network-social-graph

name	network-social-graph
description	Use when mapping a user's interaction network from reply chains or parent-child relationships, identifying frequent interlocutors, measuring reciprocity patterns, detecting audience-dependent voice shifts, or extracting ego networks from conversation data

Network and Social Graph Analysis

Overview

Build and analyze a directed interaction graph from reply chains and parent-child relationships in conversation data. The core principle: edges are replies, nodes are participants, and the asymmetry between who initiates and who responds reveals social structure -- before inferring relationships, measure the graph.

When to Use

• Conversation data with reply chains or parent-child references (forum threads, comment trees, message archives)
• Need to identify a user's frequent interlocutors and interaction patterns
• Measuring whether interactions are reciprocal or one-directional
• Detecting whether a user shifts voice, tone, or vocabulary depending on who they are replying to
• Extracting an ego network for a single user from a larger interaction dataset
• Feeding interaction patterns into downstream persona or archetype analysis

When NOT to use:

• Content has no reply structure (standalone documents, articles, reviews without threading)
• Need content-level analysis only (sentiment, topic modeling) -- use NMF or VADER skills instead
• Fewer than 10 interactions total -- insufficient for any graph metric (see Insufficient Data section)
• Attempting to infer real-world personal relationships from interaction frequency alone

Workflow

Copy this checklist and track progress:

Network and Social Graph Analysis Progress:
- [ ] Step 1: Extract reply pairs from conversation data
- [ ] Step 2: Build directed interaction graph
- [ ] Step 3: Filter for meaningful interactions
- [ ] Step 4: Compute graph-level metrics
- [ ] Step 5: Extract ego network for target user
- [ ] Step 6: Identify frequent interlocutors
- [ ] Step 7: Measure reciprocity patterns
- [ ] Step 8: Detect audience-dependent voice shifts
- [ ] Step 9: Run community detection (if graph is large enough)
- [ ] Step 10: Write findings to docs/analysis/10-network-social-graph.md

Step 1: Extract Reply Pairs from Conversation Data

Parse the conversation dataset to extract directed edges: (replier, parent_author). Each reply creates one directed edge from the replier to the author of the parent content.

import pandas as pd

def extract_reply_pairs(comments_df, id_col='id', parent_col='parent_id',
                        author_col='author', body_col='body'):
    """
    Extract directed reply pairs from a comments/messages dataframe.
    Returns a dataframe of (source, target, metadata) edges.

    Adapt column names to your data format:
    - Forum data: thread_id, reply_to_id, username, content
    - Chat data: message_id, reply_to, sender, text
    - Email data: message_id, in_reply_to, from, body
    """
    # Build lookup: content ID -> author
    id_to_author = comments_df.set_index(id_col)[author_col].to_dict()

    edges = []
    for _, row in comments_df.iterrows():
        source = row[author_col]
        parent_id = row[parent_col]

        if pd.isna(parent_id) or pd.isna(source):
            continue

        # Resolve parent author from lookup
        target = id_to_author.get(parent_id)
        if target is None or target == source:  # Skip self-replies
            continue

        edges.append({
            'source': source,          # Who replied
            'target': target,          # Who they replied to
            'body': row.get(body_col, ''),
            'timestamp': row.get('date', row.get('timestamp', None)),
            'parent_id': parent_id,
        })

    return pd.DataFrame(edges)

Data format adaptation: The column names above are generic. Map them to your actual data:

• Reddit exports: id, parent_id (prefixed with t1_ or t3_), author may need external resolution
• Forum exports: thread/post IDs, reply_to fields
• Chat exports: message IDs, in_reply_to or thread references

Common issue -- unresolvable parent authors: If the parent content is not in the dataset (deleted, from another export, or external), the edge cannot be constructed. Track these as "orphan references" and report the count.

Step 2: Build Directed Interaction Graph

import networkx as nx
from collections import Counter

def build_interaction_graph(edges_df):
    """
    Build a weighted directed graph from reply pairs.
    Edge weight = number of interactions from source to target.
    """
    G = nx.DiGraph()

    # Aggregate edges: count interactions per (source, target) pair
    edge_counts = Counter(
        zip(edges_df['source'], edges_df['target'])
    )

    for (source, target), weight in edge_counts.items():
        G.add_edge(source, target, weight=weight)

    return G

G = build_interaction_graph(edges_df)
print(f"Nodes: {G.number_of_nodes()}, Edges: {G.number_of_edges()}")
print(f"Total interactions: {sum(d['weight'] for _, _, d in G.edges(data=True))}")

Step 3: Filter for Meaningful Interactions

Not all interactions carry equal analytical weight. Filter edges to reduce noise.

def filter_graph(G, edges_df, min_weight=2, min_body_length=10):
    """
    Remove edges that represent trivial interactions.
    - min_weight: require at least N interactions between a pair
    - min_body_length: exclude one-word/trivial replies from edge construction
    """
    # First: rebuild edges excluding trivial replies
    substantive = edges_df[edges_df['body'].str.len() >= min_body_length]
    G_filtered = build_interaction_graph(substantive)

    # Second: remove low-weight edges
    weak_edges = [
        (u, v) for u, v, d in G_filtered.edges(data=True)
        if d['weight'] < min_weight
    ]
    G_filtered.remove_edges_from(weak_edges)

    # Remove isolated nodes (no remaining edges)
    isolates = list(nx.isolates(G_filtered))
    G_filtered.remove_nodes_from(isolates)

    print(f"Filtered: {G.number_of_nodes()} -> {G_filtered.number_of_nodes()} nodes, "
          f"{G.number_of_edges()} -> {G_filtered.number_of_edges()} edges")
    return G_filtered

Filtering thresholds depend on corpus size:

Corpus Size (interactions)	min_weight	min_body_length	Rationale
< 100	1	5	Preserve all signal in small datasets
100-500	2	10	Filter trivial but keep moderate
500-2000	2-3	15	Standard filtering
2000+	3-5	20	Aggressive noise reduction

Step 4: Compute Graph-Level Metrics

def compute_graph_metrics(G):
    """Compute standard SNA metrics for the interaction graph."""
    metrics = {}

    # Basic structure
    metrics['nodes'] = G.number_of_nodes()
    metrics['edges'] = G.number_of_edges()
    metrics['density'] = nx.density(G)
    metrics['total_interactions'] = sum(d['weight'] for _, _, d in G.edges(data=True))

    # Reciprocity: proportion of edges that are bidirectional
    metrics['reciprocity'] = nx.reciprocity(G)

    # Degree centrality (in and out)
    in_degree = nx.in_degree_centrality(G)
    out_degree = nx.out_degree_centrality(G)
    metrics['in_degree_centrality'] = in_degree
    metrics['out_degree_centrality'] = out_degree

    # Betweenness centrality: who bridges different clusters
    if G.number_of_nodes() <= 1000:  # Expensive for large graphs
        metrics['betweenness'] = nx.betweenness_centrality(G, weight='weight')

    # Weakly connected components
    components = list(nx.weakly_connected_components(G))
    metrics['num_components'] = len(components)
    metrics['largest_component_size'] = max(len(c) for c in components) if components else 0

    return metrics

Key metrics and interpretation:

Metric	Formula / Function	What It Reveals
Density	2*edges / (nodes*(nodes-1))	How interconnected the network is (0-1)
Reciprocity	|{(u,v) in G | (v,u) in G}| / |{(u,v) in G}|	Proportion of mutual interactions
In-degree centrality	Normalized count of incoming edges	Who receives the most replies (attention target)
Out-degree centrality	Normalized count of outgoing edges	Who initiates the most replies (active participant)
Betweenness centrality	Fraction of shortest paths through node	Who bridges different groups
Weakly connected components	Subgraphs reachable ignoring direction	How fragmented the network is

Step 5: Extract Ego Network for Target User

The ego network is the subgraph centered on the target user, including all their direct interlocutors (alters) and the connections between those alters.

def extract_ego_network(G, ego_user):
    """
    Extract the ego network: ego + alters + edges between them.
    Returns both the ego subgraph and summary statistics.
    """
    if ego_user not in G:
        return None, {"error": f"User '{ego_user}' not found in graph"}

    # Ego network: ego + all neighbors + edges among them
    ego_graph = nx.ego_graph(G, ego_user, undirected=True)

    alters = set(ego_graph.nodes()) - {ego_user}

    # Classify alter relationships
    alter_stats = []
    for alter in alters:
        outgoing = G[ego_user].get(alter, {}).get('weight', 0)  # ego -> alter
        incoming = G[alter].get(ego_user, {}).get('weight', 0) if G.has_edge(alter, ego_user) else 0

        alter_stats.append({
            'alter': alter,
            'ego_to_alter': outgoing,
            'alter_to_ego': incoming,
            'total': outgoing + incoming,
            'reciprocity_ratio': min(outgoing, incoming) / max(outgoing, incoming)
                                 if max(outgoing, incoming) > 0 else 0,
            'direction': 'mutual' if outgoing > 0 and incoming > 
                          (  outgoing >   ),
        })

    alter_df = pd.DataFrame(alter_stats).sort_values(, ascending=)

    summary = {
        : ego_user,
        : (alters),
        : alter_df[].(),
        : (alter_df[alter_df[] == ]),
        : (alter_df[alter_df[] == ]),
        : (alter_df[alter_df[] == ]),
        : (alter_df[alter_df[] == ]) / (alters)  alters  ,
    }

     alter_df, summary

Step 6: Identify Frequent Interlocutors

Rank alters by interaction volume and classify the interaction pattern.

def classify_interlocutors(alter_df, top_n=10):
    """
    Classify the ego's top interlocutors by interaction pattern.
    """
    top = alter_df.head(top_n).copy()

    def classify(row):
        ratio = row['reciprocity_ratio']
        if row['direction'] != 'mutual':
            return 'one-directional'
        elif ratio >= 0.6:
            return 'balanced-mutual'
        elif row['ego_to_alter'] > row['alter_to_ego']:
            return 'ego-dominant'
        else:
            return 'alter-dominant'

    top['pattern'] = top.apply(classify, axis=1)
    return top

Interlocutor pattern interpretation:

Pattern	Description	Implication
balanced-mutual	Roughly equal exchange	Peer relationship, conversational
ego-dominant	Ego replies more than alter replies back	Ego seeks engagement, alter less invested
alter-dominant	Alter replies more to ego	Ego attracts attention, alter seeks engagement
one-directional	Only one direction of interaction	Broadcast or observation pattern

Step 7: Measure Reciprocity Patterns

Reciprocity operates at three levels: global network, ego-level, and per-alter.

def measure_reciprocity(G, ego_user, alter_df):
    """
    Compute reciprocity at three levels.
    """
    # 1. Global network reciprocity
    global_recip = nx.reciprocity(G)

    # 2. Ego-level reciprocity (what fraction of ego's alters are mutual)
    mutual = alter_df[alter_df['direction'] == 'mutual']
    ego_recip = len(mutual) / len(alter_df) if len(alter_df) > 0 else 0

    # 3. Weighted ego reciprocity (accounts for interaction volume)
    total_out = alter_df['ego_to_alter'].sum()
    total_in = alter_df['alter_to_ego'].sum()
    weighted_recip = min(total_out, total_in) / max(total_out, total_in) \
                     if max(total_out, total_in) > 0 else 0

    # 4. Initiation ratio: how often does ego start threads vs respond
    ego_out_degree = G.out_degree(ego_user, weight='weight') if ego_user in G else 0
    ego_in_degree = G.in_degree(ego_user, weight='weight') if ego_user in G else 0
    initiation_ratio = ego_out_degree / (ego_out_degree + ego_in_degree) \
                       if (ego_out_degree + ego_in_degree) > 0 else 0.5

    return {
        : global_recip,
        : ego_recip,
        : weighted_recip,
        : initiation_ratio,
        : interpret_initiation(initiation_ratio),
    }

 ():
     ratio > :
         
     ratio > :
         
     ratio > :
         
     ratio > :
         
    :
         

Step 8: Detect Audience-Dependent Voice Shifts

Compare the ego's linguistic features when replying to different interlocutors or interlocutor types. This step draws on Communication Accommodation Theory (CAT): speakers adjust their language to converge toward or diverge from their conversation partners.

from collections import defaultdict
import numpy as np

def compute_voice_features(text):
    """
    Extract lightweight linguistic features for voice comparison.
    Extend with spaCy/NLTK for deeper analysis.
    """
    if not text or not isinstance(text, str):
        return None
    words = text.split()
    sentences = text.split('.')
    return {
        'avg_word_length': np.mean([len(w) for w in words]) if words else 0,
        'avg_sentence_length': np.mean([len(s.split()) for s in sentences if s.strip()]) if sentences else 0,
        'vocabulary_richness': len(set(words)) / len(words) if words else 0,
        'question_ratio': text.count('?') / max(len(sentences), 1),
        'exclamation_ratio': text.count('!') / max(len(sentences), 1),
        : (text),
        : (  w  words  (w) > ) / ((words), ),
    }

 ():
    
    ego_replies = edges_df[edges_df[] == ego_user]
    top_alters = alter_df.head(top_n)[].tolist()

    alter_features = {}
     alter  top_alters:
        replies_to_alter = ego_replies[ego_replies[] == alter]
        features = [compute_voice_features(r)  r  replies_to_alter[]  r]
        features = [f  f  features  f   ]
         features:
            alter_features[alter] = {
                k: np.mean([f[k]  f  features])
                 k  features[].keys()
            }

     (alter_features) < :
         , 

    
    feature_names = (((alter_features.values())).keys())
    variance_report = {}
     feat  feature_names:
        values = [alter_features[a][feat]  a  alter_features]
        variance_report[feat] = {
            : np.mean(values),
            : np.std(values),
            : np.std(values) / np.mean(values)  np.mean(values) >   ,
            : (values) - (values),
        }

     alter_features, variance_report

Interpreting voice shift results:

Coefficient of Variation (CV)	Interpretation
CV < 0.10	Stable voice -- minimal audience adaptation
CV 0.10 - 0.25	Moderate shifts -- some audience sensitivity
CV > 0.25	Strong shifts -- voice varies substantially by interlocutor

What constitutes a meaningful shift: A single feature with high CV is not sufficient. Look for consistent patterns across multiple features -- if word length, sentence length, AND formality all shift for the same alter, that is a meaningful accommodation pattern. One noisy feature alone is not.

Step 9: Community Detection (If Graph Is Large Enough)

Community detection requires a minimum network size to produce meaningful results.

def detect_communities(G, min_nodes=15):
    """
    Detect communities using the Louvain method.
    Requires converting DiGraph to undirected for most algorithms.
    """
    if G.number_of_nodes() < min_nodes:
        return None, f"Graph too small ({G.number_of_nodes()} nodes) for community detection (need {min_nodes}+)"

    # Convert to undirected, summing edge weights
    G_undirected = G.to_undirected()

    # Louvain community detection
    try:
        from community import community_louvain
        partition = community_louvain.best_partition(G_undirected, weight='weight')
    except ImportError:
        # Fallback: greedy modularity
        communities = nx.community.greedy_modularity_communities(G_undirected, weight='weight')
        partition = {}
        for i, comm in enumerate(communities):
            for node in comm:
                partition[node] = i

    num_communities = len(set(partition.values()))
    modularity = nx.community.modularity(
        G_undirected,
        [{n for n, c in partition.items() if c == i} for i in set(partition.values())],
        weight='weight'
    )

    return partition, {
        'num_communities': num_communities,
        'modularity': modularity,
        'interpretation': 'Strong structure'  modularity > 
                             modularity > 
                           ,
    }

Step 10: Write Report

Write all findings to docs/analysis/10-network-social-graph.md. See Report Output Template below.

Report Output Template

The final report MUST be written to docs/analysis/10-network-social-graph.md with this structure:

# Network and Social Graph Analysis

## Methodology
- Data source: [describe conversation dataset and reply structure]
- Reply pairs extracted: [N edges from M records, O orphan references]
- Filtering: min_weight=[N], min_body_length=[N]
- Graph construction: directed, weighted by interaction count
- Date of analysis: [date]

## Graph Summary

| Metric | Value | Interpretation |
|--------|-------|----------------|
| Nodes (unique participants) | [N] | |
| Edges (directed interaction pairs) | [N] | |
| Total interactions (weighted) | [N] | |
| Density | [value] | [sparse/moderate/dense] |
| Global reciprocity | [value] | [low/moderate/high mutual interaction] |
| Connected components | [N] | [fragmented/cohesive] |
| Largest component | [N nodes] | [% of total] |

## Ego Network: [Target User]

### Ego Summary
| Metric | Value |
|--------|-------|
| Direct interlocutors (alters) | [N] |
| Mutual alters | [N] ([%]) |
| Outgoing-only alters | [N] |
| Incoming-only alters | [N] |
| Ego reciprocity | [value] |
| Initiation ratio | [value] -- [interpretation] |

### Top Interlocutors
| Alter | Ego->Alter | Alter->Ego | Total | Pattern |
|-------|-----------|-----------|-------|---------|
| [user] | [N] | [N] | [N] | [balanced/ego-dominant/alter-dominant/one-directional] |

## Reciprocity Analysis
- Global reciprocity: [value]
- Ego alter reciprocity: [value]
- Weighted reciprocity: [value]
- Initiation ratio: [value] -- [interpretation]
- [Narrative: Is the user primarily a conversation starter or responder?]

## Audience-Dependent Voice Shifts

### Feature Variance Across Interlocutors
| Feature | Mean | Std Dev | CV | Interpretation |
|---------|------|---------|-----|----------------|
| avg_word_length | [val] | [val] | [val] | [stable/moderate/strong shift] |
| avg_sentence_length | [val] | [val] | [val] | |
| vocabulary_richness | [val] | [val] | [val] | |
| formality_proxy | [val] | [val] | [val] | |

### Accommodation Pattern
[Describe whether the user adapts voice by audience, and if so, how]

## Community Structure (if applicable)
- Communities detected: [N]
- Modularity: [value] -- [interpretation]
- Ego's community: [ID] with [N] members
- [Description of the ego's position within and across communities]

## Limitations and Caveats
- [Orphan references that could not be resolved]
- [Sparse graph warnings if applicable]
- [Features that could not be computed due to insufficient data]
- [Interaction frequency does NOT imply personal relationship strength]
- [Network position reflects online interaction patterns only]

## Downstream Use
[How these findings feed into persona synthesis, style replication, or archetype assignment]

Good Patterns

• Build directed graphs from reply relationships: Direction encodes who initiated vs. who responded. Never collapse to undirected until community detection requires it.
• Calculate degree centrality AND reciprocity ratios: In-degree alone misses the difference between a user who receives many one-off replies vs. one who sustains ongoing dialogues.
• Compare linguistic features across different interlocutor types: Group alters by interaction pattern (mutual vs. one-directional) or by community, then compare ego's voice features per group.
• Extract ego network before global analysis: The ego network is the analytically relevant subgraph for individual profiling. Global metrics provide context but the ego view is the deliverable.
• Use weighted edges: Interaction count matters. A single reply is qualitatively different from a 20-message exchange. Weight edges by interaction count.

Anti-Patterns

Anti-Pattern	Why It Fails	Instead
Treating all interactions as equal weight	A single reply and a 50-message exchange look identical	Weight edges by interaction count; filter by min_weight
Ignoring edge directionality	Conflates initiating with responding; loses reciprocity signal	Always build DiGraph first; convert to undirected only when algorithm requires it
Building graphs without filtering trivial interactions	One-word replies ("thanks", "lol") inflate edge counts without social signal	Filter by min_body_length before graph construction
Over-interpreting sparse graphs	A graph with 5 nodes and 3 edges cannot support centrality or community claims	Check minimum thresholds before computing metrics (see Insufficient Data)
Assuming high degree = high influence	A user replying "ok" to 100 different people has high out-degree but no influence	Cross-reference degree with interaction quality (body length, reciprocity)
Treating interaction frequency as relationship strength	50 hostile exchanges are not a "strong relationship"	Interaction frequency measures engagement volume, not valence or quality
Running community detection on tiny graphs	Modularity optimization is meaningless with <15 nodes	Skip community detection; report "graph too small"

Boundaries

This skill SHOULD:

• Map directed interaction networks from reply chains
• Identify frequent interlocutors and classify interaction patterns
• Measure reciprocity at global, ego, and per-alter levels
• Detect audience-dependent voice shifts via cross-alter feature comparison
• Calculate standard SNA metrics (centrality, density, reciprocity, components)
• Extract and analyze ego networks
• Produce a structured report at docs/analysis/10-network-social-graph.md

This skill should NOT:

• Infer personal relationships from interaction frequency alone (frequency measures engagement, not affection or trust)
• Deanonymize users or attempt to link pseudonymous accounts
• Build surveillance-style profiles or track users across platforms
• Assume network position reflects offline relationships or social status
• Make claims about the nature of relationships (friendly, hostile) without sentiment analysis from another skill
• Attribute motivation to interaction patterns ("they reply because they admire the user")

Insufficient Data Handling

digraph insufficient_data {
    rankdir=TB;
    node [shape=diamond, style=filled, fillcolor="#f0f0f0"];

    start [label="Total interactions?", shape=diamond];
    tiny [label="< 10 interactions", shape=box, fillcolor="#ffcccc"];
    small [label="10-50 interactions", shape=box, fillcolor="#fff3cc"];
    moderate [label="50-200 interactions", shape=box, fillcolor="#ffffcc"];
    adequate [label="> 200 interactions", shape=box, fillcolor="#ccffcc"];

    start -> tiny [label="< 10"];
    start -> small [label="10-50"];
    start -> moderate [label="50-200"];
    start -> adequate [label="> 200"];

    tiny_action [label="STOP: Report insufficient data.\nList raw reply pairs only.\nNo graph metrics.", shape=box, fillcolor="#ffcccc"];
    small_action [label="BASIC: Build graph.\nCompute degree and reciprocity.\nSkip community detection.\nSkip voice shift analysis.\nFlag all metrics as low-confidence.", shape=box, fillcolor="#fff3cc"];
    moderate_action [label="STANDARD: Full ego network.\nReciprocity and centrality.\nVoice shifts if 3+ alters.\nSkip community detection if < 15 nodes.", shape=box, fillcolor="#ffffcc"];
    adequate_action [label="FULL: Complete analysis pipeline.\nCommunity detection if 15+ nodes.\nVoice shift comparison.\nAll metrics.", shape=box, fillcolor="#ccffcc"];

    tiny -> tiny_action;
    small -> small_action;
    moderate -> moderate_action;
    adequate -> adequate_action;
}

Condition	Impact	Action
< 10 total interactions	No graph metrics are meaningful	List raw reply pairs. Report "insufficient data for graph analysis." Do NOT compute centrality or reciprocity.
10-50 interactions	Basic structure visible but metrics unstable	Build graph, compute degree and reciprocity only. Flag all values as "low-confidence (n=X)." Skip community detection and voice shifts.
50-200 interactions	Ego network viable, global metrics marginal	Full ego network analysis. Reciprocity and interlocutor classification. Voice shifts only if 3+ alters with 5+ interactions each. Skip community detection if < 15 nodes.
> 200 interactions	Full analysis viable	Complete pipeline including community detection (if 15+ nodes) and voice shift analysis.
No parent references in data	Cannot construct reply edges	Report "data lacks threading structure." Suggest alternative approaches (co-occurrence in same thread, temporal proximity).
Ego user has < 5 alters	Ego network too sparse for pattern detection	Report basic alter list. Skip voice shift analysis (need 3+ alters minimum). Skip interlocutor classification (need pattern variation).
> 50% orphan parent references	Graph is severely incomplete	Report the orphan rate. Proceed with available edges but caveat all metrics as "partial network -- [X]% of references unresolved."

Minimum thresholds for specific analyses:

Analysis	Minimum Requirement
Graph construction	10+ interactions, 3+ unique participants
Ego network extraction	Ego user has 3+ alters
Reciprocity measurement	5+ bidirectional pairs in network
Voice shift detection	3+ alters with 5+ interactions each
Community detection	15+ nodes in largest connected component
Betweenness centrality	10+ nodes (computationally expensive, analytically meaningless below this)

When in doubt: Report what you CAN compute and explicitly state what you CANNOT and why. A partial report with honest limitations is more valuable than a complete report built on insufficient data.

Common Mistakes

Mistake	Fix
Computing centrality on disconnected graph without noting components	Always report number of connected components; centrality is only meaningful within a component
Reporting graph density without context	Interaction graphs are almost always sparse (density < 0.1). Compare against expected density for the corpus type, not against 1.0.
Including self-replies in the graph	Filter source == target edges during extraction. Self-replies inflate degree without social meaning.
Treating the unfiltered graph as the analytical graph	Always report metrics on the filtered graph. Show unfiltered counts for context but analyze the filtered version.
Using undirected metrics on a directed graph	Use DiGraph-compatible functions. nx.degree_centrality on a DiGraph gives total degree; use in_degree_centrality / out_degree_centrality explicitly.
Not normalizing for corpus size when comparing users	Raw degree counts are not comparable across different-sized corpora. Use centrality (normalized) metrics.
Claiming "strong community structure" with modularity < 0.3	Modularity < 0.3 is weak. Only claim meaningful structure above 0.4.
Skipping the orphan reference count	Unreported orphans silently bias the graph toward interactions where both participants are in the dataset

References

• NetworkX Documentation: DiGraph
• NetworkX: Reciprocity
• NetworkX: Centrality Algorithms
• Reciprocity in Network Science (Wikipedia)
• Communication Accommodation Theory (Wikipedia)
• Ego Network Analysis (Hanneman & Riddle, Chapter 9)
• Social Network Analysis Survey (ACM Computing Surveys)
• User Profiling in Ego Networks (WWW 2014)
• Concepts and Measurements in SNA (D-Lab Berkeley)
• Louvain Community Detection (python-louvain)