arXiv subjects

Fangzhu Shen

Publications and source records attributed to Fangzhu Shen.

3 recordsLinked to original sources

arXiv2026

Selectivity Estimation for Linear Queries via Online Learning

Learning-based approaches for selectivity estimation in databases have gained significant traction in recent years. However, theoretical studies of these learning-based approaches are essentially limited to fixed query distributions on static databases. In practice, both the underlying database and the query workload can dynamically change over time. In this work, we propose an algorithmic framework for learning selectivity of queries in this more general dynamic setup. Inspired by online learning, we measure the performance of the learning algorithm in this setting by its regret, which compares the cumulative loss incurred by the learning algorithm to that of the best fixed strategy. We establish upper and lower bounds on regret for histogram-based linear queries, such as point, range, and subset selection queries, under standard loss functions, in both static and dynamic database settings.

cs.DB↗

arXiv2026

Efficient Shapley-Based Influence Attribution in Social Networks

The ubiquity of social platforms has reshaped the way information, behaviors, and advertisements diffuse across networks, with influence propagation often initiated by a small set of ``seed'' users. While much of the literature emphasizes optimizing seed selection to maximize spread, a critical yet underexplored question remains: how to fairly estimate the contributions of individual seeds ``ex-ante'', i.e., before the diffusion process occurs? This capability is essential for budget allocation, influencer pricing, and fair, privacy-preserving credit distribution under uncertainty, without relying on ex-post cascade logs that capture only a single execution of influence propagation. We introduce a framework for ex-ante influence attribution based on Shapley values from cooperative game theory, which capture each seed's marginal impact in a principled and equitable manner. Adapting Shapley values to influence propagation raises unique computational challenges due to the stochastic nature of diffusion and the intricate dependencies across network structures. To address these challenges, we design polynomial-time algorithms for the special case of single-step activation that is of independent practical interest, establish a sharp tractability boundary by proving $\#P$-hardness for any propagation beyond one step, and develop approximation algorithms with provable guarantees for the standard IC model as well as time-bounded variants. Empirical evaluation on real-world and synthetic networks demonstrates that our methods are both efficient and effective, offering a practical mechanism for ex-ante influence attribution.

cs.SI↗

arXiv2024

The Cost of Representation by Subset Repairs

Datasets may include errors, and specifically violations of integrity constraints, for various reasons. Standard techniques for ``minimal-cost'' database repairing resolve these violations by aiming for minimum change in the data, and in the process, may sway representations of different sub-populations. For instance, the repair may end up deleting more females than males, or more tuples from a certain age group or race, due to varying levels of inconsistency in different sub-populations. Such repaired data can mislead consumers when used for analytics, and can lead to biased decisions for downstream machine learning tasks. We study the ``cost of representation'' in subset repairs for functional dependencies. In simple terms, we target the question of how many additional tuples have to be deleted if we want to satisfy not only the integrity constraints but also representation constraints for given sub-populations. We study the complexity of this problem and compare it with the complexity of optimal subset repairs without representations. While the problem is NP-hard in general, we give polynomial-time algorithms for special cases, and efficient heuristics for general cases. We perform a suite of experiments that show the effectiveness of our algorithms in computing or approximating the cost of representation.

cs.DB↗