C3
Raw session log. Atwl spec + example + 54-workflow library (atwl form).
4 turns (2 user · 2 assistant) json raw log
1 Researcher
You have received three files: the ATWL specification, which defines a formal language for representing visual analytics workflows; an example workflow expressed in ATWL; and a document titled ATWL Library: Titles and ATWL Representations, which contains 53 visual analytics workflows drawn from the published research literature, each formalised in ATWL. The library was curated by our research group.
Please read all three carefully before we proceed.
The ATWL specification describes the language's artifact types, transform intents, actor designations, loop constructs, and syntax. The example workflow illustrates how these elements are used together to describe a specific visual analytics process. Note that the example represents one type of workflow among many possible; its specific analytical structure reflects its particular task and should not be treated as a template that must be followed for other tasks. What the example illustrates is how the language is used — the syntax, the vocabulary, and the conventions for organising artifacts, transforms, and loops.
The library is a resource of formalised workflows that you may consult when designing your recommendation. Each library workflow addresses a specific analytical problem in a specific domain. You may draw on the library to identify workflow structures, iteration patterns, artifact typing conventions, or methodological approaches that could inform your recommendation for the target task. You may combine elements from multiple library workflows, adapt them, or design new elements as the task requires. You are not obliged to use the library, but you should treat it as a substantive source of design ideas.
In the next message I will describe a specific visual analytics design task. Your job will be to design an analytical workflow that addresses the task, and to express your recommendation both in natural language (for readability) and in ATWL (for structural precision). The natural-language description should explain the analytical logic of the workflow; the ATWL representation should formally specify the artifacts, transforms, loops, actors, and control flow. Where you draw on specific library workflows, please cite them by name so the sources of your recommendation are traceable.
Before we proceed, could you briefly confirm that you have absorbed the specification, the example, and the library, and let me know if you have any questions about the language, the syntax, or the library before the task is described?
2 Model
I need to confirm that I’ve understood the received text and clarify that there aren’t any questions. The user is asking for a brief confirmation in English. I'll mention that I reviewed the sources, including the 53 libraries, noting that the document content might be truncated. They provided a large amount of content, but I should state that I’m ready to proceed and will cite exact names when necessary.
Confirmed. I have reviewed the ATWL specification, the HITL example workflow, and the curated workflow library.
I understand the artifact types, transform intents, actors, arrangements, visualisations, patterns/models/knowledge/specifications, assignments, loops, and conditionals. I will use the example as a syntax and modelling convention reference—not as a mandatory analytical template—and will draw on and cite named library workflows where relevant.
I have no questions at this stage and am ready for the design task.
3 Researcher
The IEEE VIS conferences (Vis, InfoVis, VAST, SciVis) have accumulated 3596 papers between 1990 and 2024. Each paper record contains the list of authors, publication year, title, abstract, keywords, and citations. Author names have been disambiguated against DBLP (unique author identifiers are provided alongside the raw name strings).
A research analyst studying the intellectual structure of the visualization community wants to identify the major communities of collaborating researchers and characterise how they have evolved. The analyst expects that a community typically has a relatively stable core of leading researchers who continue collaborating across many years, while other members may move between communities over time or contribute to several communities simultaneously. Each community should also be associated with its major research foci — the topics or problem areas that characterise its work.
The analyst wants to answer questions such as: how many distinct communities can be identified? How stable is each community's core, and who are its leaders? What are its main research foci, and how have they evolved? How does membership overlap between communities, and how does it change over time? Do communities merge, split, or dissolve, and if so, when and why?
The analyst will need to inspect intermediate results visually and use domain knowledge to interpret patterns and make judgements at each stage.
Design an analytical workflow for this problem.
4 Model
Recommended analytical workflow
The workflow treats the corpus as a dynamic, overlapping co-authorship network enriched with paper-level topical content. It deliberately distinguishes:
- Communities: groups inferred from recurring collaboration structure, with authors allowed to belong to multiple communities.
- Core and leaders: authors with persistent, central membership within a community—not merely highly cited or prolific authors.
- Research foci: topic distributions derived from titles, abstracts, and keywords, then aggregated for each community and time period.
- Evolution: continuity, overlap, membership flows, and structural events such as merges, splits, emergence, and dissolution.
The analyst first creates annual or rolling multi-year co-authorship-network snapshots from disambiguated author IDs. A dynamic overlapping-community model then identifies communities while preserving membership strengths across time. The analyst iteratively evaluates whether the detected communities are structurally coherent, temporally plausible, and interpretable; they can adjust temporal resolution, community-resolution settings, overlap thresholds, and persistence regularisation.
In parallel, a topic model derives paper-level research-focus vectors from textual metadata. The analyst checks topic coherence and revises topic-model settings where necessary. Community-level topic profiles are then obtained by aggregating the papers and authors associated with each community in each time period.
The central visual interface combines:
- Temporal community evolution view: alluvial/lineage view showing communities over time, member retention, merges, splits, emergence, and dissolution.
- Community network view: node-link or NodeTrix-style view of selected community members and inter-community overlaps.
- Membership and leadership view: ranked persistent-core members, bridge authors, centrality, productivity, citations, and membership strengths.
- Topic evolution view: topic-by-community heatmaps and temporal profiles, linked to representative papers.
- Evidence view: selected papers, abstracts, keywords, citation links, and author histories supporting interpretation.
The analyst iteratively selects suspicious or important transitions—such as a split, merger, or loss of a core—and investigates associated author movements and changes in topic distributions. This supports explaining when a change occurred, which authors drove it, and why it may have occurred substantively.
This design draws particularly on:
- Dynamic Network Exploration / “Reducing Snapshots to Points” for snapshot-based dynamic-network representations and temporal structural exploration.
- NodeTrix / “Hybrid Network Community Analysis” for inspecting dense communities while retaining inter-community links and bridge actors.
- Human-Steered Topic Modelling / “Interactive Topic Modeling” for iterative evaluation and refinement of interpretable research topics.
- EventThread Analysis for representing branching, merging, and evolving group structures over time.
- Cluster-calendar workflow for analyst-guided refinement of clustering parameters and interpretability assessment.
ATWL representation
workflow ieee-vis-community-evolution-analysis
template: define-unit (authors and temporal networks) →
characterise (network and text features) →
loop(build-model (overlapping communities) →
define-unit (infer communities) →
build-model (topics) →
characterise (community profiles) →
contextualise → visualise →
abstract → assess →
generate-knowledge (refine specifications)) →
contextualise → visualise →
loop(abstract → define-unit (select transition) →
visualise → abstract → assess) →
generate-knowledge
description: "Identify, characterise, validate, and explain the evolution
of overlapping research communities in IEEE VIS publications from
1990–2024. Communities are inferred from dynamic co-authorship
structure, linked to research topics derived from paper text, and
interpreted through coordinated visual exploration."
# ============================================================================
# INPUT
# ============================================================================
artifact D_papers : entities
origin: given
internal structure: elementary
embedment: time
features:
- id: f_paper_id
value structure: atomic
value type: reference
description: "Unique paper identifier"
- id: f_year
value structure: atomic
value type: temporal
description: "Publication year, 1990–2024"
- id: f_authors
value structure: list
value type: reference
description: "Disambiguated DBLP author identifiers with raw name strings"
- id: f_title
value structure: atomic
value type: text
description: "Paper title"
- id: f_abstract
value structure: atomic
value type: text
description: "Paper abstract"
- id: f_keywords
value structure: list
value type: text
description: "Author- and venue-provided keywords"
- id: f_citations
value structure: list
value type: reference
description: "Cited paper identifiers"
description: "IEEE VIS, InfoVis, VAST, and SciVis papers published between
1990 and 2024, with disambiguated author identities, bibliographic text,
keywords, and citation information"
artifact S_temporal_network : specification
origin: given
representation form: "temporal-network construction parameters"
description: "Initial choice of annual or rolling multi-year windows,
edge weighting rule (e.g., co-authored-paper count with recency decay),
minimum activity threshold, and inclusion of citation-derived relations"
artifact S_community_model : specification
origin: given
representation form: "dynamic overlapping-community model parameters"
description: "Initial settings for number or resolution of communities,
overlap regularisation, temporal persistence regularisation, minimum
membership strength, lineage matching threshold, and core-member
persistence threshold"
artifact S_topic_model : specification
origin: given
representation form: "topic-model parameters"
description: "Initial topic count, text preprocessing, term weighting,
use of title/abstract/keywords, and topic-coherence criteria"
artifact K_domain_context : knowledge(D_papers)
origin: given
representation form: "domain annotations and interpretation notes"
description: "Analyst knowledge about visualization research areas,
historically important venues, known research programs, institutions,
and potentially meaningful author or topic changes"
# ============================================================================
# STEP 1: DEFINE AUTHORS AND TEMPORAL CO-AUTHORSHIP NETWORKS
# ============================================================================
transform T_define_authors :
intent: define-unit
manner: "group papers by disambiguated author identifier"
input: D_papers
output: D_authors
actor: machine
description: "Create one author entity per DBLP-disambiguated identifier,
retaining links to authored papers and activity history"
artifact D_authors : entities
internal structure: elementary
embedment: set
features:
- id: f_author_id
value structure: atomic
value type: reference
description: "Disambiguated DBLP author identifier"
- id: f_raw_names
value structure: list
value type: text
description: "Observed raw author-name strings"
- id: f_authored_papers
value structure: list
value type: reference
description: "Papers authored by this researcher"
- id: f_active_years
value structure: list
value type: temporal
description: "Publication years in the IEEE VIS corpus"
description: "Unique visualization-community researchers identified through
DBLP-based author disambiguation"
transform T_construct_networks :
intent: define-unit
manner: "time-partitioning and co-authorship-network aggregation"
input: D_papers, D_authors, S_temporal_network
output: D_network_snapshots
actor: machine
description: "Construct annual or rolling-window weighted co-authorship
network snapshots; nodes are active authors and edges represent
collaboration strength"
artifact D_network_snapshots : entities
internal structure: formation
embedment: time
features:
- id: f_coauthor_edges
value structure: relational configuration
value type: {reference, numeric}
description: "Weighted co-authorship graph for each temporal snapshot"
- id: f_active_authors
value structure: list
value type: reference
description: "Authors active in the snapshot window"
- id: f_snapshot_period
value structure: atomic
value type: temporal
description: "Year or rolling time-window interval"
description: "Temporal sequence of weighted co-authorship-network snapshots
representing evolving collaboration structure"
# ============================================================================
# STEP 2: DERIVE NETWORK AND TEXTUAL FEATURES
# ============================================================================
transform T_characterise_network :
intent: characterise
manner: "compute graph statistics and inter-author relations"
input: D_network_snapshots, D_authors
output: F_network_structure, F_author_centrality
actor: machine
description: "Compute node centrality, weighted degree, brokerage,
collaboration persistence, pairwise collaboration similarity, and
temporal author-activity descriptors"
artifact F_network_structure : feature(D_network_snapshots)
value structure: relational configuration
value type: {reference, numeric}
representation form: "weighted dynamic co-authorship graph descriptors"
description: "Structural descriptors of author relationships, including
edge strength, persistence, neighbourhood similarity, and temporal
continuity between snapshots"
artifact F_author_centrality : feature(D_authors)
value structure: vector
value type: numeric
description: "Per-author, per-time-window structural measures including
weighted degree, betweenness, eigenvector centrality, brokerage,
publication count, and collaboration persistence"
transform T_encode_documents :
intent: characterise
manner: "text encoding and term-document representation"
input: D_papers, S_topic_model
output: F_paper_text
actor: machine
description: "Preprocess titles, abstracts, and keywords and create
weighted document representations for topic discovery"
artifact F_paper_text : feature(D_papers)
value structure: matrix
value type: numeric
representation form: "weighted term-document matrix"
description: "Textual representation of each paper based on title,
abstract, and keywords"
# ============================================================================
# STEP 3: ITERATIVE MODEL CALIBRATION AND COMMUNITY DISCOVERY
# ============================================================================
loop L_model_refinement:
purpose: "Refine dynamic overlapping community and topic models until
communities are structurally coherent, temporally plausible, and
interpretable, while topics are semantically coherent and useful for
characterising community research foci"
until: "Community boundaries, overlaps, cores, lineage events, and topic
descriptions are judged interpretable, stable enough for the research
questions, and supported by visual and domain evidence"
body:
transform T_build_community_model :
intent: build-model
manner: "fit dynamic overlapping community model"
input: D_network_snapshots, F_network_structure, S_community_model
output: M_community_model
actor: machine
description: "Fit a temporally regularised overlapping-community model
so authors may have multiple membership strengths while recurring
collaboration structure is encouraged to persist across time"
artifact M_community_model : model(D_network_snapshots,
F_network_structure)
model type: "dynamic overlapping community model"
representation form: "time-indexed author-community membership matrix
with temporal regularisation and community lineage correspondences"
description: "Model of evolving collaboration communities allowing
partial and simultaneous membership in multiple communities"
transform T_infer_communities :
intent: define-unit
manner: "infer overlapping dynamic communities"
input: D_network_snapshots, M_community_model, S_community_model
output: D_communities, F_membership_strength, F_lineage_links
actor: machine
description: "Infer communities in each time period, author membership
strengths, and correspondence links between communities in adjacent
periods"
artifact D_communities : entities
internal structure: group/cluster
embedment: time
features:
- id: f_community_id
value structure: atomic
value type: categorical
description: "Persistent or period-specific community identifier"
- id: f_member_authors
value structure: list
value type: reference
description: "Authors associated with the community"
- id: f_time_period
value structure: atomic
value type: temporal
description: "Snapshot window in which the community exists"
description: "Time-indexed collaboration communities whose memberships
may overlap"
artifact F_membership_strength : feature(D_authors)
value structure: matrix
value type: numeric
description: "Author-by-community membership strengths over time;
authors may hold substantial membership in several communities"
artifact F_lineage_links : feature(D_communities)
value structure: relational configuration
value type: numeric
representation form: "weighted community correspondence graph"
description: "Continuity, split, merge, emergence, and disappearance
links between communities in consecutive temporal windows"
transform T_build_topic_model :
intent: build-model
manner: "fit topic model"
input: F_paper_text, S_topic_model
output: M_topic_model
actor: machine
description: "Build a topic model identifying research themes from
paper titles, abstracts, and keywords"
artifact M_topic_model : model(F_paper_text)
model type: "topic model"
representation form: "topic-term and paper-topic weight matrices"
description: "Model of latent research topics in IEEE VIS publications"
transform T_characterise_topics :
intent: characterise
manner: "compute paper-topic, community-topic, and temporal topic profiles"
input: D_papers, D_communities, F_membership_strength, M_topic_model
output: F_paper_topics, F_community_topics, F_topic_trends
actor: machine
description: "Assign topic weights to papers and aggregate them by
community and time, weighted by authors' community memberships"
artifact F_paper_topics : feature(D_papers)
value structure: vector
value type: numeric
description: "Topic-weight vector for each paper"
artifact F_community_topics : feature(D_communities)
value structure: vector
value type: numeric
description: "Topic distribution for each community in each temporal
window, calculated from associated papers and membership strengths"
artifact F_topic_trends : feature(D_communities)
value structure: matrix
value type: numeric
description: "Temporal topic-prevalence trajectories for each
community"
transform T_characterise_community_roles :
intent: characterise
manner: "derive cores, leaders, bridges, overlap, and stability measures"
input: D_communities, F_membership_strength, F_author_centrality,
F_lineage_links, F_community_topics
output: F_community_roles, F_community_stability, F_overlap
actor: machine
description: "Compute persistent core membership, leadership rankings,
bridge roles, community retention, topic stability, and pairwise
community overlap"
artifact F_community_roles : feature(D_authors)
value structure: vector
value type: numeric
description: "Per-author community-role measures: persistent core
score, within-community centrality, leadership rank, bridge score,
number of concurrent communities, and membership trajectory"
artifact F_community_stability : feature(D_communities)
value structure: vector
value type: numeric
description: "Community-level retention, member turnover, core
persistence, cohesion, temporal continuity, size change, and
topic-change measures"
artifact F_overlap : feature(D_communities)
value structure: matrix
value type: numeric
description: "Pairwise community overlap through shared or
multi-membership authors, calculated per time period"
transform T_arrange_community_projection :
intent: contextualise
manner: "projection-based community and author similarity layout"
input: D_communities, F_membership_strength, F_community_topics,
F_overlap
output: D_community_projection_space, A_community_projection
actor: machine
description: "Create a 2D similarity space where proximity reflects
similarity in collaboration membership, topical focus, and overlap"
artifact D_community_projection_space : entities
internal structure: elementary
features:
- id: f_dimensions
value structure: atomic
value type: numeric
description: "Two projection dimensions"
description: "Computed 2D similarity space for communities and authors"
artifact A_community_projection : arrangement(D_communities)
context: D_community_projection_space
principle: "projection preserving similarity in membership, overlap,
and topic-profile structure"
description: "2D arrangement exposing related, overlapping, or
separated communities"
transform T_arrange_evolution :
intent: contextualise
manner: "time-based lineage arrangement"
input: D_communities, F_lineage_links, F_community_stability
output: A_evolution
actor: machine
description: "Arrange communities by time and connect them through
weighted lineage links to expose continuity, splits, merges,
emergence, and dissolution"
artifact A_evolution : arrangement(D_communities)
context: D_network_snapshots
principle: "horizontal time axis; vertically ordered communities;
ribbon links encode membership continuity and lineage relations"
description: "Temporal lineage arrangement of evolving overlapping
communities"
transform T_visualise_model_results :
intent: visualise
manner: "coordinated community, evolution, membership, and topic views"
input: A_community_projection, A_evolution, D_communities,
F_membership_strength, F_community_roles,
F_community_stability, F_community_topics, F_topic_trends,
F_overlap
output: V_community_overview
actor: machine
description: "Display a coordinated dashboard: community projection,
temporal lineage ribbons, membership-overlap matrix, leader/core
ranking, stability plots, and topic evolution profiles"
artifact V_community_overview : visualisation(A_community_projection,
A_evolution, F_community_roles, F_community_stability,
F_community_topics, F_overlap)
layout: "coordinated dashboard with similarity projection, temporal
alluvial lineage view, overlap matrix, ranked role table, and
topic-profile panels"
form: "points and hulls, alluvial ribbons, matrices, ranked bars,
line charts, and topic heatmaps"
encoding: "community position from projection; ribbon width from
retained membership; ribbon branching/convergence from split/merge;
colour from persistent community identity; node size from active
member count; rank bars from core and leadership scores; heatmap
intensity from topic prevalence; matrix intensity from overlap"
description: "Interactive visualisation for evaluating communities,
membership overlap, core stability, leadership, topical focus,
and temporal change"
transform T_interpret_model_results :
intent: abstract
manner: "perception-and-interpretation of community structure"
input: V_community_overview, K_domain_context
output: P_community_structure, P_topic_structure
actor: human
description: "Interpret candidate communities, their stable cores,
bridge authors, topical identities, suspicious model artifacts,
and candidate merge or split events"
artifact P_community_structure : pattern(D_communities,
F_community_stability)
representation form: "interpreted community and evolution descriptions"
description: "Tentative descriptions of major communities, stable
cores, leaders, overlap structures, bridge authors, and lineage
events"
artifact P_topic_structure : pattern(F_community_topics,
F_topic_trends)
representation form: "named research-topic descriptions"
description: "Tentative semantic descriptions of recurring research
foci and their evolution across communities"
transform T_assess_models :
intent: assess
manner: "evaluate structural validity, temporal plausibility, and topic coherence"
input: V_community_overview, P_community_structure, P_topic_structure,
S_community_model, S_topic_model, K_domain_context
output: K_model_assessment
actor: human
description: "Judge whether community boundaries, overlap levels,
lineage events, core definitions, and topic descriptions are
meaningful enough for substantive analysis"
artifact K_model_assessment : knowledge(D_communities, M_topic_model)
representation form: "quality judgment"
description: "Assessment of community coherence, temporal stability,
interpretability of overlap and leadership, and semantic coherence
of research topics; identifies required model refinements"
if K_model_assessment indicates model refinement needed:
then:
transform T_refine_models :
intent: generate-knowledge
manner: "adjust temporal, community, and topic-model specifications"
input: K_model_assessment, V_community_overview,
S_temporal_network, S_community_model, S_topic_model
output: S_temporal_network_r, S_community_model_r,
S_topic_model_r
actor: human
description: "Revise time-window size, edge weighting, community
overlap and persistence settings, lineage thresholds, topic
count, or text preprocessing based on visual assessment"
artifact S_temporal_network_r : specification
representation form: "revised temporal-network construction parameters"
description: "Updated snapshot resolution, window width, edge
weighting, or inclusion criteria"
artifact S_community_model_r : specification
representation form: "revised dynamic overlapping-community parameters"
description: "Updated overlap, persistence, resolution, core,
and lineage-matching settings"
artifact S_topic_model_r : specification
representation form: "revised topic-model parameters"
description: "Updated topic count, preprocessing, or coherence
settings"
assign:
S_temporal_network := S_temporal_network_r
S_community_model := S_community_model_r
S_topic_model := S_topic_model_r
else:
exit loop L_model_refinement
end loop L_model_refinement
# ============================================================================
# STEP 4: DETAILED EXPLORATION OF EVOLUTION EVENTS
# ============================================================================
artifact P_evolution_findings : pattern(D_communities)
representation form: "accumulated community-evolution interpretations"
description: "Initially empty; accumulates interpreted community
transitions, causes, leadership changes, topical shifts, and evidence
from selected papers"
loop L_evolution_investigation:
purpose: "Investigate consequential community transitions and membership
changes to explain how communities evolved and why merges, splits,
emergence, or dissolution occurred"
until: "Major communities, their cores and leaders, membership overlaps,
topical trajectories, and consequential evolution events have been
sufficiently explained for the analyst's research questions"
body:
transform T_identify_transition :
intent: abstract
manner: "identify salient evolution events and anomalous changes"
input: V_community_overview, P_community_structure,
P_topic_structure, P_evolution_findings
output: P_transition_candidates
actor: human
description: "Identify a significant merge, split, emergence,
dissolution, leadership transition, unusually high overlap, or
topical shift for detailed investigation"
artifact P_transition_candidates : pattern(D_communities,
F_lineage_links)
representation form: "candidate evolution-event descriptions"
description: "Candidate events requiring explanation, including their
time interval, involved communities, and preliminary structural or
topical interpretation"
transform T_select_transition :
intent: define-unit
manner: "interactive selection of communities, authors, and time interval"
input: P_transition_candidates, V_community_overview
output: D_transition_focus
actor: human
description: "Select communities, involved authors, and time windows
for detailed analysis of a particular evolution event"
artifact D_transition_focus : entities
internal structure: group/cluster
embedment: time
description: "Focused subset of communities, authors, papers, and
temporal windows implicated in a selected evolution event"
transform T_characterise_transition_evidence :
intent: characterise
manner: "derive membership-flow, publication, citation, and topic-shift evidence"
input: D_transition_focus, D_papers, D_authors, D_communities,
F_membership_strength, F_community_roles, F_community_topics,
F_topic_trends, F_lineage_links
output: F_transition_evidence
actor: machine
description: "Compute member inflow and outflow, retained core,
leadership change, shared-author bridges, representative papers,
citation links, and topic-distribution differences before and after
the selected event"
artifact F_transition_evidence : feature(D_transition_focus)
value structure: vector
value type: {numeric, categorical, temporal, reference}
description: "Evidence for explaining a selected event: author flows,
core retention, leadership changes, topic shifts, representative
publications, and citation relations"
transform T_visualise_transition :
intent: visualise
manner: "linked event-detail views"
input: D_transition_focus, F_transition_evidence, D_papers,
F_paper_topics, F_community_roles
output: V_transition_detail
actor: machine
description: "Show selected communities as linked NodeTrix or
node-link detail, author membership trajectories, before-after
topic profiles, membership-flow ribbons, and representative-paper
evidence"
artifact V_transition_detail : visualisation(D_transition_focus,
F_transition_evidence, D_papers, F_paper_topics)
layout: "coordinated event-detail views: community network, author
membership trajectories, before-after topic profiles, flow ribbons,
and representative-paper list"
form: "NodeTrix matrices and node-link links, alluvial ribbons,
ranked lists, line charts, topic bars, and paper cards"
encoding: "matrix cells and links from co-authorship strength;
author line thickness from membership strength; colour from
community identity; topic bar length from prevalence; paper cards
show title, keywords, abstract excerpt, citations, and authors"
description: "Detailed visual evidence supporting explanation of why
a community changed"
transform T_interpret_transition :
intent: abstract
manner: "explain merge, split, dissolution, or topic shift"
input: V_transition_detail, K_domain_context, P_evolution_findings
output: P_evolution_findings_r
actor: human
description: "Interpret the selected event using structural and
topical evidence; distinguish substantive reorientation from a
methodological artifact or short-lived collaboration fluctuation"
artifact P_evolution_findings_r : pattern(D_communities, D_papers)
representation form: "accumulated evidence-grounded evolution explanations"
description: "Accumulated explanations of community emergence,
persistence, merging, splitting, dissolution, leadership change,
bridge roles, and changing research foci"
transform T_assess_evolution_coverage :
intent: assess
manner: "evaluate completeness of community-evolution understanding"
input: P_evolution_findings_r, V_community_overview,
V_transition_detail
output: K_evolution_assessment
actor: human
description: "Judge whether enough major communities and evolution
events have been examined, or whether another event, period, or
community requires investigation"
artifact K_evolution_assessment : knowledge(P_evolution_findings_r)
representation form: "quality judgment"
description: "Assessment of whether the analyst has sufficient
evidence to answer questions about community count, stable cores,
leaders, overlap, research foci, and evolution"
if K_evolution_assessment indicates sufficient understanding:
then:
assign:
P_evolution_findings := P_evolution_findings_r
exit loop L_evolution_investigation
else:
assign:
P_evolution_findings := P_evolution_findings_r
end loop L_evolution_investigation
# ============================================================================
# STEP 5: SYNTHESIS
# ============================================================================
transform T_synthesise_findings :
intent: generate-knowledge
manner: "formulate statements and evidence-backed community profiles"
input: P_community_structure, P_topic_structure, P_evolution_findings,
F_community_stability, F_community_roles, F_overlap,
K_model_assessment, K_evolution_assessment
output: K_community_knowledge
actor: human
description: "Produce an evidence-backed account of the intellectual
structure and evolution of the IEEE VIS community"
artifact K_community_knowledge : knowledge(P_community_structure,
P_topic_structure, P_evolution_findings)
representation form: "community profiles, evolution narratives, and
methodological provenance"
description: "Final analytical knowledge: number and identity of major
collaborating communities; stable cores and leaders; bridge authors and
overlapping memberships; research foci and their temporal evolution;
merges, splits, emergence, and dissolution events with supporting paper,
citation, membership, and topic evidence; plus documented modeling
choices and assessment rationale"