Moses Boudourides

Moses Boudourides

We study missing-link inference in partially observed networks by systematically comparing dyadic link prediction (LP) with hyperlink prediction (HP) and an estimation-based ERGM comparator. LP serves as the primary baseline, using classical heuristics computed on the observed graph. HP extends this framework by scoring candidate higher-order structures (cliques) via lifted dyadic scores and via the CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE). All methods are evaluated under a common masking protocol that removes dyadic evidence induced by held-out hyperlinks to ensure comparability. Across public email and covert-network datasets, LP remains strong for dyadic recovery, while HP -- particularly CHESHIRE -- provides gains when the inferential target is higher-order group structure. ERGMs offer an interpretable dependence-based complement through conditional tie probabilities. The contribution is a comparative, reproducible evaluation clarifying when LP, HP, and ERGM estimation are most appropriate under network missingness.

Moses Boudourides

This paper introduces a novel, multi-source framework for the relational validation of Large Language Models (LLMs). While existing benchmarks have demonstrated LLMs' proficiency at factual recall, their ability to understand and reproduce the intricate web of relationships that defines a domain's conceptual structure remains largely unexplored. Our three-layer analytical framework provides a scalable and robust methodology for assessing the depth of an LLM's knowledge across diverse academic domains. By comparing LLM-generated knowledge graphs to expert-curated encyclopedias, we reveal a consistent and significant ``relational deficit'': LLMs recognize domain-specific concepts but consistently fail to reproduce their relational structure. Our findings highlight the need for more sophisticated evaluation metrics that go beyond simple accuracy and assess the relational integrity of an LLM's knowledge. We demonstrate that this deficit is highly domain-dependent, with performance varying significantly across ten specialized encyclopedias spanning sociology, political science, philosophy, and other fields. The cases of complete relational failure in the most specialized domains are particularly revealing, suggesting that the LLM's internal knowledge representation is not aligned with the conceptual structures of these fields. This has significant implications for the deployment of LLMs in high-stakes applications that require a deep, nuanced understanding of domain-specific knowledge.

Rahulrajan Karthikeyan, Moses Boudourides

Contemporary debates in AI ethics increasingly foreground the prospective moral status of artificial intelligence and the possibility of extending moral or legal rights to artificial agents. While such discussions raise substantive philosophical questions, they often proceed alongside a comparatively limited engagement with the empirically documented harms generated by algorithmic systems already embedded within social, legal, and economic institutions. We conceptualize this asymmetry as an algorithmic blind spot: a discursive-structural pattern in which disproportionate ethical investment in speculative future artificial agents marginalizes empirically documented and asymmetrically distributed harms affecting human populations. The paper analyzes prominent strands of the robot rights literature and juxtaposes them with empirical evidence of algorithmic bias and harm across domains including employment, criminal justice, surveillance, and facial recognition. It demonstrates how ethical preoccupation with hypothetical future entities can obscure existing injustices, diffuse responsibility, and impede mechanisms of accountability and redress. Without rejecting philosophical inquiry into the moral status of artificial systems, the paper instead emphasizes the importance of ethical prioritization and temporal ordering within AI ethics. Addressing the algorithmic blind spot, we argue, requires re-centering ethical evaluation on human impacts, institutional responsibility, and the governance of algorithmic systems currently in operation. In doing so, the paper introduces a conceptual framework for critically assessing ethical discourse in AI and underscores the need to align ethical reflection more closely with its immediate social consequences.

Moses Boudourides

We introduce and analyze a discrete-time network process in which each node holds a (weak) preference ordering over a finite set of alternatives and updates by local Borda aggregation. At each step, a node forms a weighted average (row-stochastic random-walk normalization) of its neighbors' Borda score vectors and projects the aggregated score back to a weak order. Updates are bounded: in each round, a node advances by at most one step along a shortest path in the fixed graph of preference orderings, following the direction prescribed by its neighbors' Borda-aggregated preferences. Our emphasis is dynamical: we develop sufficient conditions, stated directly in terms of graph topology, weights, and the bounded step rule, for (i) self-sustained oscillations in the absence of persistent sources, and (ii) forced oscillations under contrarian persistent camps. We also record robustness (structural stability) away from score-tie hyperplanes and contrast synchronous (Variant S) and asynchronous (Variant A) updating.

Moses Boudourides

Two-paths (wedges) are the elementary combinatorial objects behind clustering, triadic closure, redundancy, and brokerage. Motivated by a two-path formalism that links Burt's structural holes to node-centered ego networks, we develop an operator viewpoint in which wedge incidence induces a canonical ``two-walk'' matrix and a unique decomposition into an edge--supported (triadic) part and a nonedge-supported (open) part. We then study quotient/contraction constructions designed to compress collections of dominating ego networks together with selected ``traversing'' nodes, and we prove a safe (inequality) transfer theorem for two--walk mass under contraction, with an explicit nonnegative error and an equality characterization in terms of a wedge--equitable partition. Finally, we illustrate the theory on ten benchmark graphs and their ego-traversing contractions using table-driven diagnostics and two distribution figures.