Salman Rushdie: Midnight’s Children (1981)
This example uses Hellixia for a holistic analysis of Salman Rushdie’s Midnight’s Children. The workflow creates a semantic network of narrative, character, and thematic dimensions, then uses the Report Analyzer to summarize node forces and relationship strengths in the generated network.
Holistic Analysis
Workflow for Creating a Semantic Network
Create the node “Midnight’s Children, by Salman Rushdie”.
Use the Dimension Elicitor, employing a broad array of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Theses, and Values.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to the analysis.
Disregard the “Midnight’s Children, by Salman Rushdie” node and run the Embedding Generator on all remaining nodes to capture the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node’s comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on the graph; this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with the intended presentation.
Switch over to Validation Mode F5 and select Skeleton View. Since the network does not represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Workflow for Node Force Analysis
Return to Modeling Mode F4 and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode F5 to run a Node Force analysis.
Analysis of the Relationship Analysis Report
Switch to Validation Mode F5.
This report returns two key pieces of information: the Node Force, which indicates the influence and importance of each node within the network, and the strength of all relationships as described in the network. This provides a comprehensive view of how nodes are interconnected and the significance of these connections.
With the Relationship Report in hand, proceed to run the Report Analyzer. This tool is designed to synthesize the data into a narrative form. It interprets the node forces and relationship strengths to create a story that summarizes the main dynamics of the domain. This narrative provides a digestible and insightful summary of the complex relationships and key elements within the network.
Workflow for Creating the Hierarchical Semantic Network
This operation will categorize analogous variables based on semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors.
Use the Export Descriptions function and save the newly created descriptions.
Return to Modeling Mode F4 and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the Delete Unfixed Arcs option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you’ve created.
Switch to Validation Mode F5 and run Node Force analysis.
Focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Reviewing the Hierarchical Network Through the Report Analyzer’s Relationship Analysis
After the initial Report Analyzer review of the “manifest variables” network, the next step generates a report for the hierarchical network, i.e., the domain of latent variables.
