = data$edges$id,
               color = 'pink', 
               arrows = 'from')
vn

Interpretation Questions

Look at your position and ask:

Am I in the core or periphery?
- Core = Many connections; visually central
- Periphery = Few connections; visually on the edges
Do I bridge different clusters?
- If you see separate groups and you connect them → High brokerage value
Who are my advice connections?
- Outgoing arrows = You seek advice from them
- Incoming arrows = They seek advice from you
What does this reveal about my expertise visibility?
- Many incoming arrows = Others see you as expert
- Many outgoing arrows = You actively seek diverse input
- Few of either = Opportunity to engage more

Trust Network Graph

Visualization

Question: Where are you in the trust network?

Trust is harder to build than communication
Trust networks are typically sparser than advice networks
High trust centrality = Moral authority and psychological safety

# add node network attributes from network graph stats data fram generated above
V(trust_graph)$betweenness <- trust_stats$betweenness
V(trust_graph)$indegree <- trust_stats$indegree
V(trust_graph)$outdegree <- trust_stats$outdegree
V(trust_graph)$eigenvector <- trust_stats$eigenvector
V(trust_graph)$closeness <- trust_stats$closeness
V(trust_graph)$value <- trust_stats$betweenness

trust_graph<-set_graph_attr(trust_graph, "layout", layout_with_fr)

data <- toVisNetworkData(trust_graph)
node_df <- as.data.frame(data$nodes)
node_df$id <- as.numeric(node_df$id)
node_df <- node_df[order(node_df$id), ]
node_df$id <- as.character(node_df$id)

node_df$title = paste0("node id:",node_df$id,"</b><br>betweenness_centrality:", node_df$betweenness, "</b><br>eigenvector_centrality:",node_df$eigenvector,"</b><br>indegree_centrality:", node_df$indegree, "</b><br>outdegree_centrality:", node_df$outdegree, "</b><br>closeness_centrality:", node_df$closeness)

vn = visNetwork(node_df, edges = data$edges,height = "1500px", width = "200%") %>%
  visConfigure(enabled = FALSE) %>%
  visPhysics(enabled = FALSE, stabilization = FALSE) %>%
  visIgraphLayout(layout = "layout.fruchterman.reingold") %>%
  visLayout(randomSeed = 100) %>%
  visOptions(highlightNearest =list(enabled = TRUE, hover = TRUE,degree = 3),
              nodesIdSelection = list(enabled = TRUE,
                                 style = 'width: 200px; height: 26px;
                                 background: #f8f8f8;
                                 color: darkblue;
                                 border:none;
                                 outline:none;'))
vn <- visNodes(vn, id = data$nodes$id, 
                  borderWidthSelected = 15,
                  color = list(highlight ="red"))
vn <- visEdges(vn, id = data$edges$id,
               color = 'pink', 
               arrows = 'from')
vn

Interpretation Questions

Look at your position and ask:

How many people trust me? (Incoming arrows)
- High trust in-degree = You’re seen as having integrity and empathy
- Low trust in-degree = Opportunity to build deeper relationships
How many people do I trust? (Outgoing arrows)
- High trust out-degree = You extend trust broadly (vulnerability)
- Low trust out-degree = You may be guarded or self-reliant
Am I a trust broker? (High betweenness)
- Do you bridge trust across different social groups?
- This is rare and valuable—trust usually stays within tight clusters
What does this reveal about my reputation?
- Trust centrality = Trustworthiness & Credibility
- Foundation for difficult conversations and authentic leadership

Communication Network Graph

Visualization

Question: Where are you in the communication network?

This is typically the densest network (operational necessity)
High degree = You’re socially embedded and have access to informal information
High eigenvector = You’re connected to influencers

# add node network attributes from network graph stats data fram generated above
V(comm_graph_undirected)$betweenness <- comm_stats2$betweenness
V(comm_graph_undirected)$degree <- comm_stats2$degree
V(comm_graph_undirected)$eigenvector <- comm_stats2$eigenvector
V(comm_graph_undirected)$closeness <- comm_stats2$closeness
V(comm_graph_undirected)$value <- comm_stats2$betweenness

comm_graph_undirected<-set_graph_attr(comm_graph_undirected, "layout", layout_with_fr)

data <- toVisNetworkData(comm_graph_undirected)
node_df <- as.data.frame(data$nodes)
node_df$title = paste0("node id:",node_df$id,"</b><br>betweenness_centrality:", node_df$betweenness, "</b><br>eigenvector_centrality:",node_df$eigenvector,"</b><br>degree_centrality:",node_df$degree, "</b><br>closeness_centrality:", node_df$closeness)

vn = visNetwork(node_df, edges = data$edges,height = "1500px", width = "200%") %>%
  visConfigure(enabled = FALSE) %>%
  visPhysics(enabled = FALSE, stabilization = FALSE) %>%
  visIgraphLayout(layout = "layout.fruchterman.reingold") %>%
  visLayout(randomSeed = 123) %>%
  visOptions(highlightNearest =list(enabled = TRUE, hover = TRUE,degree = 3),
              nodesIdSelection = list(enabled = TRUE,
                                 style = 'width: 200px; height: 26px;
                                 background: #f8f8f8;
                                 color: darkblue;
                                 border:none;
                                 outline:none;'))
vn = visNodes(vn, id = data$nodes$id, 
                  borderWidthSelected = 6,
                  color = list(highlight ="red"))
vn <- visEdges(vn, id = data$edges$id,
               color = 'pink')

vn

Interpretation Questions

Look at your position and ask:

How well-connected am I? (Node size/degree)
- Large node = Many communication partners
- Small node = Fewer connections (may indicate working independently)
Am I connected to others who are well-connected people? (Eigenvector)
- Are your connections themselves well-connected?
- This gives you access to informal power
Do I bridge communication clusters? (Betweenness)
- Do you connect different study groups or social circles?
- Communication brokers often become informal coordinators
What does this reveal about my social integration?
- Communication is the foundation for influence
- If you’re peripheral here, you’re likely peripheral in trust/advice too

What to Submit: Class Network Position Analysis

Now that you’ve explored your position across all three networks, it’s time to synthesize your findings into a written analysis.

Deliverable Components

1 Centrality Analysis Across Networks

Create a summary table of your centrality scores:

Network	In-Degree	Out-Degree	Betweenness	Closeness	Eigenvector
Trust	[Your score]	[Your score]	[Your score]	[Your score]	[Your score]
Advice	[Your score]	[Your score]	[Your score]	[Your score]	[Your score]
Communication	[Your score]	—	[Your score]	[Your score]	[Your score]

Narrative interpretation:

Where are you central? (e.g., “I have high advice betweenness but low trust in-degree”)
Where are you peripheral?
What patterns emerge across networks?
- Are you central in all three? (Rare—suggests strong all-around leadership presence)
- Central in one but not others? (Specialized role: expert vs. trusted friend vs. social connector)
- Peripheral in all? (Opportunity for intentional reconnection)

Compare to class benchmarks:

Are you above median (top half) or below median (bottom half) in each measure?
Are you in the top quartile (top 25%) for any measures?

2 Strategic Gaps & Opportunities

Leadership Archetype Diagnosis - Based on your centrality patterns, understand your leadership style.

Identify gaps and opportunities:

Are there disconnected clusters you could bridge?
Are you peripheral in networks where you want influence?
Are you missing from certain conversations (advice, trust)?

Brokerage Opportunities:

If you have high betweenness, what groups are you bridging?
Can you leverage this position to translate between domains?

3 Network Growth & Engagement Plan

Develop a tactical plan for the next 6 months:

If you’re peripheral:

Goal: Increase visibility and engagement
Tactics:
- Actively contribute in class discussions
- Initiate study groups
- Offer help to classmates
- Attend social events

If you’re a broker:

Goal: Deepen relationships while maintaining bridges
Tactics:
- Invest in strong ties (not just weak ties)
- Be mindful of bottleneck risk
- Use brokerage position to add value (not just extract)

If you’re central:

Goal: Maintain position while developing others
Tactics:
- Mentor peripheral classmates
- Connect people strategically
- Be accessible without burning out

Common Pitfalls & How to Avoid Them

Pitfall #1: Confusing Centrality with Leadership Quality

Problem: Assuming high centrality = good leader, low centrality = bad leader

Reality:

Central people have visibility and influence, but may lack depth
Peripheral people may have deep expertise or strong niche relationships (they may also be brokers to other groups)
Brokers have structural power but may not be trusted

Solution: Focus on fit between your style and your goals, not absolute centrality scores.

Pitfall #2: Ignoring Out-Degree

Problem: Only focusing on in-degree (who chooses you) and ignoring out-degree (who you choose)

Reality:

High out-degree shows proactivity and vulnerability (willingness to seek help/trust)
Low out-degree may indicate self-reliance or isolation
Imbalanced in/out ratios reveal asymmetries (e.g., many seek your advice but you don’t seek theirs)

Solution: Analyze both directions of ties.

Pitfall #3: Static Analysis

Problem: Treating your current position as fixed

Reality:

Networks are dynamic—your position changes over time
Early in a class/team, everyone is peripheral
Low centrality now ≠ low centrality forever

Solution: Frame this as a baseline diagnostic and commit to intentional network building.

Pitfall #4: Over-Interpretation of Betweenness

Problem: Assuming betweenness = always good

Reality:

High betweenness can be a burden (bottleneck, time drain)
In dense networks, betweenness is less valuable (many redundant paths)
In trust networks, high betweenness may indicate bridging fragile coalitions (stress)

Solution: Consider context—betweenness is valuable when networks are sparse and information is siloed.

Pitfall #5: Forgetting Reciprocity

Problem: Focusing only on what the network does for you

Reality:

Networks are reciprocal—you must give to get
High in-degree without out-degree = You’re extracting value without contributing
Sustainable leadership requires mutual support

Solution: Look for asymmetries (people you rely on but don’t help; people who help you but you don’t reciprocate). Fix them.

Future Use: Repeatable Diagnostic

This exact analysis can be deployed in:

Teams

Map trust and coordination within project teams
Identify communication bottlenecks
Diagnose why collaboration isn’t working

Organizations

Understand informal power structures (eigenvector centrality)
Find information silos (low betweenness)
Spot coordination failures (fragmented networks)

Consulting Projects

Diagnose client organizations’ informal structures
Identify key influencers (not just formal leaders)
Design interventions to bridge gaps

Personal Growth

Run this analysis annually to track your network evolution
Identify when you’re becoming too central (burnout risk) or too peripheral (invisibility risk)
Calibrate your networking strategy

Key Takeaways

By the end of this analysis, you should be able to answer:

Diagnostic: How am I perceived by my peers across trust, advice, and communication?
Comparative: Where do I stand relative to classmates on key centrality measures?
Analytical: What does my structural position reveal about my network style?
Strategic: What gaps or opportunities exist in my current network position?
Actionable: What specific steps will I take to strengthen my network positioning?

Remember:

“It’s not about having the most connections—it’s about having the right positioning for your goals.”

Your network position is not destiny—it’s data for intentional design.

Last updated: December 2025 Course: Leading by Design - People Analytics Diagnostic Institution: Carnegie Mellon University, Tepper School of Business

Class Network Position Analysis - Tutorial & Assignment

Tutorial Overview

Module Context

Quick Facts

Learning Objectives

Theoretical Foundation

Why Class Networks Matter for Leadership

The Hidden Architecture of Influence

Three Critical Networks

1. Trust Network (Directed)

2. Advice Network (Directed)

3. Communication Network (Undirected)

Key Concepts to Apply

Centrality Measures: What They Mean for Leadership

Structural Holes Theory (Ronald Burt)

Understanding Centrality Distributions

Centrality Statistics for the Class

Degree Centrality

Betweenness Centrality

Eigenvector Centrality

Closeness Centrality

Finding Your Position in the Networks

Your Individual Network Measures

Advice Network Scores

Trust Network Scores

Communication Network Scores

Visualizing Your Position: Interactive Network Graphs

Advice Network Graph

Visualization

Interpretation Questions

Trust Network Graph

Visualization

Interpretation Questions

Communication Network Graph

Visualization

Interpretation Questions

What to Submit: Class Network Position Analysis

Deliverable Components

1 Centrality Analysis Across Networks

2 Strategic Gaps & Opportunities

3 Network Growth & Engagement Plan

Common Pitfalls & How to Avoid Them

Pitfall #1: Confusing Centrality with Leadership Quality

Pitfall #2: Ignoring Out-Degree

Pitfall #3: Static Analysis

Pitfall #4: Over-Interpretation of Betweenness

Pitfall #5: Forgetting Reciprocity

Future Use: Repeatable Diagnostic

Teams

Organizations

Consulting Projects

Personal Growth

Key Takeaways