Core Concepts
Nodes and Edges
| Term | Definition | Examples |
|---|---|---|
| Node | An actor in the network | Organization, individual, agency |
| Edge | A relationship between nodes | Partnership, funding, communication |
| Directed Edge | One-way relationship | Funding flows from A to B |
| Undirected Edge | Two-way relationship | A and B collaborate |
| Weighted Edge | Relationship with strength | Frequency of contact |
Network Visualization
A ─────── B
│ │
│ │
C ─────── D ─────── E
│
│
F
In this simple network:
- D has the most connections (highest degree centrality)
- D bridges two clusters (high betweenness centrality)
- A and F are peripheral nodes
Centrality Metrics
Degree Centrality
Definition: Total number of direct connections to a node.
| Application | Interpretation |
|---|---|
| High degree | Most active, many direct partnerships |
| Advocacy use | Identifies well-connected hubs |
| Risk | High-degree nodes face burnout and resource depletion |
Formula: Degree = Number of edges connected to node
Betweenness Centrality
Definition: Frequency with which a node lies on the shortest path between other nodes.
| Application | Interpretation |
|---|---|
| High betweenness | Acts as bridge between otherwise disconnected groups |
| Advocacy use | Identifies critical brokers connecting silos |
| Risk | Removal fragments the network |
Strategic importance: Organizations connecting legal clinics with grassroots mutual aid are high-betweenness nodes. They ensure policy shifts translate to community defense.
Closeness Centrality
Definition: Average length of shortest paths from a node to all other nodes.
| Application | Interpretation |
|---|---|
| High closeness | Can reach everyone quickly |
| Advocacy use | Identifies rapid communication hubs |
| Risk | May be overwhelmed during crisis |
Strategic importance: Nodes with high closeness centrality are optimal for disseminating rapid response alerts.
Eigenvector Centrality
Definition: Influence based on connections to other influential nodes.
| Application | Interpretation |
|---|---|
| High eigenvector | Connected to other highly connected nodes |
| Advocacy use | Reveals hidden power structures |
| Risk | Invisible influence may bypass formal governance |
Strategic importance: Identifies actors with systemic power through elite connections, even if they don't have many direct partnerships.
Network Topology
Density
Definition: Proportion of actual connections relative to all possible connections.
| Density Level | Characteristics | Implications |
|---|---|---|
| High (0.7-1.0) | Everyone connected | Strong solidarity, shared norms, risk of echo chamber |
| Medium (0.3-0.7) | Clustered groups | Balance of cohesion and diversity |
| Low (0.0-0.3) | Sparse connections | Fragmented, weak coordination |
Strong vs Weak Ties
| Tie Type | Characteristics | Function |
|---|---|---|
| Strong Ties | Frequent, high-trust interactions | Execute complex, high-risk endeavors (direct action, mutual aid, community defense) |
| Weak Ties | Infrequent, superficial connections | Informational bridges to novel ideas, diverse donors, external alliances |
Strategic balance: Advocacy networks need both:
- Strong ties for execution during raids/crises
- Weak ties for innovation and coalition expansion
Structural Holes
Definition: Gaps between dense clusters where no connections exist.
Cluster A Cluster B
●──● ●──●
│ │ │ │
●──● ●──●
\ /
\ /
●───────● ← Bridge spanning structural hole
Brokerage opportunity: Organizations that span structural holes achieve leverage by synthesizing grassroots realities with elite policy formulation.
Network Types
Actor Networks
| Focus | Relationships Mapped |
|---|---|
| Organizations | Formal partnerships, coalition membership |
| Individuals | Personal connections, mentorship |
| Institutions | Organizational affiliations |
Policy Networks
| Focus | Relationships Mapped |
|---|---|
| Legislative | Bill co-sponsorship, committee membership |
| Regulatory | Agency-advocate interactions |
| Judicial | Legal defense coordination |
Information Networks
| Focus | Relationships Mapped |
|---|---|
| Knowledge flow | Who shares information with whom |
| Trusted sources | Where community members get information |
| Translation | Who adapts technical content for community |
Resource Networks
| Focus | Relationships Mapped |
|---|---|
| Funding | Grant flows, shared fundraising |
| Staff | Shared personnel, cross-training |
| Infrastructure | Shared technology, office space |
Enforcement Networks
| Focus | Relationships Mapped |
|---|---|
| Federal | ICE-CBP-DOJ inter-agency connections |
| Local | 287(g) agreements, task forces |
| Corporate | Detention contractors, data brokers |
Data Collection Methods
Socio-Centric (Whole Network)
Approach: Map every connection within a defined boundary.
| Method | Implementation |
|---|---|
| Roster Survey | Present list of all actors; respondents indicate connections to each |
| Archival Analysis | Board memberships, co-signatures, event co-attendance |
| Digital Trace | Email metadata, social media interactions |
Best for: Formal coalitions with defined membership.
Limitation: Fails to capture fluid, unbounded community networks.
Ego-Centric (Personal Network)
Approach: Build outward from focal individuals.
| Method | Implementation |
|---|---|
| Free Recall | Ask individuals to name their connections |
| Snowball Sampling | Interview contacts of contacts |
| Name Generator | Structured questions about relationship types |
Best for: Mapping organic community networks, identifying trusted information sources.
Digital Trace Data
| Source | Data Type |
|---|---|
| Social media | Follower/following, mentions, retweets |
| Public records | Board memberships, lobbying disclosures |
| Contracting databases | Federal vendor relationships |
| Email (with consent) | Communication patterns |
Advantage: Non-intrusive, doesn't burden communities with surveys.
Survey Instruments
Roster Method Template
For bounded coalitions (e.g., statewide coalition of 40 organizations):
For each organization listed below, please indicate:
1. Do you exchange policy information? [Yes/No]
2. Do you collaborate on joint funding? [Yes/No]
3. Do you meet at least monthly? [Yes/No]
4. Do you share staff or volunteers? [Yes/No]
5. How would you rate the strength of this relationship? [1-5]
[List of all coalition member organizations]
Free Recall Template
For community network mapping:
Please name up to 10 organizations or individuals:
1. Who you contact when you need immigration legal information:
________________________________
2. Who you would alert if you witnessed an ICE raid:
________________________________
3. Who helps coordinate community response in your area:
________________________________
4. Who you trust most for accurate policy updates:
________________________________
Relationship Type Classification
| Type Code | Definition | Edge Weight |
|---|---|---|
| INF | Information exchange | 1 |
| RES | Resource sharing | 2 |
| COL | Active collaboration | 3 |
| JNT | Joint program/campaign | 4 |
| FRM | Formal partnership (MOU) | 5 |
Data Quality Considerations
Common Issues
| Issue | Mitigation |
|---|---|
| Missing data | Use multiple collection methods |
| Recall bias | Triangulate with archival data |
| Social desirability | Ensure anonymity |
| Boundary specification | Define network scope clearly |
Validation Approaches
| Approach | Method |
|---|---|
| Cross-verification | Compare reported ties bidirectionally |
| Archival confirmation | Verify partnerships against public records |
| Multiple informants | Ask multiple people within each organization |
Longitudinal Analysis
Why Track Networks Over Time
| Change Type | What It Reveals |
|---|---|
| New connections | Growing coalition, emerging leaders |
| Lost connections | Organizational stress, funding loss |
| Density changes | Increasing or decreasing cohesion |
| Centrality shifts | Power redistribution |
Update Frequency
| Trigger | Rationale |
|---|---|
| Annual | Routine health check |
| Post-election | Political landscape shift |
| Post-major policy | Enforcement or advocacy changes |
| Post-crisis | Network response effectiveness |
Network Health Indicators
Coalition Strength
| Indicator | Healthy Range | Warning Sign |
|---|---|---|
| Density | 0.3-0.6 | Below 0.2 or above 0.8 |
| Average degree | 4-8 | Below 2 |
| Clustering coefficient | 0.3-0.7 | Below 0.2 |
| Largest component | 90%+ of nodes | Below 70% |
Vulnerability Assessment
| Indicator | What to Check |
|---|---|
| Single points of failure | Nodes whose removal fragments network |
| Bottleneck nodes | Over-burdened high-betweenness actors |
| Isolated periphery | Organizations with only 1-2 connections |
| Funding concentration | Over-reliance on single funder |
Next Steps
- Explore tools for data collection and analysis
- Map enforcement ecosystems to understand opposition
- Map advocacy networks to strengthen coalitions
- Review ethical considerations before collecting data