Online social networks have emerged as prominent platforms for individuals to rapidly share ideas and perspectives. However, their swift dissemination capabilities also make them powerful channels for the spread of misinformation. Such dissemination results in substantial economic and societal harm, highlighting the need for effective suppression. To support this practical demand, this paper investigates the influence minimization (IMIN) and blocker minimization (BM) problems. The IMIN problem aims to find a node set of size k (the given budget) to block, such that the reduction in influence spread of a given seed set S is maximized. The BM problem is the dual of IMIN, aiming to find a node set of minimum cardinality to block, such that the threshold condition is satisfied, i.e., the reduction in influence spread of S is not less than the given threshold η . Both problems are NP-hard and involve a non-submodular objective function. Existing IMIN solutions incur high computational costs and offer no approximation guarantees on influence spread reduction. To fill the gap, we build upon the sandwich strategy to develop the first efficient algorithm with data-dependent approximation guarantees for IMIN. In particular, we design non-trivial (1-1/e-ϵ ) -approximation algorithms to optimize the proposed submodular bounding functions. For the BM problem, the existing solution is of limited effectiveness and lacks theoretical guarantees for both node set cardinality and threshold condition satisfaction. In this paper, we design a heuristic and two bicriteria approximation algorithms to address the BM problem. The heuristic is efficient and satisfies the threshold condition, but fails to ensure near-optimal cardinality. The two approximation algorithms both provide guarantees on cardinality: the first attains a tighter approximation ratio at the expense of efficiency, while the second yields a slightly weaker ratio with improved efficiency. Finally, comprehensive experiments on 9 real-world datasets are conducted to validate the efficiency and effectiveness of the proposed techniques.
We study the distinctiveness maximization problem for datasets assemblage: assembling a set of datasets from a repository that satisfies a user-specified target schema and query set under a budget constraint, while maximizing the number of distinct tuples. This problem is NP-hard in two scenarios: (i) when the repository contains datasets conforming to the target schema, and (ii) when no dataset individually satisfies the schema and candidates must be constructed through joins. For the first scenario, a greedy algorithm with exact distinctiveness computation achieves a (1-e^-1)/2 -approximation ratio, but is costly due to exhaustive tuple-wise marginal gain computation. We therefore propose an efficient machine learning (ML)-based method that directly estimates the marginal distinctiveness gain of each candidate dataset, avoiding costly exact computations. For the second scenario, we integrate a column-level merging technique into our ML framework to construct candidate datasets and estimate their distinctiveness without physically materializing join results, supporting efficient assemblage over heterogeneous datasets. Extensive experiments on real-world data pools show that our methods consistently outperform relevant baselines in effectiveness, efficiency, and scalability. A downstream case study on two ML tasks further shows that our approach identifies datasets with more useful tuples and improves ML performance.
Text-to-Vis is an emerging task in the data engineering and mining area that aims to automatically generate data visualizations from natural language questions (NLQs). Despite their progress, existing text-to-vis models often heavily rely on lexical matching between words in the questions and tokens in data schemas. This overreliance on lexical matching may lead to a diminished level of model robustness against input variations. In this study, we thoroughly examine the robustness of current text-to-vis models, an area that has not previously been explored. In particular, we construct the first robustness dataset nvBench-Rob, which contains diverse lexical and phrasal variations based on the original text-to-vis benchmark nvBench. Then, we found that the performance of existing text-to-vis models on this new dataset dramatically drops, implying that these methods exhibit inadequate robustness overall. To this end, we propose a novel framework based on Retrieval-Augmented Generation (RAG) technique, named GRED, specifically designed to address input perturbations in these two variants. The framework consists of three parts: NLQ-Retrieval Generator, Visualization Query-Retrieval Retuner and Annotation-based Debugger, which are used to tackle the challenges posed by natural language variants, programming style differences and data schema variants, respectively. Furthermore, we extend the framework, named C C, which further mitigates the uncertainty in visualization caused by ambiguous NLQ. It chooses the clause with the largest expected reduction of uncertainty under an elicited heuristic candidate-confidence measure and interacts with users for clarification to resolve ambiguity. This targeted interaction ensures that the resulting visualization more accurately reflects the user’s actual intent. Extensive experimental evaluations show that, compared to the state-of-the-art model Prompt4Vis in the Text-to-Vis field, GRED and C C perform better in terms of model robustness, with more than 40
Formal feature explanations strictly maintain perfect conformity but are intractable to compute, while heuristic methods are much faster but can lead to problematic explanations due to lack of conformity guarantees. We propose relative keys that have the best of both worlds. Relative keys associate feature explanations with a context, and warrant perfect conformity over the context as formal explanations do, whilst being orders of magnitudes faster and working for complex blackbox models. Based on it, we develop , an add-on for database systems to compute explanations of models they host, with provably bounded conformity and succinctness. We show that computing the most succinct relative keys is -complete and develop various algorithms for it with performance guarantees. Using 10 real-life datasets and 8 state-of-the-art explanation methods, we demonstrate that explains cases where existing methods cannot, and provides more succinct explanations with perfect conformity for cases they can; moreover, it is 2 orders of magnitude faster.
Answering an unbounded number of multi-dimensional range queries while preserving privacy is a significant problem that has been the focus of recent studies since range queries serve as a core component of many data analysis tasks. Existing techniques that use local differential privacy (LDP), which adds noise to users’ data locally, make assumptions that significantly affect utility. In particular, they assume that all attributes in a dataset are correlated and materialize all pair-wise grids/trees. Moreover, they assume that each pair-wise correlation is equally relevant when answering queries. These assumptions result in a low signal/noise ratio, especially for datasets with a large number of attributes. To address the deficiency of existing techniques, we propose a new approach to answer multi-dimensional range queries that leverages multi-dimensional correlations and workload characteristics to selectively choose the data that is collected under the LDP model. Our approach first leverages the availability of a small number of DP users to build a correlation model to determine how attributes are correlated. Then, we use the notion of multi-dimensional grids to capture multi-dimensional correlations. That allows us to materialize fewer grids, boosting the signal/noise ratio. Finally, we propose a new optimization step that makes use of available workload characteristics to identify the user distribution that minimizes the total error when answering multi-dimensional queries among users during the grid assignment process. We conduct extensive experiments on real and synthetic datasets and demonstrate the superiority of our method over existing approaches.
