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Julian D. Allagan

Publications and source records attributed to Julian D. Allagan.

2 recordsLinked to original sources

Discounted Hitting Domination on Graphs with Submodularity, Complexity and Exact Algorithms

On a network with a fixed set of verified sources, discounted averaging induces an equilibrium support $h_i^S=\mathbb{E}_i[λ^{T_S}]$, the discounted probability that a random walk reaches $S$ before attenuation. We define the \emph{discounted hitting domination number} $δ_{λ,τ}(G)$ as the minimum number of sources required to guarantee $h_i^S\geτ$ at every vertex. Although this potential is known through penalized and group hitting probabilities, the associated minimum-cardinality uniform-coverage problem appears to be new. Aggregate support is monotone submodular, while the uniform-floor problem is an exact submodular-cover problem. Moreover, if $λ^{r+1}<τ\le\left(\fracλΔ\right)^r$, then $δ_{λ,τ}(G)$ equals the distance-$r$ domination number. This yields NP-completeness and APX-completeness at $(λ,τ)=(1/4,1/14)$ on graphs of maximum degree three. For spiders, we obtain an exact finite-state characterization and a polynomial-time algorithm for every fixed rational pair $(λ,τ)$, and show that the branching vertex need not belong to a minimum source set. Finally, an exact mixed-integer linear formulation certifies optimal placements on a real network and a synthetic graph and demonstrates substantial differences from degree, closeness, and classical domination.

math.CO↗

Multi-Method Analysis of Mathematics Placement Assessments: Classical, Machine Learning, and Clustering Approaches

This study evaluates a 40-item mathematics placement examination administered to 198 students using a multi-method framework combining Classical Test Theory, machine learning, and unsupervised clustering. Classical Test Theory analysis reveals that 55\% of items achieve excellent discrimination ($D \geq 0.40$) while 30\% demonstrate poor discrimination ($D < 0.20$) requiring replacement. Question 6 (Graph Interpretation) emerges as the examination's most powerful discriminator, achieving perfect discrimination ($D = 1.000$), highest ANOVA F-statistic ($F = 4609.1$), and maximum Random Forest feature importance (0.206), accounting for 20.6\% of predictive power. Machine learning algorithms demonstrate exceptional performance, with Random Forest and Gradient Boosting achieving 97.5\% and 96.0\% cross-validation accuracy. K-means clustering identifies a natural binary competency structure with a boundary at 42.5\%, diverging from the institutional threshold of 55\% and suggesting potential overclassification into remedial categories. The two-cluster solution exhibits exceptional stability (bootstrap ARI = 0.855) with perfect lower-cluster purity. Convergent evidence across methods supports specific refinements: replace poorly discriminating items, implement a two-stage assessment, and integrate Random Forest predictions with transparency mechanisms. These findings demonstrate that multi-method integration provides a robust empirical foundation for evidence-based mathematics placement optimization.

cs.LG↗