Multi-View Genealogy Analysis

• Multi-view genealogy analysis is a computational framework that integrates multiple perspectives—including structural, temporal, community, and attribute views—to explore complex lineage relationships.
• It employs formal graph models, graph traversal algorithms, and statistical metrics to quantify ancestry, citation networks, and phylogenomic discordance in diverse datasets.
• Practical implementations in academic, phylogenetic, and citation analyses reveal actionable insights on lineage structures, temporal dynamics, and community influence, highlighting scalability challenges.

Multi-view genealogy analysis encompasses computational frameworks and visualization strategies designed to explore, quantify, and interpret ancestry, descent, and relationships in complex genealogical systems from multiple, coordinated analytical perspectives. This field operates at the intersection of graph algorithms, statistical inference, data visualization, and domain-specific metrics, supporting research areas such as academic lineage mapping, phylogenetic inference, and citation network analysis. By integrating multiple views—structural, temporal, community, attribute-based, and discordance-focused—multi-view genealogy analysis enables rigorous characterization of lineage structure, knowledge transmission, and the dynamics of influence or evolutionary history within populations of entities, whether researchers, species, or authors.

1. Formal Graph Models in Genealogy Analysis

Genealogies are formally modeled as directed acyclic graphs (DAGs), where each node corresponds to an entity (e.g., researcher, species, or author), and each edge encodes a directed ancestry or mentorship relationship, such as advisor→advisee or ancestor→descendant (Cota et al., 2021, Anil et al., 2018). For example, in academic genealogy, the graph $G = (V, E)$ consists of vertices $V$—the set of all PhD-holding researchers or authors—and edges $E \subseteq V \times V$ representing direct advisory relationships. Constraints such as acyclicity (no cycles) ensure the lineage structure is well-defined.

In phylogenomic genealogy, $T = (V, E)$ represents the species tree, with each edge $e$ annotated with coalescent time and mutation parameters, capturing evolutionary distances and branching history (Dasarathy et al., 2014). In citation genealogy analysis, the model is extended to include a citation matrix $A \in \mathbb{N}^{n \times n}$, where $A[i, j]$ records the number of times entity $j$ cites entity $i$, superimposing influence or knowledge flows atop the structural lineage (Anil et al., 2018).

2. Multi-View Analysis Architecture

Multi-view genealogy analysis systems deliver coordinated perspectives on geneaological data. Principal view types include:

• Structural (Lineage) View: Visualizes the genealogy tree or DAG, focusing exclusively on ancestry, mentorship, or evolutionary relationships. Standard layouts include hierarchical ("Sugiyama"), radial, or depth-limited graphical trees (Cota et al., 2021, Anil et al., 2018). This view supports metrics such as tree depth $depth(v)$, branching factor $b(v)$, width, and per-node summary statistics.
• Temporal View: Addresses the chronology of key events (e.g., thesis defenses, speciation, or citation accumulation). Charts plot, for instance, the quantity of supervised theses per year or the proliferation of gene tree splits over time (Cota et al., 2021).
• Community/Citation View: Emphasizes influence, collaborations, and citation clusters. Force-directed layouts render citation edges and highlight communities detected via modularity algorithms (e.g., Louvain), or based on unusually dense intra-lineage citations ("copious citation pairs") (Anil et al., 2018).
• Combined (Super-graph) View: Integrates both genealogical and citation or influence edges, using styles or colors to differentiate edge types. Node encodings can map to custom metrics, such as the ratio of non-genealogical to total citations (Anil et al., 2018).
• Discordance/Attribute View: In phylogenetic contexts, dedicated views expose disagreement ("discordance") across genealogical reconstructions, e.g., alternative gene tree topologies, support values, or quartet frequencies (Sayyari et al., 2017). Attribute panels permit filtering on metadata (field, time span, publication count) and geolocation.

Such multi-view clients are implemented with coordinated interaction—highlighting, synchronized filtering, and drill-down functionality—using client-side frameworks (ReactJS, vis.js, D3.js) and graph DBs (MariaDB, Neo4j) (Cota et al., 2021, Anil et al., 2018).

3. Algorithms and Metrics for Genealogy Quantification

Genealogy analysis leverages both combinatorial graph algorithms and statistical metrics. Key computational steps include:

• Graph Traversals for depth, width, and neighborhood extraction via breadth-first search, local block matrices, or iterative adjacency operations (Cota et al., 2021, Anil et al., 2018).
• Citation-Based Metrics: Define for each node $i$:
  • Total citations: $Y_i = \sum_{j} A[i, j]$
  • Genealogical citations: $GC_i = \sum_{j \in S_i} A[i, j]$, where $S_i$ is a k-hop neighborhood of genealogical relatives.
  • Non-genealogical citations: $NGC_i = Y_i - GC_i$
  • Lineage-independence: $\rho_i = GC_i / Y_i$ (low $\rho_i$ = high independence)
  • Composite lineage scores: $$ALS_i = \alpha |Descendants_i| + \beta |Ancestors_i| + \gamma (NGC_i / Y_i)$$ (Anil et al., 2018).
• Phylogenomic Metrics: For gene-tree discordance,
  • Empirical Hamming distances $\hat p_{AB}$ and log-corrected distances $$\hat d_{AB} = -\frac{3}{4} \ln(1-\frac{4}{3}\hat p_{AB})$$ for METAL (Dasarathy et al., 2014).
  • Concordance factors for focal splits: $$CF(s) = |\{i : s \in g_i\}| / |\{i : g_i \text{ informative for } s\}|$$ (Sayyari et al., 2017).
  • Quartet-support frequencies $\hat p_{b}, \hat q_{b1}, \hat q_{b2}$ for analyzing support and alternative topologies near the focal branch (Sayyari et al., 2017).
• Community Detection: Detects clusters with intense citation or co-citation within genealogical neighborhoods, based on adjustable thresholds $\rho$, $\delta$, $k$ (Anil et al., 2018).

4. Practical Implementations and Analytical Workflows

End-to-end system construction involves:

1. Data Acquisition and Normalization: Scientific genealogy platforms crawl and parse digital records (XML for Lattes, CSV/JSON for author–advisor lists), normalize names and institutions, and perform manual or algorithmic disambiguation (Cota et al., 2021, Anil et al., 2018).
2. Graph Construction: Advisors and advisees are linked into a DAG, with auxiliary matrices for citations or attributes; missing data and consistency are managed via custom rules.
3. Metric Calculation: Structural metrics are calculated on demand (SQL traversals or in-memory BFS). Advanced systems compute block matrices for efficient neighborhood definitions and scalable metric pre-computation (Anil et al., 2018).
4. Interaction and Visualization: Dynamic visual interfaces allow exploration of structures, time lines, and influence communities, with drill-down and filtering on k-hop depth, attribute presence, or lineage-dependence metrics (Cota et al., 2021, Anil et al., 2018).
5. Offline and Export Analytics: Users can export raw metrics and filtered subgraphs for further analysis or reproducibility (Cota et al., 2021).

5. Case Studies and Empirical Findings

• Academic Genealogy in Brazil (Science Tree):
  • Median tree depth in Genetics: ≈7; in Social Sciences: ≈4.
  • 60% of academic lineage edges originate in five states (SP, MG, RJ, RS, PR), matching research program density.
  • Temporal bursts in doctorate supervisions coincide with post-reform expansions in the 1970s and 1990s.
  • <5% of senior advisors account for 20% of advisees; lineage width distributions are fat-tailed (Cota et al., 2021).
• Citation Genealogy Analysis:
  • Citations and influence often cluster within close genealogical neighborhoods; lineage-independence ratios $\rho_i$ can distinguish researchers with broad versus insular impact.
  • Copious citation pairs and communities are detectable via block-matrix neighborhoods and thresholding, flagging possible mutually reinforcing citation practices (Anil et al., 2018).
• Gene Tree Discordance in Phylogenomics:
  • DiscoVista analyses reveal that for key branches, concordance factors can be low (<0.2), and quartet support often matches MSC model null expectations, indicating very short internal branches or pervasive discordance.
  • Discordance is localized; after contracting low-support branches, compatibility among gene trees rises substantially.
  • Patterns of occupancy and GC-content highlight data filtering or systematic bias risks (Sayyari et al., 2017).

6. Limitations and Prospects

Current genealogy analysis systems exhibit several limitations:

• Many platforms restrict analysis to local (per-node) structural metrics; global centrality, modularity, or advanced community detection require back-end extensions (e.g., integration with NetworkX or modularity libraries) (Cota et al., 2021).
• Citation-community detection is sensitive to neighborhood definitions and threshold selection; interpretation of lineage-dependence is context-specific (Anil et al., 2018).
• Visualizations such as DiscoVista require careful a priori selection of focal splits, and are limited by gene-tree estimation errors and support threshold calibration (Sayyari et al., 2017).
• Planned extensions include attribute-rich filtering (publication counts, h-index, custom cohorts), geospatial visualization, and more scalable real-time analytics (Cota et al., 2021).

A plausible implication is that, as dataset scale and heterogeneity increase, robust, interpretable, and multi-view genealogy analysis will continue to demand scalable algorithms, rigorous normalization, and careful interface and visualization design to ensure scientific validity and reproducibility.