VIS · Claude Sonnet 5 · C7

workflow vis-coauthorship-community-evolution template: define-unit (extract authors) → define-unit (network construction: windows & anchor) → loop(define-unit (community-detection) → contextualise (projection) → visualise → assess) → characterise (cross-time overlap) → define-unit (trace threads) → contextualise (alluvial) → visualise → abstract (detect events) → characterise (author role metrics) → define-unit (classify roles) → loop(contextualise (drill-down layouts) → visualise → assess) → characterise (text encoding) → loop(build-model (topic model) → characterise (topic profiles) → visualise → abstract (interpret) → assess) → characterise (aggregate community topic profiles) → visualise (topic evolution) → characterise (period overlap matrix) → visualise (overlap matrix) → generate-knowledge description: "Human-in-the-loop visual analytics workflow for identifying communities of collaborating VIS researchers, tracing their evolution (stability, drift, merges, splits, dissolution), characterising core leadership, and linking communities to their evolving research foci."

artifact D_papers : entities origin: given internal structure: elementary embedment: {set, time} features: - 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: categorical description: "Author/venue keywords" - id: f_authors value structure: list value type: reference description: "Disambiguated author identifiers" - id: f_citations value structure: list value type: reference description: "References to other papers" description: "3596 IEEE VIS (Vis/InfoVis/VAST/SciVis) papers, 1990-2024" artifact K_prior_domain_knowledge : knowledge(D_papers) origin: given representation form: "informal domain expertise" description: "Analyst's prior knowledge of known VIS research groups, leading researchers, institutional moves, and historical events, used throughout to validate detected communities, threads, and topics"

transform T_extract_authors : intent: define-unit manner: "extract distinct entities (disambiguated authors)" input: D_papers output: D_authors actor: machine description: "Extract the set of distinct disambiguated authors referenced across the paper corpus" artifact D_authors : entities internal structure: elementary embedment: {set, time} features: - id: f_active_years value structure: list value type: temporal description: "Years in which the author published at IEEE VIS" description: "Disambiguated VIS authors, 1990-2024"

artifact S_windowing : specification origin: given representation form: "parameter settings" description: "Window length and stride for overlapping time windows (e.g., 5-year windows advancing by 2 years)" transform T_build_windows : intent: define-unit manner: "time-partitioning with overlapping windows; aggregate co-authorship edges per window" input: D_papers, D_authors, S_windowing output: D_networks actor: machine description: "Partition the publication timeline into overlapping windows and build one weighted co-authorship network snapshot per window" artifact D_networks : entities internal structure: formation embedment: time features: - id: f_edge_weight value structure: atomic value type: numeric description: "Number of co-authored papers between two authors within the window" - id: f_window_span value structure: atomic value type: temporal description: "Time span covered by the window" description: "Co-authorship network snapshots, one per overlapping time window" transform T_build_anchor : intent: define-unit manner: "aggregate co-authorship edges across the full period" input: D_papers, D_authors output: D_network_full actor: machine description: "Build a single full-period co-authorship network to serve as a stable long-run reference structure" artifact D_network_full : entities internal structure: formation features: - id: f_edge_weight value structure: atomic value type: numeric description: "Number of co-authored papers between two authors across the full 1990-2024 period" description: "Full-period reference co-authorship network (single entity)"

artifact S_community_params : specification origin: given representation form: "method choice and parameter settings" description: "Initial community-detection algorithm (e.g., Leiden) and resolution parameter, applied identically to anchor and windowed networks" loop L_community_detection: purpose: "Detect communities on the anchor network and on each windowed snapshot at a resolution that yields analytically meaningful, interpretable community structure" until: "Community counts and sizes are neither over-fragmented nor overly coarse, and communities are visually cohesive and consistent with known VIS research groups" body: transform T_detect_anchor_communities : intent: define-unit manner: "cluster-by-similarity (community detection on full-period network)" input: D_network_full, S_community_params output: D_anchor_communities, F_anchor_membership actor: machine description: "Detect long-run reference communities on the full-period network" artifact D_anchor_communities : entities internal structure: group/cluster embedment: set description: "Long-term reference communities detected on the full-period network" artifact F_anchor_membership : feature(D_authors) value structure: atomic value type: categorical description: "Anchor community assignment for each author" transform T_detect_window_communities : intent: define-unit manner: "cluster-by-similarity (community detection per window)" input: D_networks, S_community_params output: D_window_communities, F_community_members actor: machine description: "Detect communities independently within each windowed network snapshot" artifact D_window_communities : entities internal structure: group/cluster embedment: {set, time} description: "Communities detected within each time-window network snapshot" artifact F_community_members : feature(D_window_communities) value structure: list value type: reference description: "Author members belonging to each community instance" transform T_contextualise_projection : intent: contextualise manner: "projection-based (embed community-window instances by member similarity)" input: D_window_communities, F_community_members output: D_projection_space, A_projection_communities actor: machine description: "Compute a 2D projection space in which community-window instances close in membership/time appear near each other, and arrange instances within it" artifact D_projection_space : entities internal structure: elementary features: - id: f_dimensions value structure: atomic value type: numeric description: "Number of projection dimensions (2)" description: "Computed 2D projection space for community-window instances" artifact A_projection_communities : arrangement(D_window_communities) context: D_projection_space principle: "similarity-preserving projection based on member overlap" description: "Positions of community-window instances in projection space" transform T_vis_overview : intent: visualise manner: "2D projection with temporal trails" input: A_projection_communities, D_anchor_communities, F_anchor_membership output: V_community_states actor: machine description: "Show community-window instances in projection space, coloured by anchor community, to assess granularity and cohesion" artifact V_community_states : visualisation(A_projection_communities, F_anchor_membership) layout: "2D projection" form: "coloured points with temporal trail lines" encoding: "position from A_projection_communities; colour from F_anchor_membership; trails connect same-authorship-dominant instances across adjacent windows" description: "Overview for judging whether detected communities are cohesive, distinct, and stable across time" transform T_assess_resolution : intent: assess manner: "evaluate community granularity and cohesion" input: V_community_states, D_window_communities, D_anchor_communities, K_prior_domain_knowledge output: K_resolution_quality actor: human description: "Judge whether the current resolution parameter yields interpretable, stable communities consistent with known VIS research groups" artifact K_resolution_quality : knowledge(D_window_communities, D_anchor_communities) representation form: "quality judgment" description: "Judgment of whether community granularity is adequate or requires adjustment" if K_resolution_quality indicates refinement needed: then: transform T_refine_resolution : intent: generate-knowledge manner: "formulate revised community-detection specification" input: K_resolution_quality, S_community_params output: S_community_params_r actor: human description: "Decide on a revised resolution parameter or algorithm variant" artifact S_community_params_r : specification representation form: "method choice and parameter settings" description: "Revised community-detection configuration" assign: S_community_params := S_community_params_r else: exit loop L_community_detection end loop L_community_detection

transform T_characterise_overlap : intent: characterise manner: "define-relations (membership overlap between temporally adjacent community instances)" input: D_window_communities, F_community_members output: F_community_overlap actor: machine description: "Compute Jaccard-style member overlap between every pair of community instances in temporally adjacent windows" artifact F_community_overlap : feature(D_window_communities) value structure: relational configuration value type: numeric representation form: "weighted directed graph linking community instances in adjacent time windows by member-overlap weight" description: "Overlap-based linkage used to trace community continuity" transform T_trace_threads : intent: define-unit manner: "trace continuity chains across time (merge-groups via overlap linkage)" input: D_window_communities, F_community_overlap output: D_community_threads actor: hybrid description: "Chain community-window instances into long-run trajectories wherever member overlap is strong; propose thread identities for analyst review" artifact D_community_threads : entities internal structure: sequence embedment: set features: - id: f_thread_span value structure: atomic value type: temporal description: "First and last window of the thread" - id: f_thread_size_trend value structure: vector value type: numeric description: "Member count per window along the thread" description: "Long-run community trajectories chained from per-window community instances"

transform T_contextualise_alluvial : intent: contextualise manner: "flow-based layout (alluvial ordering by time and thread)" input: D_community_threads, D_window_communities output: A_alluvial actor: machine description: "Order community instances along a temporal flow layout grouped by thread" artifact A_alluvial : arrangement(D_window_communities) context: D_community_threads principle: "temporal flow ordering; vertical position by thread membership, ribbon width by community size" description: "Layout underlying an alluvial/stream diagram of community evolution" transform T_vis_alluvial : intent: visualise manner: "alluvial/stream diagram" input: A_alluvial, F_community_overlap output: V_alluvial actor: machine description: "Display community size and flow of membership across time, exposing persistence, merges, splits, emergence, and dissolution" artifact V_alluvial : visualisation(A_alluvial, F_community_overlap) layout: "alluvial/stream diagram" form: "flow ribbons with width encoding community size" encoding: "horizontal axis: time windows; ribbons: F_community_overlap linkage strength between adjacent-window communities" description: "Primary overview for answering how many communities exist and how they merge, split, or dissolve over time" transform T_detect_events : intent: abstract manner: "perception-and-interpretation (identify merge/split/emergence/ dissolution events)" input: V_alluvial, F_community_overlap, D_community_threads, K_prior_domain_knowledge output: P_evolution_events actor: human description: "Visually identify and label points where threads merge, split, newly emerge, or dissolve, cross-checked against known VIS community history" artifact P_evolution_events : pattern(D_community_threads) representation form: "labelled structural events (merge, split, emergence, dissolution) with time windows and involved thread IDs" description: "Identified structural transition events in the community evolution"

transform T_characterise_author_roles : intent: characterise manner: "compute tenure, centrality, and cross-membership per author" input: D_authors, D_community_threads, D_networks, F_community_members output: F_author_role_metrics actor: machine description: "For each author and each thread they belong to, compute tenure (fraction of thread windows present), average network centrality within the community, and count of distinct threads/communities joined" artifact F_author_role_metrics : feature(D_authors) value structure: vector value type: numeric description: "Per-author tenure, centrality, and cross-membership metrics" transform T_classify_roles : intent: define-unit manner: "cluster-by-similarity / rule-based classification (core, peripheral, boundary-spanning)" input: D_authors, F_author_role_metrics output: D_author_roles, F_role_label actor: hybrid description: "Classify authors into core (high tenure, high centrality), peripheral (short tenure, low centrality), or boundary-spanning (member of multiple communities) role categories" artifact D_author_roles : entities internal structure: group/cluster embedment: set description: "Groups of authors sharing similar role profiles" artifact F_role_label : feature(D_authors) value structure: atomic value type: categorical description: "Assigned role category per author"

loop L_structure_inspection: purpose: "Inspect the internal structure and leadership of selected community threads to validate thread linkage and role classification" until: "The analyst has examined a representative sample of threads and periods and is satisfied that thread continuity and role labels are consistent with domain knowledge" body: transform T_select_instance : intent: define-unit manner: "filter (analyst selects a thread and time window of interest)" input: D_community_threads, V_alluvial output: D_selected_window_community actor: human description: "Select a single community-window instance for detailed inspection" artifact D_selected_window_community : entities internal structure: group/cluster embedment: set description: "Single community instance selected for detailed inspection" transform T_contextualise_nodetrix : intent: contextualise manner: "hybrid matrix/node-link layout (dense core as matrix, bridging ties as node-link)" input: D_selected_window_community, D_networks, F_role_label output: A_nodetrix actor: machine description: "Arrange the selected community's dense collaboration core as an adjacency matrix while rendering bridging ties to other communities as node-link edges" artifact A_nodetrix : arrangement(D_selected_window_community) context: D_networks principle: "NodeTrix-style layout: densely connected core as matrix, sparser bridging ties as node-link edges" description: "Hybrid layout exposing core/leader structure and boundary-spanning ties" transform T_vis_nodetrix : intent: visualise manner: "node-link + matrix hybrid diagram" input: A_nodetrix, F_role_label, F_author_role_metrics output: V_nodetrix actor: machine description: "Display the selected community's internal structure, coloured by role classification" artifact V_nodetrix : visualisation(A_nodetrix, F_role_label) layout: "hybrid matrix/node-link" form: "adjacency matrix cells for the core, node-link edges for bridging ties" encoding: "matrix/edge presence from A_nodetrix; node colour from F_role_label" description: "Detailed view of a single community's core and boundary-spanning structure" transform T_contextualise_timeline : intent: contextualise manner: "relative time alignment (one track per author)" input: D_authors, D_community_threads, F_community_members, D_networks output: A_author_timeline actor: machine description: "Arrange authors as parallel tracks positioned along the shared time axis defined by the network windows, coloured segments indicating which thread the author belongs to at each window" artifact A_author_timeline : arrangement(D_authors) context: D_networks principle: "one horizontal track per author positioned along time windows" description: "Per-author community-membership timeline arrangement" transform T_vis_timeline : intent: visualise manner: "scarf-plot / aligned timeline diagram" input: A_author_timeline, F_role_label output: V_author_timeline actor: machine description: "Show each author's community membership over time, revealing loyal core members versus movers" artifact V_author_timeline : visualisation(A_author_timeline, F_role_label) layout: "aligned per-author timelines" form: "coloured segments per time window" encoding: "track position: author; colour: community/thread membership per window; opacity: role from F_role_label" description: "Author-level view of membership stability and drift" transform T_assess_structure : intent: assess manner: "judge consistency of thread linkage and role labels with domain knowledge" input: V_nodetrix, V_author_timeline, P_evolution_events, K_prior_domain_knowledge output: K_structure_assessment actor: human description: "Decide whether the selected thread's continuity or role labels need manual correction" artifact K_structure_assessment : knowledge(D_community_threads, F_role_label) representation form: "quality judgment with diagnostic notes" description: "Judgment on whether thread linkage or role classification requires correction for the inspected instance" if K_structure_assessment indicates correction needed: then: transform T_correct_threads : intent: define-unit manner: "manual correction of thread linkage" input: D_community_threads, K_structure_assessment, F_community_overlap output: D_community_threads_r actor: human description: "Manually relink, split, or merge thread segments based on domain knowledge" artifact D_community_threads_r : entities internal structure: sequence embedment: set description: "Corrected community threads" transform T_correct_roles : intent: define-unit manner: "manual correction of role classification" input: D_author_roles, F_role_label, K_structure_assessment output: D_author_roles_r, F_role_label_r actor: human description: "Manually adjust role labels found inconsistent with domain knowledge" artifact D_author_roles_r : entities internal structure: group/cluster embedment: set description: "Corrected author role groups" artifact F_role_label_r : feature(D_authors) value structure: atomic value type: categorical description: "Corrected role labels" assign: D_community_threads := D_community_threads_r D_author_roles := D_author_roles_r F_role_label := F_role_label_r else: # No correction needed for this instance; analyst continues # inspecting further threads/periods, or stops when satisfied. assign: D_community_threads := D_community_threads end loop L_structure_inspection

transform T_characterise_text : intent: characterise manner: "encode (text feature extraction from title/abstract/keywords)" input: D_papers output: F_text_features actor: machine description: "Encode each paper's title, abstract, and keywords as a numeric text representation" artifact F_text_features : feature(D_papers) value structure: vector value type: numeric representation form: "TF-IDF or embedding vector per paper" description: "Numeric text representation of each paper" artifact S_topic_params : specification origin: given representation form: "parameter settings" description: "Initial number of topics and NMF configuration" loop L_topic_modelling: purpose: "Fit and refine a topic model whose topics are interpretable as recognisable VIS research foci" until: "Topics are coherent, mutually distinct, and readily nameable by the analyst" body: transform T_fit_topics : intent: build-model manner: "fit-topic-model (NMF)" input: F_text_features, S_topic_params output: M_topic_model actor: machine description: "Fit a non-negative matrix factorisation topic model over the paper corpus" artifact M_topic_model : model(F_text_features) model type: "topic model" representation form: "non-negative matrix factorisation (W,H)" description: "Topic model over the VIS paper corpus" transform T_compute_paper_topics : intent: characterise manner: "project (compute per-paper topic-mixture from model)" input: D_papers, M_topic_model output: F_paper_topic_mixture actor: machine description: "Compute each paper's topic-mixture weights" artifact F_paper_topic_mixture : feature(D_papers) value structure: vector value type: numeric description: "Topic-mixture weights per paper" transform T_vis_topics : intent: visualise manner: "ranked term lists / topic-word matrix" input: M_topic_model, F_paper_topic_mixture output: V_topics actor: machine description: "Display top terms and representative papers per topic" artifact V_topics : visualisation(M_topic_model, F_paper_topic_mixture) layout: "topic-word matrix with representative-paper lists" form: "ranked term lists, bar charts" encoding: "topic ranked by weight from M_topic_model; representative papers ranked by F_paper_topic_mixture" description: "View for judging topic coherence and interpretability" transform T_interpret_topics : intent: abstract manner: "name-patterns (assign labels to topics)" input: V_topics output: P_topics actor: human description: "Interpret and name topics based on top terms and representative papers" artifact P_topics : pattern(M_topic_model) representation form: "named topic descriptions (label + representative terms/papers)" description: "Interpreted research-focus topics" transform T_assess_topics : intent: assess manner: "judge topic coherence and distinctiveness" input: V_topics, P_topics output: K_topic_quality actor: human description: "Decide whether the topic model needs refinement (merge, split, or change topic count)" artifact K_topic_quality : knowledge(M_topic_model, P_topics) representation form: "quality judgment" description: "Judgment on topic coherence and whether refinement is needed" if K_topic_quality indicates refinement needed: then: transform T_refine_topic_params : intent: generate-knowledge manner: "formulate revised topic-model specification" input: K_topic_quality, S_topic_params output: S_topic_params_r actor: human description: "Decide on a revised topic count or model configuration" artifact S_topic_params_r : specification representation form: "parameter settings" description: "Revised topic-model configuration" assign: S_topic_params := S_topic_params_r else: exit loop L_topic_modelling end loop L_topic_modelling

transform T_aggregate_community_topics : intent: characterise manner: "aggregate (average paper topic-mixtures per community instance)" input: D_window_communities, F_community_members, F_paper_topic_mixture output: F_community_topic_profile actor: machine description: "Aggregate the topic-mixtures of papers authored within each community instance into a single topic profile" artifact F_community_topic_profile : feature(D_window_communities) value structure: vector value type: numeric description: "Aggregated topic-mixture profile per community instance" transform T_contextualise_topic_matrix : intent: contextualise manner: "grid-based (community threads x time windows)" input: D_community_threads, D_networks output: A_topic_matrix actor: machine description: "Arrange community threads and time windows into a matrix grid for small-multiples display of topic evolution" artifact A_topic_matrix : arrangement(D_community_threads) context: D_networks principle: "rows = community threads; columns = time windows" description: "Grid layout for topic-evolution small multiples" transform T_vis_topic_evolution : intent: visualise manner: "matrix/small-multiples plot of topic profiles" input: A_topic_matrix, F_community_topic_profile, P_topics output: V_topic_evolution actor: machine description: "Show each community thread's topic profile per time window as small multiples, labelled with named topics" artifact V_topic_evolution : visualisation(A_topic_matrix, F_community_topic_profile, P_topics) layout: "matrix grid (threads x windows)" form: "small-multiple stacked bar/area charts" encoding: "cell position from A_topic_matrix; bar segments from F_community_topic_profile; segment labels from P_topics" description: "Primary view for answering what each community's research foci are and how they evolve"

transform T_characterise_period_overlap : intent: characterise manner: "define-relations (pairwise membership overlap within the same time window)" input: D_window_communities, F_community_members output: F_period_overlap_matrix actor: machine description: "Compute pairwise Jaccard overlap among communities coexisting within the same time window" artifact F_period_overlap_matrix : feature(D_window_communities) value structure: matrix value type: numeric representation form: "pairwise overlap matrix among co-existing communities per window" description: "Within-period membership overlap between communities" transform T_vis_overlap_matrix : intent: visualise manner: "matrix-plot" input: F_period_overlap_matrix output: V_overlap_matrix actor: machine description: "Display the within-period community overlap matrix, faceted by time window" artifact V_overlap_matrix : visualisation(F_period_overlap_matrix) layout: "matrix grid, one facet per time window" form: "coloured matrix cells" encoding: "cell colour from F_period_overlap_matrix overlap value" description: "View for identifying converging, diverging, or near-duplicate communities within a period" transform T_assess_overlap : intent: assess manner: "identify candidate merges or eroding distinctiveness" input: V_overlap_matrix, K_prior_domain_knowledge output: K_overlap_assessment actor: human description: "Flag community pairs with persistently high overlap as candidates for merger or loss of distinct identity" artifact K_overlap_assessment : knowledge(F_period_overlap_matrix) representation form: "quality judgment with flagged pairs" description: "Judgment identifying community pairs at risk of merging or losing distinctiveness"

transform T_synthesise : intent: generate-knowledge manner: "formulate-statements" input: P_evolution_events, F_author_role_metrics, F_role_label, P_topics, V_topic_evolution, K_overlap_assessment, K_structure_assessment output: K_findings actor: human description: "Synthesise the analyst's answers regarding the number and stability of communities, their core leadership, their research foci and evolution, cross-community membership overlap, and merge/split/ dissolution events" artifact K_findings : knowledge(D_community_threads, P_topics) representation form: "statements and insights" description: "Consolidated findings on the intellectual structure and evolution of the IEEE VIS collaboration community"