Knowledge Graph Question Answering (KGQA) has advanced through structured query generation, yet most efforts target RDF/SPARQL, leaving Cypher and property graphs underexplored, despite increasing demand for unified KGQA in industry settings. We propose UniQGen, a novel constraint-based framework that employs LLM agents to dynamically extract and refine representative graph query clauses into executable, intent-aligned graph queries across query languages. The foundation of our method is a variant of Chase & Backchase, a family of algorithms for query optimization and reformulation. We extend Chase & Backchase with a dynamic reasoning process over query constraints that also interact with LLMs for query quality estimation. With a Cypher-supported Freebase graph deployed on Amazon Neptune, we extensively evaluate our approach on popular KGQA benchmarks (GraphQ, GrailQA, and WebQSP). We demonstrate that UniQGen outperforms state-of-the-art graph query generation techniques in both accuracy and efficiency, with F1 gains of 31.6% on GraphQ and 4.9% on GrailQA. Unlike prior methods, our framework does not require fine-tuning for schema matching, making it more extensible to schema-less graphs and semantics in query workloads, and is more suitable for enterprise-grade KGQA. We release Cypher outputs and a Neptune-ready Freebase snapshot to support reproducible, cross-language KGQA research.

Machine learning (ML) assets, such as models, datasets, and metadata—are central to modern ML workflows. Despite their explosive growth in practice, these assets are often underutilized due to fragmented documentation, siloed storage, inconsistent licensing, and lack of unified discovery mechanisms, making ML-asset management an urgent challenge. This tutorial offers a comprehensive overview of ML-asset management activities across its lifecycle, including curation, discovery, and utilization. We provide a categorization of ML assets, and major management issues, survey state-of-the-art techniques, and identify emerging opportunities at each stage. We further highlight system-level challenges related to scalability, lineage, and unified indexing. Through live demonstrations of systems, this tutorial equips both researchers and practitioners with actionable insights and practical tools for advancing ML-asset management in real-world and domain-specific settings.

Knowledge graphs (KGs) are routinely curated to provide factual data for various domain-specific analyses. Nevertheless, it remains nontrivial to explore domain knowledge with standard query languages. We demonstrate GraphLingo, a natural language (NL)-based knowledge exploration system designed for exploring domain-specific knowledge graphs. It differs from conventional knowledge graph search tools in that it enables an interactive exploratory NL query over domain-specific knowledge graphs. GraphLingo seamlessly integrates graph query processing and large language models with a graph pattern-based prompt generation approach to guide users in exploring relevant factual knowledge. It streamlines NL-based question & answer, graph query optimization & refining, and automatic prompt generation. A unique feature of GraphLingo is its capability to enable users to explore by seamlessly switching between a more ‘open’ approach and a more relevant yet ‘conservative’ one, facilitated by diversified query suggestions. We show cases of GraphLingo in curriculum suggestion, and materials scientific data search.

This paper introduces a new class of explanation structures, called robust counterfactual witnesses (RCWs), to provide robust, both counterfactual and factual explanations for graph neural networks. Given a graph neural network$\mathcal{M}$, a robust counterfactual witness refers to the fraction of a graph$G$that are counterfactual and factual explanation of the results of$\mathcal{M}$over$G$, but also remains so for any “disturbed”$G$by flipping up to$k$of its node pairs. We establish the hardness results, from tractable results to co-NP-hardness, for verifying and generating robust counterfactual witnesses. We study such structures for GNN-based node classification, and present efficient algorithms to verify and generate RCWs. We also provide a parallel algorithm to verify and generate RCWs for large graphs with scalability guarantees. We experimentally verify our explanation generation process for benchmark datasets, and showcase their applications.

We demonstrate ModsNet , a search tool for pre-trained data science MOD el s recommendatio N using E xamplar da T aset. Given a set of pre-trained data science models, an "example" input dataset, and a user-specified performance metric, ModsNet answers the following query: "what are top-k models that have the best expected performance for the input data?" The need for searching high-quality pre-trained models is evident in data-driven analysis. Inspired by "query by example" paradigm, ModsNet does not require users to write complex queries, but only provide an "examplar" dataset, a task description, and a performance measure as input, and can automatically suggest top- k matching models that are expected to have desirable performance to perform the task over the provided sample dataset. ModsNet utilizes a knowledge graph to integrate model performances over datasets and synchronizes it with a bipartite graph neural network to estimate model performance, reduce inference cost, and promptly respond to top- k model search queries. To cope with strict cold-start (upon receiving a new dataset when no historical performance of registered models are observed), it performs a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising models. We demonstrate the application of ModsNet in enabling efficient scientific data analysis.

Data analytical pipelines routinely involve various domain-specific data science models. Such models require expensive manual or training effort and often incur expensive validation costs (e.g., via scientific simulation analysis). Meanwhile, high-value models remain to be ad-hocly created, isolated, and underutilized for a broad community. Searching and accessing proper models for data analysis pipelines is desirable yet challenging for users without domain knowledge. This paper introduces ModsNet, a novel MODel SelectioN framework that only requires an Example daTaset. (1) We investigate the following problem: Given a library of pre-trained models, a limited amount of historical observations of their performance, and an "example" dataset as a query, return k models that are expected to perform the best over the query dataset. (2) We formulate a regression problem and introduce a knowledge-enhanced framework using a model-data interaction graph. Unlike traditional methods, (1) ModsNet uses a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising pre-trained models in a strict cold-start scenario (when a new dataset without any interaction with existing models is given). (2) To reduce the learning cost, we develop a clustering-based sparsification strategy to prune unpromising models and their interactions. (3) We showcase of ModsNet built on top of a crowdsourced materials knowledge base platform. Our experiments verified its effectiveness, efficiency, and applications over real-world analytical pipelines.

We introduce GALE, an active adversarial learning framework to detect nodes with erroneous information in attributed graphs. GALE is empowered by a new adversarial active error detection framework, which interacts active learning with a graph generative adversarial model to best exploit limited labeled examples of erroneous nodes. It dynamically determines diversified query nodes in batches with bounded size in terms of node typicality to enrich a pool of examples, which in turn provides representative examples to best train an adversarial classifier to capture different types of errors. Moreover, GALE provides an annotation algorithm to suggest a context of possible correct attribute values and error types, to facilitate the labeling of query nodes. We show that using limited queries and examples, GALE significantly improves competing methods such as constraint-based detection, outlier detection, and Graph Neural Networks (e.g. GCNs), with 32%, 31%, and 17% gain in F-1 score on average, and is feasible in learning cost for large graphs.

Given a set of node groups in a graph (e.g., gender or race groups), how to succinctly summarize their neighbors, and meanwhile ensure a "fair" representation to mitigate under- or over-representation of a certain group? We propose a novel framework to compute concise summaries of node groups with fairness guarantees. (1) We introduce a pattern-correction structure called r-summaries. An r-summary uses a graph pattern set to specify representative nodes and an auxiliary edge correction set to losslessly describe their r-hop neighbors. (2) We formulate the fair group summarization problem, which is to compute an r-summary that can select and accurately describe high quality nodes and their neighbors with small edge corrections, and meanwhile guarantee a desirable coverage for each group. The need for generating such summaries is evident in social recommendation, healthcare and graph search. We show that the problem is $\Sigma _2^p$-complete with the verification problem already NP-complete. (3) We present approximation algorithms that can generate r-summaries with (a) guaranteed quality and coverage properties, and (b) relative approximations on optimal edge correction costs. For large groups, we introduce an efficient algorithm that interleaves node selection and localized pattern discovery to reduce unnecessary computation. In addition, we introduce an algorithm to incrementally maintain the r-summaries over dynamic graphs with evolving edges. Using real-world data, we experimentally verify the efficiency and effectiveness of our algorithms and verify their applications.

Modern multidisciplinary materials science routinely processes scientific workflows that integrate different data resources (e.g., X-ray data, scripts, analytical results). Most of such data resources are isolated in research labs, created ad-hocly, and remain underutilized. We demonstrate CRUX, a Crowdsourced platform for materials data ResoUrces and workflow eXploration. CRUX is empowered by coherent data-workflow modeling, knowledge-based resource assembly for workflow search, and data provenance to support workflow exploration. CRUX allows users to declare parameterized workflows as graph patterns, and automatically recommends crowdsourced resources with quality guarantees. We demonstrate the ease-of-use and the performance of CRUX with three categories of queries: data search, workflow recommendation, and resource exploration. We make case of CRUX for peak finding in X-ray Diffraction (XRD) data, a cornerstone task in materials research. We show that CRUX enables new interactive paradigms to explore and design workflows for data analysts in general.

This paper studies the problem of subgraph query generation with guarantees on both diversity and group fairness. Given a query template (with parameterized search predicates) and a set of node groups in a graph, it is to compute a set of sub-graph queries that instantiate the query template, and each query ensures diversified answers that meanwhile covers each group with a desired number of nodes. Such need is evident in web and social search with fairness constraints, query optimization, and query benchmarking. We formalize a bi-criteria optimization problem that aims to find a Pareto optimal set of query instances in terms of diversity and fairness measures. We show the problem is in Δ$P$2 and verify its hardness (NP-hard and fixed-parameter tractable). We provide (1) two efficient algorithms that can approximate Pareto optimal sets with E-dominance relations that yield representative query instances with a bounded size, and (2) an online algorithm that progressively generates and maintains fixed-size ∊-Pareto set with small delay time. We experimentally verify that our algorithms can efficiently generate queries with desired diversity and coverage properties for targeted groups.