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." # ============================================================================ # INPUT # ============================================================================ 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" # ============================================================================ # STEP 1: EXTRACT AUTHOR ENTITIES # ============================================================================ 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" # ============================================================================ # STEP 2: CONSTRUCT NETWORK SNAPSHOTS AND FULL-PERIOD ANCHOR NETWORK # ============================================================================ 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)" # ============================================================================ # STEP 3: COMMUNITY-DETECTION RESOLUTION LOOP # ============================================================================ 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 # ============================================================================ # STEP 4: CROSS-TIME MATCHING AND COMMUNITY THREADS # ============================================================================ 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" # ============================================================================ # STEP 5: VISUALISE EVOLUTION AND DETECT STRUCTURAL EVENTS # ============================================================================ 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" # ============================================================================ # STEP 6: CORE STABILITY AND LEADERSHIP CHARACTERISATION # ============================================================================ 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" # ============================================================================ # STEP 7: STRUCTURAL DRILL-DOWN AND CORRECTION LOOP # ============================================================================ 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 # ============================================================================ # STEP 8: RESEARCH-FOCUS EXTRACTION VIA TOPIC MODELLING # ============================================================================ 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 # ============================================================================ # STEP 9: COMMUNITY RESEARCH-FOCUS PROFILES AND THEIR EVOLUTION # ============================================================================ 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" # ============================================================================ # STEP 10: CROSS-COMMUNITY MEMBERSHIP OVERLAP # ============================================================================ 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" # ============================================================================ # STEP 11: SYNTHESISE FINDINGS # ============================================================================ 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"