Sudeepa Roy

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.

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. We are pleased to present the 3rd issue of Volume 4 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in October 2025 and is the last issue of the research track of SIGMOD 2026.

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. We are pleased to present the 1st issue of Volume 4 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in July 2025.

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. We are pleased to present the 6th issue of Volume 3 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in April 2025.

The class of hierarchical queries is known to define the boundary of the dichotomy between tractability and intractability for the following two extensively studied problems about self-join free Boolean conjunctive queries (SJF-BCQ): (i) evaluating a SJF-BCQ on a tuple-independent probabilistic database; (ii) computing the Shapley value of a fact in a database on which a SJF-BCQ evaluates to true. Here, we establish that hierarchical queries define also the boundary of the dichotomy between tractability and intractability for a different natural algorithmic problem, which we call the bag-set maximization problem. The bag-set maximization problem associated with a SJF-BCQ Q asks: given a database D, find the biggest value that Q takes under bag semantics on a database D' obtained from D by adding at most θ facts from another given database D ^r . For non-hierarchical queries, we show that the bag-set maximization problem is an NP-complete optimization problem. More significantly, for hierarchical queries, we show that all three aforementioned problems (probabilistic query evaluation, Shapley value computation, and bag-set maximization) admit a single unifying polynomial-time algorithm that operates on an abstract algebraic structure, called a 2-monoid . Each of the three problems requires a different instantiation of the 2-monoid tailored for the problem at hand.

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. The 2026 SIGMOD conference cycle begins with this issue of PACMMOD - the fourth issue of Volume 3 of PACMMOD. We thank our predecessors, Azza Abouzied and Joe Hellerstein, for their work on the previous issues, and their guidance as we began the process for SIGMOD 2026.

Prescriptions, or actionable recommendations, are commonly generated across various fields to influence key outcomes such as improving public health, enhancing economic policies, or increasing business efficiency. While traditional association-based methods may identify correlations, they often fail to reveal the underlying causal factors needed for informed decision-making. On the other hand, in decision making for tasks with significant societal or economic impact, it is crucial to provide recommendations that are interpretable and justifiable, and equitable in terms of the outcome for both the protected and non-protected groups. Motivated by these two goals, this paper introduces a fairness-aware framework leveraging causal reasoning for generating a set of interpretable and actionable prescription rules (ruleset) toward betterment of an outcome while preventing exacerbating inequalities for protected groups. By considering group and individual fairness metrics from the literature, we ensure that both protected and non-protected groups benefit from these recommendations, providing a balanced and equitable approach to decision-making. We employ efficient optimizations to explore the vast and complex search space considering both fairness and coverage of the prescription ruleset. Empirical evaluation and case study on real-world datasets demonstrates the utility of our framework for different use cases.

In this paper, we study circuits and formulas for provenance polynomials of Datalog programs. We ask the following question: given an absorptive semiring and a fact of a Datalog program, what is the optimal depth and size of a circuit/formula that computes its provenance polynomial? We focus on absorptive semirings as these guarantee the existence of a polynomial-size circuit. Our main result is a dichotomy for several classes of Datalog programs on whether they admit a formula of polynomial size or not. We achieve this result by showing that for these Datalog programs the optimal circuit depth is either Θ(log m ) or Θ(log ² m ), where m is the input size. We also show that for Datalog programs with the polynomial fringe property, we can always construct low-depth circuits of size O(log ² m ). Finally, we give characterizations of when Datalog programs are bounded over more general semirings.