Subject Cross-Fusion User Guide

Subject cross-fusion shows how disciplines, technical classifications, or AI topics co-occur in the same set of documents and how their relationships appear, strengthen, weaken, or recover over time. SciMetrics performs the deterministic calculation from the active dataset; this documentation explains the UI workflow, indicators, evidence review, and MCP/API operation path.

The examples on this page use the dataset Hot Dry Rock Topic Identification - Level 3/4 Topics.db and calculate by AITopic. In real projects, you may also use Class1, Class2, Class3, or Class4 when those fields actually contain disciplinary categories, technical categories, research-direction categories, or manually curated topic levels.

When to use it

• Identify cross-disciplinary collaboration opportunities for colleges or research management teams.
• Provide evidence for emerging interdisciplinary directions, platform planning, and joint project design.
• Add "which topics are connecting" and "which connections are strengthening" to subject service reports.
• Discover clues about method transfer, shared research problems, and application diffusion.

Data preparation and dimension selection

Open a curated SciMetrics database, or first import literature records from Web of Science, Scopus, CNKI, Excel, or other sources. Cross-fusion analysis requires each document to be linked to at least one topic or classification label. If one document is linked to multiple labels, those labels may form fusion relations.

In the sample dataset, Class3 contains only a few broad categories, and Class4 is actually a document type field. Therefore, this guide uses AITopic for interpretation. Before using Class1-Class4, inspect what the fields really contain. If they store disciplinary or technical categories, they can be used directly for subject cross-fusion. If they store document types, source types, or overly broad buckets, they should not be interpreted as topic-fusion results.

UI workflow

After opening the dataset, enter the subject cross-fusion or topic cross-fusion feature from the analysis and visualization area. A normal workflow is: open the dataset, select the analysis dimension, set thresholds, run the calculation, read the results, and export outputs.

After calculation, SciMetrics builds a cumulative time window for each year: from the earliest year up to the selected year. Each yearly frame contains nodes, edges, fusion-document counts, network fusion breadth, and the cumulative fusion index. You can switch years to inspect whether relations are new, continuing, strengthened, weakened, or recovered.

Parameters

Thresholds control the granularity of the network. A low node threshold may introduce many accidental topics; a high threshold may hide detailed directions. A low edge threshold may create many weak links; a high threshold may retain only a few strong relations. Start with the default values, then adjust according to node count, edge count, and graph readability.

Read the yearly overview

The yearly overview is suitable for report summaries. It explains the sample size, fusion-document ratio, network size, and overall fusion level. The 2026 sample result is:

A high document fusion rate means many documents are multi-topic. A high network fusion breadth means relations are not concentrated only among a few topics. A high cumulative fusion index indicates stronger overall fusion in the network.

Read the cross-fusion network

In the network, nodes represent topics, and edges represent two topics co-occurring in the same documents. Larger nodes usually indicate larger cumulative document volume. Thicker edges indicate a higher pairwise fusion index. Colors are commonly used to distinguish network clusters.

Recommended reading order:

1. Start with large nodes to identify topics with a strong literature base.
2. Inspect thick edges to find stronger topic combinations.
3. Check new or strengthened relations to locate recent cross-fusion clues.
4. Return to evidence documents to verify whether the relation reflects real problem, method, technology, or application fusion.

Read node fusion index

The node fusion index measures how strongly a topic participates in cross-fusion. It considers how many documents in the topic are fusion documents and how many effective neighbors the topic connects to. Topics with high scores are good candidates for deeper review.

In the sample result, high-fusion topics include artificial fracture evolution mechanisms and development, rock-mechanics fracture damage indicators, hydraulic-fracturing reservoir stimulation methods, rock-mechanics permeability-correlation indicators, THM coupled behavior, rock-mechanics strength indicators, and rock-mechanics temperature-sensitivity indicators. Reports should not list the score only; they should explain which topics are connected and why the node may work as a cross-fusion hub.

Read fusion relations

The fusion-relations table shows co-document counts, fractional weights, normalized strength, pairwise fusion index, and relation status between two topics. Status values include new, continuing, strengthened, weakened, and recovered.

Review evidence documents

Every cross-fusion conclusion should return to evidence documents. A strong relation does not automatically mean real interdisciplinary fusion; it may be a surface connection caused by label co-occurrence or classification practice. Open the Evidence Documents worksheet and review source node, target node, document ID, title, year, and all labels.

During review, check whether:

• The co-documents' titles and abstracts truly involve both topics.
• The fusion happens at the problem, method, technology, data, or application level.
• PMRC results, keywords, authors, institutions, or full texts are needed for verification.
• The report explains which papers support the relation and what research logic connects them.

Save, open, and export

Cross-fusion results can be saved as a native .crossfusion file and exported as Excel. For formal reporting, save both the native file and Excel output so the graph can be reproduced and the tables can be reviewed later.

Desktop supports the interactive window and SVG export. Headless supports the same calculation, native files, Excel, state inspection, and year selection, but does not create a window. An SVG request in Headless returns an explicit instruction to use Desktop instead of reporting a false success.

Use results in a subject service report

Cross-fusion results are suitable for subject service reports, college development analysis, interdisciplinary platform planning, and major project planning. A useful reporting order is:

1. State the data source, time range, checked scope, and analysis dimension.
2. Report yearly overview indicators and explain the overall fusion level.
3. List high-fusion topic nodes and explain their connected topics and possible roles.
4. List strong fusion relations and explain their status and co-document evidence.
5. Provide service recommendations, distinguishing observation clues, collaboration opportunities, and directions for focused cultivation.

In the sample dataset, hot dry rock research shows a high document fusion rate under the AI-topic dimension. This suggests that many papers connect multiple topics. A report can focus on fusion among rock-mechanics indicators, hydraulic-fracturing reservoir stimulation, THM coupled behavior, heat-exchange efficiency, and enhanced geothermal systems.

How to ask an AI assistant without knowing an API

Daily use does not require command names. State the dimension, thresholds, data scope, year, and desired output. If a choice is unclear, ask the assistant to inspect the data before recommending it.

Actually use the scim MCP and first check whether the current dataset supports cross-fusion.
Calculate cross-fusion by AI topic with at least 2 documents per node and 2 co-documents per edge, using checked documents only.
Open the interactive window at the latest year and explain the document fusion rate, network fusion breadth, and cumulative fusion index.
Read the window state again after execution to verify the result.

For a classification dimension:

Use SciMetrics MCP to calculate cross-fusion by Class2.
Use only checked documents and checked classification items, with node threshold 3 and edge threshold 2.
Save the complete result to D:\Research\output\class2.crossfusion and export the 2024 table to D:\Research\output\class2-2024.xlsx.
Verify both files afterward.

If you are not sure which classification level to use:

First inspect whether Class1-Class4 and AI topics are available for cross-fusion in the current dataset. Do not run immediately.
Recommend one dimension based on data coverage, and explain what happens if the node or edge threshold is too high or too low.

MCP and Automation API

MCP discovers these commands from the running SciMetrics host, so it does not duplicate the cross-fusion algorithm:

System integrations can POST to the local Desktop endpoint http://127.0.0.1:37618/api/automation/command:

{
  "command": "analysis.cross_fusion",
  "arguments": {
    "dimension": "AITopic",
    "minNodeDocumentCount": 2,
    "minEdgeDocumentCount": 2,
    "onlyCheckedDocuments": true,
    "year": 2026,
    "openWindow": true,
    "nativePath": "D:\\Research\\output\\ai-topic.crossfusion",
    "excelPath": "D:\\Research\\output\\ai-topic-2026.xlsx",
    "language": "en-US"
  }
}

To control the current window before export, call:

{
  "command": "visual.set_cross_fusion_options",
  "arguments": {
    "year": 2022,
    "showLabels": true,
    "showClusters": true,
    "showClusterHulls": true,
    "backgroundColor": "#FFFFFF"
  }
}

Then call visual.export_cross_fusion with path, optional format, and optional year. The Headless endpoint defaults to http://127.0.0.1:37619/api/automation/command.

FAQ

Why are there no fusion edges?

Possible causes include documents having only one label, an edge threshold that is too high, or no shared topics/classifications in the data. Lower the edge threshold or check whether labels were written correctly.

Does a high fusion index always mean the direction is worth strategic investment?

No. The index is only an entry point. Formal service recommendations should also consider representative papers, team capacity, college needs, and expert judgment.

How can I avoid surface-level cross-fusion?

Return to evidence documents and judge whether the co-documents truly show shared problems, method transfer, technical diffusion, or application-scenario fusion.

How should I use the exported Excel file?

Use the yearly overview for report summaries, the node table for topic ranking, the relations table for identifying topic combinations, and the evidence-document table for source review.