{"id":19,"date":"2025-07-28T19:40:14","date_gmt":"2025-07-28T19:40:14","guid":{"rendered":"http:\/\/vargas-solar.com\/intergraphia\/?page_id=19"},"modified":"2025-08-23T19:24:12","modified_gmt":"2025-08-23T19:24:12","slug":"glosary","status":"publish","type":"page","link":"http:\/\/vargas-solar.com\/intergraphia\/glosary\/","title":{"rendered":"GLOSARY"},"content":{"rendered":"\n<ul class=\"wp-block-list\">\n<li><strong>Cultural analytics<\/strong>\u00a0\u2014 Using data science to study cultural production, circulation, and reception (texts, images, media, institutions).<br><em>Example:<\/em>\u00a0Measuring how Latin American women novelists are cited across decades and languages.<\/li>\n\n\n\n<li><strong>Property\/knowledge graph<\/strong>\u00a0\u2014 A graph where nodes\/edges have attributes (property graph) and often typed relations consistent with a schema\/ontology (knowledge graph).<br><em>Example:<\/em>\u00a0Nodes = authors with attributes (gender, region); edges = \u201ccites\u201d, \u201ctranslated_to\u201d.<\/li>\n\n\n\n<li><strong>Node \/ Edge \/ Attribute<\/strong>\u00a0\u2014 Nodes are entities (author, film, museum); edges are relations (citation, collaboration); attributes add context (year, genre, language).<br><em>Example:<\/em>\u00a0A \u201cBorges \u2192 Kafka\u201d edge with\u00a0<code>relation=\"cites\"<\/code>\u00a0and\u00a0<code>year=1954<\/code>.<\/li>\n\n\n\n<li><strong>Bipartite graph<\/strong>\u00a0\u2014 Two types of nodes with edges only across types (e.g., people\u2194films).<br><em>Example:<\/em>\u00a0People\u2013film participation; later\u00a0<strong>project<\/strong>\u00a0to a people\u2194people graph weighted by shared films.<\/li>\n\n\n\n<li><strong>Multiplex \/ multilayer graph<\/strong>\u00a0\u2014 Multiple relation types or layers on the same set of nodes.<br><em>Example:<\/em>\u00a0Author graph with layers for \u201ccites\u201d, \u201ccoauthors\u201d, and \u201cmentions\u201d, analyzed together.<\/li>\n\n\n\n<li><strong>Projection (of bipartite graphs)<\/strong>\u00a0\u2014 Building a one-mode graph from a two-mode graph by connecting nodes that co-occur.<br><em>Example:<\/em>\u00a0Two artists connected if they co-exhibited in at least one show; edge weight = number of shared shows.<\/li>\n<\/ul>\n\n\n\n<h1 class=\"wp-block-heading\">Network structure &amp; metrics<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Degree \/ Weighted degree<\/strong>\u00a0\u2014 Number of ties (or sum of weights) a node has; a proxy for activity\/visibility.<br><em>Example:<\/em>\u00a0Which director has the most repeated collaborations in a decade?<\/li>\n\n\n\n<li><strong>PageRank \/ Eigenvector centrality<\/strong>\u00a0\u2014 Influence via connections to influential neighbors; good for \u201ccanon\u201d detection.<br><em>Example:<\/em>\u00a0Ranking philosophers by incoming citations from already central philosophers.<\/li>\n\n\n\n<li><strong>Betweenness centrality<\/strong>\u00a0\u2014 Fraction of shortest paths that pass through a node; highlights bridges\/gatekeepers.<br><em>Example:<\/em>\u00a0Identifying the curator who links otherwise separate museum circuits.<\/li>\n\n\n\n<li><strong>Closeness centrality<\/strong>\u00a0\u2014 How close a node is, on average, to all others (reachability\/efficiency).<br><em>Example:<\/em>\u00a0Which festival gives an artist the shortest path to most other festivals?<\/li>\n\n\n\n<li><strong>Clustering coefficient<\/strong>\u00a0\u2014 How inter-connected a node\u2019s neighbors are (local cohesion).<br><em>Example:<\/em>\u00a0Tight artistic scenes vs. loosely connected collectives.<\/li>\n\n\n\n<li><strong>Burt\u2019s structural holes \/ Constraint<\/strong>\u00a0\u2014 Low constraint = brokerage power across otherwise disconnected groups.<br><em>Example:<\/em>\u00a0A translator connecting minoritized authors to mainstream publishers.<\/li>\n\n\n\n<li><strong>Community detection (Louvain\/Leiden)<\/strong>\u00a0\u2014 Partitioning into densely connected groups (subcultures, schools, circuits).<br><em>Example:<\/em>\u00a0Distinct subgenres in a sampling\/influence network in hip-hop.<\/li>\n\n\n\n<li><strong>Assortativity \/ Homophily<\/strong>\u00a0\u2014 Do ties cluster by attributes (gender, region, language)? +1 segregated; 0 mixed; \u22121 cross-mixing.<br><em>Example:<\/em>\u00a0Are women screenwriters collaborating mostly with women?<\/li>\n\n\n\n<li><strong>Exposure \/ Neighborhood composition<\/strong>\u00a0\u2014 Share of neighbors with a given attribute (who \u201csees\u201d whom).<br><em>Example:<\/em>\u00a0Average % of non-EU artists in a museum\u2019s co-exhibition neighborhood.<\/li>\n\n\n\n<li><strong>Centrality mass by group<\/strong>\u00a0\u2014 Share of total centrality a group holds (fairness diagnostic).<br><em>Example:<\/em>\u00a0% of PageRank held by Global South journals in a citation graph.<\/li>\n\n\n\n<li><strong>Motifs \/ k-core \/ articulation points<\/strong>\u00a0\u2014 Small recurring subgraphs; nested cores of cohesion; nodes whose removal disconnects the graph.<br><em>Example:<\/em>\u00a0Triadic motifs in film crews; articulation festivals whose removal splits the touring circuit.<\/li>\n<\/ul>\n\n\n\n<h1 class=\"wp-block-heading\">Dynamics &amp; inference<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Temporal (dynamic) networks<\/strong>\u00a0\u2014 Time-sliced graphs to track change.<br><em>Example:<\/em>\u00a0How gender assortativity in film collaborations changes every three years.<\/li>\n\n\n\n<li><strong>Diffusion \/ Cascades<\/strong>\u00a0\u2014 Tracing how memes, styles, or ideas spread over time.<br><em>Example:<\/em>\u00a0Pathways by which a dance trend crosses language communities on TikTok.<\/li>\n\n\n\n<li><strong>Link prediction<\/strong>\u00a0\u2014 Estimating likely future edges from topology\/embeddings.<br><em>Example:<\/em>\u00a0Predicting future co-exhibitions between artists.<\/li>\n\n\n\n<li><strong>Influence maximization \/ backbone extraction<\/strong>\u00a0\u2014 Choosing seed nodes to maximize spread; pruning to the most informative edges.<br><em>Example:<\/em>\u00a0Selecting a few festivals to maximize international exposure for emerging artists.<\/li>\n<\/ul>\n\n\n\n<h1 class=\"wp-block-heading\">Content &amp; graph hybrids<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Embedding-based similarity graph<\/strong>\u00a0\u2014 Connect items by semantic similarity (text\/image\/audio embeddings).<br><em>Example:<\/em>\u00a0A book-to-book similarity network that you cluster and then audit for diversity.<\/li>\n\n\n\n<li><strong>Fairness dashboard (graph)<\/strong>\u00a0\u2014 A compact set of equity metrics over time: assortativity, exposure, centrality mass, reciprocity.<br><em>Example:<\/em>\u00a0Quarterly monitoring of a museum\u2019s acquisition and exhibition networks.<\/li>\n<\/ul>\n\n\n\n<h1 class=\"wp-block-heading\">Ethics &amp; critical frameworks (with graph-friendly operationalizations)<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Epistemic violence<\/strong>\u00a0\u2014 Harm enacted when knowledge from certain communities is excluded, misrepresented, or structurally discounted in canons and platforms.<br><em>Graph example:<\/em>\u00a0In a Wikipedia biography network, a high asymmetry of incoming vs. outgoing links for women scholars (low visibility inlinks, many outlinks to male hubs) plus low reciprocity and peripheral positioning indicates epistemic violence.<\/li>\n\n\n\n<li><strong>Decoloniality<\/strong>\u00a0\u2014 A project to delink knowledge, methods, and institutions from colonial power structures and to center local, plural epistemologies.<br><em>Graph example:<\/em>\u00a0Building region-aware, language-aware knowledge graphs where Latin American or African intellectual networks are analyzed on their own terms (not only via links to Euro-American hubs), and reporting metrics stratified by region\/language with corrective sampling.<\/li>\n\n\n\n<li><strong>Data feminism<\/strong>\u00a0\u2014 Principles for making data work more equitable by examining power, context, and whose interests are served or harmed.<br><em>Graph example:<\/em>\u00a0Publishing a fairness dashboard with group-wise centrality mass, exposure, and assortativity; including participatory interpretation with affected communities.<\/li>\n\n\n\n<li><strong>Feminist AI<\/strong>\u00a0\u2014 Designing AI systems that center care, accountability, and justice, resisting bias and amplifying marginalized perspectives.<br><em>Graph example:<\/em>\u00a0A recommendation system on a cultural similarity graph that optimizes for relevance\u00a0<em>and<\/em>diversity\/exposure of underrepresented creators, with transparent knobs and impact audits.<\/li>\n\n\n\n<li><strong>Data sovereignty<\/strong>\u00a0\u2014 The right of people\/communities (often Indigenous\/minoritized) to control data about them\u2014how it\u2019s collected, used, and shared.<br><em>Graph example:<\/em>\u00a0A museum collaboration graph where contributors consent to inclusion, can set sharing bounds on node attributes (e.g., community affiliation), and where analyses respect governance protocols (e.g., opt-out removes nodes\/attributes from all projections).<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Mini \u201chow you\u2019d measure it\u201d examples<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Under-exposure of a group<\/strong>: compute group\u2019s share of PageRank vs. share of nodes; large gap \u21d2 under-\/over-representation.<\/li>\n\n\n\n<li><strong>Segregation<\/strong>: attribute assortativity by gender\/region over time; decreasing trend \u21d2 improving mixing.<\/li>\n\n\n\n<li><strong>Hidden bridges<\/strong>: flag nodes with high betweenness but low in-degree\/PageRank (bridges with little visibility).<\/li>\n\n\n\n<li><strong>Epistemic violence proxy (one operationalization)<\/strong>:<br>EVI\u2248EVI\u2248\u00a0(centrality gap index) + (1 \u2212 reciprocity) + (peripherality score) + (exposure deficit) \u2014 normalized per group\/time slice.<br><em>Use<\/em>: compare EVI across regions\/genders and track change after policy shifts.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Network structure &amp; metrics Dynamics &amp; inference Content &amp; graph hybrids Ethics &amp; critical frameworks (with graph-friendly operationalizations) Mini &ldquo;how you&rsquo;d measure it&rdquo; examples<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"class_list":["post-19","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/pages\/19","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/comments?post=19"}],"version-history":[{"count":3,"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/pages\/19\/revisions"}],"predecessor-version":[{"id":57,"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/pages\/19\/revisions\/57"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/intergraphia\/wp-json\/wp\/v2\/media?parent=19"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}