VIS · Claude Sonnet 5 · C3

workflow vis-community-evolution-analysis template: define-unit (windows, authors, networks) → loop(define-unit (detect communities) → characterise (quality) → visualise → assess) → characterise (overlaps) → loop(define-unit (match lineages) → contextualise → visualise → assess) → characterise (core & leaders) → visualise → abstract → loop(build-model (topics) → visualise → abstract → assess) → characterise (author trajectories) → contextualise → visualise → abstract → loop(abstract (integrate) → assess) → generate-knowledge description: "Identify communities of collaborating researchers in the IEEE VIS literature (1990-2024) via time-windowed co-authorship networks, track their evolution (continuation, merger, splitting, birth, dissolution) across windows, characterise core stability, leadership, research foci, and cross-community researcher mobility"

artifact D_papers : entities origin: given internal structure: elementary embedment: {set, time} features: - id: f_authors value structure: list value type: reference description: "DBLP-disambiguated author identifiers" - id: f_year value structure: atomic value type: temporal description: "Publication year" - 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-supplied keywords" - id: f_citations value structure: atomic value type: numeric description: "Citation count" description: "3596 IEEE VIS/InfoVis/VAST/SciVis papers (1990-2024) with disambiguated author lists" artifact S_window_config : specification origin: given representation form: "parameter settings" description: "Initial time-window scheme: window length and stride (e.g. 3-year sliding windows, 1-year stride) for constructing successive co-authorship snapshots"

transform T_define_windows : intent: define-unit manner: "time-partitioning into sliding windows" input: D_papers, S_window_config output: D_time_windows actor: machine description: "Partition the publication timeline into overlapping multi-year windows serving as the temporal resolution for community detection" artifact D_time_windows : entities internal structure: episode embedment: time features: - id: f_window_id value structure: atomic value type: categorical description: "Window identifier" - id: f_year_range value structure: vector value type: temporal description: "Start and end year of the window" description: "Bounded multi-year intervals over which co-authorship networks and communities are computed" transform T_extract_authors : intent: define-unit manner: "extract" input: D_papers output: D_authors actor: machine description: "Extract unique disambiguated authors as first-class analysis units" artifact D_authors : entities internal structure: elementary embedment: set features: - id: f_total_papers value structure: atomic value type: numeric description: "Total number of papers authored" - id: f_career_span value structure: vector value type: temporal description: "First and last publication year" description: "Individual researchers, disambiguated via DBLP identifiers" transform T_build_networks : intent: define-unit manner: "aggregate co-authorship into per-window network snapshots" input: D_papers, D_authors, D_time_windows output: D_networks actor: machine description: "Construct one weighted co-authorship network per time window: nodes are authors active in the window, edges are weighted by number of jointly authored papers" artifact D_networks : entities internal structure: formation embedment: time features: - id: f_coauthor_graph value structure: relational configuration value type: {reference, numeric} representation form: "weighted undirected co-authorship graph" description: "Author nodes with co-authorship-count edge weights for this window" description: "Temporal sequence of co-authorship network snapshots, one per time window"

artifact S_community_params : specification origin: given representation form: "parameter settings" description: "Community-detection algorithm choice and resolution parameter (e.g. Louvain modularity resolution) applied uniformly across all window networks" loop L_community_detection: purpose: "Calibrate community-detection resolution so that communities are neither over-fragmented nor overly coarse, and remain interpretable across the whole time span" until: "Modularity, size distribution, and visual interpretability of communities are satisfactory across the majority of time windows" body: transform T_detect_communities : intent: define-unit manner: "graph community detection (modularity optimisation, e.g. Louvain), applied independently per window" input: D_networks, S_community_params output: D_communities, F_community_label actor: machine description: "Partition each window's co-authorship network into communities of densely collaborating authors" artifact D_communities : entities internal structure: group/cluster embedment: {set, time} features: - id: f_window_id value structure: atomic value type: reference description: "Time window this community instance belongs to" - id: f_size value structure: atomic value type: numeric description: "Number of member authors" description: "Community instances: groups of authors detected as densely collaborating within one specific time window" artifact F_community_label : feature(D_authors) value structure: vector value type: categorical description: "Per-window community-assignment sequence for each author across all time windows in which they were active" transform T_compute_quality : intent: characterise manner: "compute-statistics (modularity, size distribution)" input: D_communities, D_networks output: F_quality actor: machine description: "Compute per-window modularity score, community count, mean/variance of community size, and singleton-author fraction" artifact F_quality : feature(D_networks) value structure: vector value type: numeric description: "Per-window community-detection quality metrics" transform T_visualise_communities : intent: visualise manner: "small-multiples node-link / hybrid NodeTrix-style views per window" input: D_networks, D_communities, F_community_label output: V_communities actor: machine description: "Display each window's network with communities colour-coded; densest communities optionally shown as matrices (NodeTrix-style) to reveal internal collaboration structure" artifact V_communities : visualisation(D_networks, D_communities, F_community_label) layout: "small multiples, one panel per time window" form: "node-link diagrams with colour-coded community membership; NodeTrix-style hybrid matrix insets for dense communities" encoding: "node colour: community label; node size: author publication count; matrix cell: intra-community co-authorship count" description: "Per-window overview supporting visual judgment of community coherence and granularity" transform T_assess_communities : intent: assess manner: "evaluate-clustering-quality" input: V_communities, F_quality output: K_community_quality actor: human description: "Judge whether modularity, size distribution, and visual coherence of communities are acceptable across windows" artifact K_community_quality : knowledge(D_communities) representation form: "quality judgment" description: "Assessment of whether current resolution parameter yields interpretable, appropriately granular communities" if K_community_quality indicates refinement needed: then: transform T_adjust_community_params : intent: generate-knowledge manner: "adjust resolution parameter based on assessment" input: K_community_quality, S_community_params output: S_community_params_r actor: human description: "Revise resolution or algorithm choice to correct over-fragmentation or excessive coarseness" artifact S_community_params_r : specification representation form: "parameter settings" description: "Updated community-detection configuration" assign: S_community_params := S_community_params_r else: exit loop L_community_detection end loop L_community_detection

transform T_compute_overlaps : intent: characterise manner: "define-relations (similarity) — Jaccard overlap of member sets" input: D_communities output: F_overlap actor: machine description: "Compute pairwise membership-overlap similarity between every pair of community instances in temporally adjacent windows" artifact F_overlap : feature(D_communities) value structure: relational configuration value type: numeric representation form: "directed overlap graph (Jaccard similarity) between community instances of temporally adjacent windows" description: "Basis for linking community instances into persistent lineages" artifact S_matching_params : specification origin: given representation form: "parameter settings" description: "Overlap threshold above which two community instances in adjacent windows are linked as continuous, and rules for classifying one-to-many / many-to-one links as split / merge" loop L_lineage_tracking: purpose: "Calibrate the overlap threshold so that resulting community lineages and their merge/split/birth/dissolution events are structurally plausible" until: "The evolution diagram shows neither excessive fragmentation (spurious births/dissolutions) nor implausible over-merging" body: transform T_match_lineages : intent: define-unit manner: "link community instances above overlap threshold into lineages; classify each linkage as continuation, merge, split, birth, or dissolution" input: D_communities, F_overlap, S_matching_params output: D_lineages, F_transition_events actor: machine description: "Track community identity across windows, producing persistent lineages and typed transition events between consecutive windows" artifact D_lineages : entities internal structure: sequence embedment: set features: - id: f_first_window value structure: atomic value type: reference description: "Window in which the lineage first appears" - id: f_last_window value structure: atomic value type: reference description: "Window in which the lineage last appears (or dissolves)" description: "Ordered sequences of community instances across time windows, each sequence representing one persistent community" artifact F_transition_events : feature(D_lineages) value structure: vector value type: categorical description: "Per-consecutive-window-pair transition type along each lineage: continuation, merge, split, birth, or dissolution" transform T_arrange_evolution : intent: contextualise manner: "column-band arrangement (StratomeX-style) plus alluvial flow layout (EventThread-style)" input: D_lineages, D_communities, F_overlap, F_transition_events output: A_evolution actor: machine description: "Arrange community instances as columns per time window with connecting bands sized by membership overlap, and as an alluvial diagram highlighting branch/merge/dissolution points" artifact A_evolution : arrangement(D_lineages) context: D_time_windows principle: "horizontal: time windows as columns; vertical: stacked community-instance blocks sized by member count; bands between adjacent columns sized by overlap; alluvial flow shows lineage continuity" description: "Temporal arrangement exposing continuity, merges, splits, births, and dissolutions of communities" transform T_visualise_evolution : intent: visualise manner: "column-band matrix + alluvial diagram" input: A_evolution output: V_evolution actor: machine description: "Render the community-evolution diagram with colour-coded lineages and highlighted transition events" artifact V_evolution : visualisation(A_evolution) layout: "sequence of window columns with connecting overlap bands; alluvial flow overlay" form: "stacked blocks, ribbon bands, branch/merge glyphs" encoding: "block height: community size; band width: overlap strength; block colour: lineage identity; glyph: transition type (merge/split/birth/dissolution)" description: "Primary view for inspecting how communities persist, combine, fragment, or disappear over the 35-year span" transform T_assess_lineages : intent: assess manner: "evaluate structural plausibility of lineages and events" input: V_evolution, F_transition_events output: K_lineage_quality actor: human description: "Judge whether the overlap threshold produces believable lineages, avoiding spurious fragmentation or over-merging" artifact K_lineage_quality : knowledge(D_lineages) representation form: "quality judgment" description: "Assessment of whether current matching threshold yields plausible lineage structure and transition events" if K_lineage_quality indicates refinement needed: then: transform T_adjust_matching_params : intent: generate-knowledge manner: "adjust overlap threshold based on assessment" input: K_lineage_quality, S_matching_params output: S_matching_params_r actor: human description: "Revise the overlap threshold to correct fragmentation or over-merging" artifact S_matching_params_r : specification representation form: "parameter settings" description: "Updated lineage-matching configuration" assign: S_matching_params := S_matching_params_r else: exit loop L_lineage_tracking end loop L_lineage_tracking

transform T_compute_core : intent: characterise manner: "compute-statistics (per-author presence frequency; centrality)" input: D_lineages, D_communities, D_networks, F_community_label output: F_core_members, F_leaders actor: machine description: "For each lineage, compute each author's presence frequency across the lineage's windows to identify a stable core; compute within-community degree/betweenness centrality combined with publication and citation counts to identify leading researchers per window" artifact F_core_members : feature(D_lineages) value structure: list value type: reference description: "Authors present in a majority of the lineage's time windows, forming its stable core" artifact F_leaders : feature(D_communities) value structure: list value type: reference description: "Most central / most cited authors within each community instance" transform T_visualise_stability : intent: visualise manner: "author x time-window presence heatmap per lineage" input: F_core_members, F_leaders, D_lineages output: V_stability actor: machine description: "Display, per lineage, which authors were present in which windows, with core members and leaders highlighted" artifact V_stability : visualisation(F_core_members, F_leaders, D_lineages) layout: "matrix per lineage: rows = authors, columns = time windows" form: "coloured cells with leader markers" encoding: "cell colour: membership presence; marker: leader status; row ordering: total presence count (core members at top)" description: "Reveals which lineages have a persistent core of leading researchers versus high membership turnover" transform T_abstract_stability : intent: abstract manner: "perception-and-interpretation" input: V_stability, F_transition_events output: P_stability_patterns actor: human description: "Identify lineages with strong stable cores versus high turnover, and relate core/leader changes to transition events" artifact P_stability_patterns : pattern(D_lineages) representation form: "descriptions of stability and leadership patterns" description: "Characterisation of each lineage's core stability and leadership continuity or change"

transform T_aggregate_text : intent: characterise manner: "aggregate" input: D_communities, D_papers output: F_text_corpus actor: machine description: "Aggregate titles, abstracts, and keywords of papers authored by each community instance's members within its window" artifact F_text_corpus : feature(D_communities) value structure: atomic value type: text description: "Concatenated title/abstract/keyword text for each community instance's papers" artifact S_topic_params : specification origin: given representation form: "parameter settings" description: "Initial topic-modelling configuration (e.g. number of NMF topics, term weighting)" loop L_topic_modeling: purpose: "Fit and refine a topic model producing interpretable, distinctive research-focus labels for community instances" until: "Topics are coherent, distinctive, and interpretable as named research foci across community instances" body: transform T_fit_topics : intent: build-model manner: "fit-topic-model (NMF)" input: F_text_corpus, S_topic_params output: M_topic_model, F_topic_weights actor: machine description: "Fit a topic model over community-instance corpora and compute each instance's weight distribution across topics" artifact M_topic_model : model(F_text_corpus) model type: "topic model" representation form: "term-topic and community-topic weight matrices" description: "Latent research-theme structure underlying community instances' publications" artifact F_topic_weights : feature(D_communities) value structure: vector value type: numeric description: "Per-community-instance topic weight distribution" transform T_visualise_topics : intent: visualise manner: "term-topic matrix + per-lineage topic-weight timeline" input: M_topic_model, F_topic_weights, D_lineages output: V_topics actor: machine description: "Display characteristic terms per topic and each lineage's topic-weight evolution over its lifespan" artifact V_topics : visualisation(M_topic_model, F_topic_weights) layout: "term-topic matrix; per-lineage stacked/line chart over time" form: "coloured cells (term weight); coloured area/line chart" encoding: "cell colour: term-topic weight; line/area colour: topic identity; height: topic weight within the lineage's window" description: "Reveals dominant research foci per community instance and how a lineage's focus shifts over time" transform T_abstract_foci : intent: abstract manner: "name-patterns" input: V_topics output: P_research_foci actor: human description: "Assign interpretable research-focus labels to topics (e.g. 'text/NLP visualization', 'immersive analytics')" artifact P_research_foci : pattern(D_communities, M_topic_model) representation form: "named research-focus labels per topic and per community instance/lineage" description: "Human-interpretable characterisation of each community's dominant research themes and their evolution" transform T_assess_topics : intent: assess manner: "evaluate topic coherence and interpretability" input: V_topics, P_research_foci output: K_topic_quality actor: human description: "Judge whether topics are distinct and interpretable as research foci" artifact K_topic_quality : knowledge(M_topic_model) representation form: "quality judgment" description: "Assessment of topic coherence, distinctiveness, and interpretability" if K_topic_quality indicates refinement needed: then: transform T_adjust_topic_params : intent: generate-knowledge manner: "adjust number of topics or term weighting" input: K_topic_quality, S_topic_params output: S_topic_params_r actor: human description: "Revise topic-model configuration" artifact S_topic_params_r : specification representation form: "parameter settings" description: "Updated topic-modelling configuration" assign: S_topic_params := S_topic_params_r else: exit loop L_topic_modeling end loop L_topic_modeling

transform T_derive_trajectories : intent: characterise manner: "reconstruct per-author lineage-membership sequences" input: D_authors, F_community_label, D_lineages output: F_author_trajectories actor: machine description: "Reconstruct each author's career-long sequence of lineage memberships, capturing sequential moves between communities and simultaneous membership in more than one" artifact F_author_trajectories : feature(D_authors) value structure: vector value type: {reference, temporal} description: "Per-author ordered sequence of (time window, lineage) membership pairs across their career" transform T_arrange_trajectories : intent: contextualise manner: "relative-time-alignment against lineage timelines" input: F_author_trajectories, D_lineages output: A_trajectories actor: machine description: "Position each author's trajectory against the shared lineage timeline for cross-community mobility comparison" artifact A_trajectories : arrangement(D_authors) context: D_lineages principle: "horizontal: time; vertical: lineage identity; author trajectory drawn as a path across lineage rows" description: "Arrangement exposing which authors move between, or simultaneously belong to, multiple communities" transform T_visualise_trajectories : intent: visualise manner: "trajectory lines over lineage timeline" input: A_trajectories output: V_trajectories actor: machine description: "Display author trajectories as paths across the lineage timeline, highlighting bridging and multi-community authors" artifact V_trajectories : visualisation(A_trajectories) layout: "lineage rows along vertical axis; time along horizontal axis" form: "coloured trajectory lines/paths, one per selected author or author group" encoding: "line colour: author identity or role (bridge/core/peripheral); vertical jumps: community changes" description: "Reveals which researchers bridge multiple communities and how cross-community membership evolves" transform T_abstract_mobility : intent: abstract manner: "perception-and-interpretation" input: V_trajectories output: P_mobility_patterns actor: human description: "Identify bridging authors, simultaneous multi-community membership, and directional flows of researchers between communities" artifact P_mobility_patterns : pattern(D_authors, D_lineages) representation form: "descriptions of mobility and overlap patterns" description: "Characterisation of how individual researchers move between, or belong simultaneously to, multiple communities"

loop L_interpretation: purpose: "Integrate evolution, stability, research-focus, and mobility views to interpret why communities merge, split, or dissolve, revising earlier configuration choices if the picture remains unclear" until: "The analyst has developed a sufficient holistic understanding of community evolution and its drivers" body: transform T_explore_integrated : intent: abstract manner: "cross-view comparison" input: V_evolution, V_stability, V_topics, V_trajectories output: P_integrated_findings actor: human description: "Interpret merge/split/dissolution events in light of co-occurring research-focus shifts, core/leader turnover, and researcher mobility" artifact P_integrated_findings : pattern(D_lineages) representation form: "causal interpretations of transition events" description: "Explanations linking specific merge/split/dissolution events to concurrent topical shifts, leadership changes, or researcher migration" transform T_assess_completeness : intent: assess manner: "judge sufficiency of holistic understanding" input: P_integrated_findings output: K_completeness actor: human description: "Judge whether all major transition events have been satisfactorily interpreted" artifact K_completeness : knowledge(P_integrated_findings) representation form: "quality judgment" description: "Assessment of interpretive completeness" if K_completeness indicates further investigation needed: then: transform T_refine_focus : intent: generate-knowledge manner: "decide which earlier configuration to revisit (window scheme, matching threshold, or topic granularity)" input: K_completeness, S_window_config, S_matching_params output: S_window_config_r actor: human description: "Formulate a revised window or matching configuration to resolve ambiguous transitions" artifact S_window_config_r : specification representation form: "parameter settings" description: "Revised window scheme guiding a further pass through Phases A-G" assign: S_window_config := S_window_config_r else: exit loop L_interpretation end loop L_interpretation

transform T_synthesise : intent: generate-knowledge manner: "formulate-statements" input: P_integrated_findings, P_mobility_patterns, P_research_foci, P_stability_patterns, F_transition_events output: K_findings actor: human description: "Synthesise findings on the number and character of communities, their core stability and leadership, research-focus evolution, cross-community overlap, and the timing and drivers of merges, splits, and dissolutions" artifact K_findings : knowledge(P_integrated_findings, P_research_foci) representation form: "statements and explanations" description: "Documented understanding of the intellectual structure of the IEEE VIS community: number of distinct communities, degree of core stability and leadership continuity per community, characteristic and evolving research foci, patterns of membership overlap and researcher mobility across communities, and identified merge/split/dissolution events with plausible drivers"