OpenRaiser/Intern-Atlas huggingface dataset:
Wind Farm Layout Optimization with Diffusion Models
Authors: Yujin Shin, Taeyoung Yun, Sujin Yun, Sungpil Woo, Sunhwan Lim, Jinkyoo Park
Venue: KDD
Year: 2025

Wind farms generate electricity from wind, offering a sustainable and eco-friendly power source. In wind farms, determining the positions of wind turbines is crucial for high energy production due to complex wake interactions. However, optimizing wind farm layouts for given wind conditions remains challenging. While exist- ing methods try to solve the problem by training a surrogate model and employing optimization algorithms based on the model, these methods require a large number of simulations to obtain highly productive layouts, which is time-consuming in large-scale tasks. Furthermore, they mostly yield less diverse layouts, making the deployment of such layouts into real-world scenarios difficult. To address these challenges, we introduce a novel conditional genera- tive modeling approach to find wind farm layouts that maximize energy production. Our method consists of four stages. First, we collect datasets that consist of layouts and their corresponding an- nual energy production (AEP). Then, we train a diffusion model conditioned on AEP and wind scenarios using the collected dataset. Specifically, we use a Graph Neural Network as the backbone for the denoising network to ensure permutation invariance. Next, we sample promising layouts from the trained diffusion model by conditioning with a high AEP and given wind scenario. We also in- troduce a local search algorithm, which enables us to adjust layouts that violate design constraints. Finally, we evaluate the generated layouts and augment the dataset. We repeat these processes itera- tively to further optimize the layouts. Throughout these processes, we effectively generate highly productive and diverse wind farm layouts in a sample-efficient manner. Experimental results demon- strate that our approach outperforms state-of-the-art methods in maximizing energy production across various scenarios.
Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Authors: Harry Shomer, Jay Revolinsky, Jiliang Tang
Venue: KDD
Year: 2025

Knowledge Graph Completion (KGC) attempts to predict missing facts in a Knowledge Graph (KG). Recently, there's been an increased focus on designing KGC methods that can excel in the inductive setting, where a portion or all of the entities and relations seen in inference are unobserved during training. Numerous benchmark datasets have been proposed for inductive KGC, all of which are subsets of existing KGs used for transductive KGC. However, we find that the current procedure for constructing inductive KGC datasets inadvertently creates a shortcut that can be exploited even while disregarding the relational information. Specifically, we observe that the Personalized PageRank (PPR) score can achieve strong or near SOTA performance on most datasets. In this paper, we study the root cause of this problem. Using these insights, we propose an alternative strategy for constructing inductive KGC datasets that helps mitigate the PPR shortcut. We then benchmark multiple popular methods using the newly constructed datasets and analyze their performance. The new benchmark datasets help promote a better understanding of the capabilities and challenges of inductive KGC by removing any shortcuts that obfuscate performance. The code and dataset and can be found https://github.com/HarryShomer/Better-Inductive-KGC.
READ: Robust and Efficient Anomaly Detection under Data Contamination and Limited Supervision
Authors: Hongzhe Shou, Guanyu Lu, Martin Pavlovski, Fang Zhou
Venue: KDD
Year: 2025

Existing anomaly detection methods tend to utilize a large amount of training data to learn patterns of normal data for effective anomaly identification, but such methods typically incur substantial training time overhead. Considering that unlabeled data often contains a lot of redundant information, selecting and utilizing a small yet representative subset instead of the entire dataset can significantly improve training efficiency while maintaining detection performance. To this end, we introduce an end-to-end reinforcement learning framework with a balanced sampling strategy that targets both normal and abnormal instances. This framework identifies and exploits potential anomalies in the unlabeled data while sampling peripheral normal instances (often difficult to detect), thereby enhancing the overall anomaly detection performance without requiring excessive time for the sampling process. Additionally, we present a joint reward mechanism, combined with inconsistency penalties, which optimizes both an agent's action space and the representation space, ultimately improving the quality of the sampling process. Extensive experiments on four public datasets from different domains demonstrate the effectiveness and efficiency of our framework. The code is available at https://github.com/ZhouF-ECNU/READ.
Energy-Based Models for Predicting Mutational Effects on Proteins
Authors: Patrick Soga, Zhenyu Lei, Yinhan He, Camille L. Bilodeau, Jundong Li
Venue: KDD
Year: 2025

Predicting changes in binding free energy (ΔΔ G) is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have observed a high correlation between ΔΔ G and entropy, using probabilities of biologically important objects such as side chain angles and residue identities to estimate ΔΔ G. However, estimating the full conformational distribution of a protein complex is generally considered intractable. In this work, we propose a new approach to ΔΔ G prediction that avoids this issue by instead leveraging energy-based models for estimating the probability of a complex's conformation. Specifically, we novelly decompose ΔΔ G into a sequence-based component estimated by an inverse folding model and a structure-based component estimated by an energy model. This decomposition is made tractable by assuming equilibrium between the bound and unbound states, allowing us to simplify the estimation of degeneracies associated with each state. Unlike previous deep learning-based methods, our method incorporates an energy-based physical inductive bias by connecting the often-used sequence log-odds ratio-based approach to ΔΔ G prediction with a new ΔΔ E term grounded in statistical mechanics. We demonstrate superiority over existing state-of-the-art structure and sequence-based deep learning methods in ΔΔ G prediction and antibody optimization against SARS-CoV-2.
CATER: A Cluster-Based Alternative-Term Recommendation Framework for Large-Scale Web Search at NAVER
Authors: Jiwon Son, Jaeyoon Kim, Taekin Kim, Yeon-Chang Lee, Sang-Wook Kim
Venue: KDD
Year: 2025

Recently, searching for information by using search engines such as Google, Bing, and NAVER has become ubiquitous. While they attempt to provide information based on the search queries that users enter, it is not trivial to accurately capture the search intent of users. Motivated by this situation, NAVER Corp., the largest portal company in Korea, has developed a framework named as CATER (Cluster-based Alternative TErm Recommendation) framework that suggests alternative terms ("al-terms,'' in short) for better search outcomes relevant to a user's search intent. We introduce four design considerations (DCs) that were considered when designing and implementing CATER. Then, we describe how our CATER addresses the four DCs by using a clustering stage that dynamically maintains a pool of topic-oriented clusters containing terms, and a recommendation stage that identifies the top-k clusters (i.e., topics) and the top-k al-terms for each cluster. Furthermore, we present the scalable architecture adopted by CATER. Through various offline and online A/B tests using real-world datasets from NAVER, we validate that CATER successfully incorporates all DCs and that all design choices help improve the recommendation accuracy.
Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution
Authors: Wenhao Song, Xuan Wu, Bo Yang, You Zhou, Yubin Xiao, Yanchun Liang, Hongwei Ge, Heow Pueh Lee, Chunguo Wu
Venue: KDD
Year: 2025

To address the weight coupling problem, certain studies introduced few-shot Neural Architecture Search (NAS) methods, which partition the supernet into multiple sub-supernets. However, these methods often suffer from computational inefficiency and tend to provide suboptimal partitioning schemes. To address this problem more effectively, we analyze the weight coupling problem from a novel perspective, which primarily stems from distinct modules in succeeding layers imposing conflicting gradient directions on the preceding layer modules. Based on this perspective, we propose the Gradient Contribution (GC) method that efficiently computes the cosine similarity of gradient directions among modules by decomposing the Vector-Jacobian Product during supernet backpropagation. Subsequently, the modules with conflicting gradient directions are allocated to distinct sub-supernets while similar ones are grouped together. To assess the advantages of GC and address the limitations of existing Graph Neural Architecture Search methods, which are limited to searching a single type of Graph Neural Networks (Message Passing Neural Networks (MPNNs) or Graph Transformers (GTs)), we propose the Unified Graph Neural Architecture Search (UGAS) framework, which explores optimal combinations of MPNNs and GTs. The experimental results demonstrate that GC achieves state-of-the-art (SOTA) performance in supernet partitioning quality and time efficiency. In addition, the architectures searched by UGAS+GC outperform both the manually designed GNNs and those obtained by existing NAS methods. Finally, ablation studies further demonstrate the effectiveness of all proposed methods.
Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time
Authors: Zixing Song, Muzhi Li, Yifei Zhang, Irwin King, José Miguel Hernández-Lobato
Venue: KDD
Year: 2025

The prevalence of temporal networks in real-world applications, like financial transaction networks for loan approval prediction, poses significant challenges for ensuring fairness across different groups. These dynamic systems increasingly rely on Temporal Graph Neural Networks (TGNNs) to model evolving interactions between users over time, but TGNNs can inadvertently produce unfair outcomes across different demographic groups. In this work, we are the first to investigate group fairness on temporal graphs and propose a novel real-time framework for monitoring and improving group fairness in TGNNs. We begin by incorporating a fixed fairness regularization term into the TGNN framework, named FTGNN-R, which operates in real-time but exhibits several critical limitations. To address this, we propose FTGNN-M, a new monitoring-based approach that assesses fairness on the fly, without relying on unseen test data. By conducting a sensitivity analysis, FTGNN-M further identifies the specific channels of node embeddings responsible for unfairness and adaptively adjusts the corresponding subset of model parameters. This approach enables a trade-off between fairness and utility in dynamic settings. FTGNN-M offers theoretical guarantees for both fairness assessment and fairness promotion. Extensive experiments on five temporal transaction network datasets demonstrate the effectiveness of our proposed FTGNN-M model in terms of both utility and fairness metrics.
Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection
Authors: Minkyoo Song, Eugene Jang, Jaehan Kim, Seungwon Shin
Venue: KDD
Year: 2025

In light of rising drug-related concerns and the increasing role of social media, sales and discussions of illicit drugs have become commonplace online. Social media platforms hosting user-generated content must therefore perform content moderation, which is a difficult task due to the vast amount of jargon used in drug discussions. Previous works on drug jargon detection were limited to extracting a list of terms, but these approaches have fundamental problems in practical application. First, they are trivially evaded using word substitutions. Second, they cannot distinguish whether euphemistic terms (pot, crack) are being used as drugs or as their benign meanings. We argue that drug content moderation should be done using contexts, rather than relying on a banlist. However, manually annotated datasets for training such a task are not only expensive but also prone to becoming obsolete. We present JEDIS, a framework for detecting illicit drug jargon terms by analyzing their contexts. JEDIS utilizes a novel approach that combines distant supervision and delexicalization, which allows JEDIS to be trained without human-labeled data while being robust to new terms and euphemisms. Experiments on two manually annotated datasets show JEDIS significantly outperforms state-of-the-art word-based baselines in terms of F1-score and detection coverage in drug jargon detection. We also conduct qualitative analysis that demonstrates JEDIS is robust against pitfalls faced by existing approaches.
Canada AI Day
Authors: Filippo Maria Sposini, Aijun An
Venue: KDD
Year: 2025

Artificial Intelligence (AI) research in Canada is driving a vibrant ecosystem of startups and industry innovation, thanks significantly to the leadership of Canada CIFAR AI Chairs. Canada AI Day at KDD 2025 will showcase cutting-edge research by some of these leading experts, with a focus on building ethical, interpretable, and accessible AI systems. The event will feature a series of invited talks followed by a panel discussion bringing together academic, government, and industry researchers to address the challenges and opportunities in developing effective and responsible AI.
Difference-in-Differences Subset Scan
Authors: Will Stamey, Sriram Somanchi, Edward McFowland III
Venue: KDD
Year: 2025

Difference-in-differences (DiD) has been extensively applied in the literature to elicit the average causal effect of an intervention or policy. Though researchers explore heterogeneity in the treatment effect with respect to time or some observed covariate (usually driven by domain knowledge), there is limited work on a principled, algorithmic approach to discovering the subgroups that exhibit heterogeneity in the treatment effect in DiD settings. In this research, we propose the Difference-in-Differences Subset Scan (DiD-Scan), which finds subregions of the observed covariate space corresponding to anomalous patterns in the conditional mean treatment effect. We mold our method to the DiD setting by also enabling a scan for dynamic effects over time and across multiple outcome variables. This supports the discovery of patterns where a subset of outcomes are highly impacted for a limited time window. We develop a generalized likelihood ratio-based scoring function to quantify the treatment effect of a given subgroup and propose a computationally efficient method to discover the subgroups with the largest treatment effect. We extend the method to consider correlations across time, a common condition in difference-in-difference settings that increases the difficulty of subset identification. Lastly, we demonstrate the efficacy and interpretability of the method with both simulations and applications to real datasets, replicating and extending two published studies.
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
Authors: Ignacy Stepka, Jerzy Stefanowski, Mateusz Lango
Venue: KDD
Year: 2025

Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static scenarios, real-world applications often involve data or model changes, potentially invalidating previously generated CFEs and rendering user-induced input changes ineffective. Current methods addressing this issue often support only specific models or change types, require extensive hyperparameter tuning, or fail to provide probabilistic guarantees on CFE robustness to model changes. This paper proposes a novel approach for generating CFEs that provides probabilistic guarantees for any model and change type, while offering interpretable and easy-to-select hyperparameters. We establish a theoretical framework for probabilistically defining robustness to model change and demonstrate how our BetaRCE method directly stems from it. BetaRCE is a post-hoc method applied alongside a chosen base CFE generation method to enhance the quality of the explanation beyond robustness. It facilitates a transition from the base explanation to a more robust one with user-adjusted probability bounds. Through experimental comparisons with baselines, we show that BetaRCE yields robust, most plausible, and closest to baseline counterfactual explanations.
Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Authors: Zhixiang Su, Di Wang, Chunyan Miao
Venue: KDD
Year: 2025

Recent investigations on the effectiveness of Graph Neural Network (GNN)-based models for link prediction in Knowledge Graphs (KGs) show that vanilla aggregation does not significantly impact the model performance. In this paper, we introduce a novel method, named Context Pooling, to enhance GNN-based models' efficacy for link predictions in KGs. To our best of knowledge, Context Pooling is the first methodology that applies graph pooling in KGs. Additionally, Context Pooling is first-of-its-kind to enable the generation of query-specific graphs for inductive settings, where testing entities are unseen during training. Specifically, we devise two metrics, namely neighborhood precision and neighborhood recall, to assess the neighbors' logical relevance regarding the given queries, thereby enabling the subsequent comprehensive identification of only the logically relevant neighbors for link prediction. Our method is generic and assessed by being applied to two state-of-the-art (SOTA) models on three public transductive and inductive datasets, achieving SOTA performance in 42 out of 48 settings.
Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity
Authors: Zhiyuan Su, Sunhao Dai, Xiao Zhang
Venue: KDD
Year: 2025

Clustering of Bandits (CB) methods enhance sequential decision-making by grouping bandits into clusters based on similarity and incorporating cluster-level contextual information, demonstrating effectiveness and adaptability in applications like personalized streaming recommendations. However, when extending CB algorithms to their neural version (commonly referred to as Clustering of Neural Bandits, or CNB), they suffer from loss of plasticity, where neural network parameters become rigid and less adaptable over time, limiting their ability to adapt to non-stationary environments (e.g., dynamic user preferences in recommendation). To address this challenge, we propose Selective Reinitialization (SeRe), a novel bandit learning framework that dynamically preserves the adaptability of CNB algorithms in evolving environments. SeRe leverages a contribution utility metric to identify and selectively reset underutilized units, mitigating loss of plasticity while maintaining stable knowledge retention. Furthermore, when combining SeRe with CNB algorithms, the adaptive change detection mechanism adjusts the reinitialization frequency according to the degree of non-stationarity, ensuring effective adaptation without unnecessary resets. Theoretically, we prove that SeRe enables sublinear cumulative regret in piecewise-stationary environments, outperforming traditional CNB approaches in long-term performances. Extensive experiments on six real-world recommendation datasets demonstrate that SeRe-enhanced CNB algorithms can effectively mitigate the loss of plasticity with lower regrets, improving adaptability and robustness in dynamic settings.
A Framework for Leveraging Partially-Labeled Data for Product Attribute-Value Identification
Authors: D. Subhalingam, Keshav Kolluru, Mausam, Saurabh Singal
Venue: KDD
Year: 2025

In the e-commerce domain, the accurate extraction of attribute-value pairs (e.g., Brand: Apple) from product titles and user search queries is crucial for enhancing search and recommendation systems. A major challenge with neural models for this task is the lack of high-quality training data, as the annotations for attribute-value pairs in the available datasets are often incomplete. To address this, we introduce GenToC, a model designed for training directly with partially-labeled data, eliminating the necessity for a fully annotated dataset. GenToC employs a marker-augmented generative model to identify potential attributes, followed by a token classification model that determines the associated values for each attribute. GenToC outperforms existing state-of-the-art models, exhibiting upto 56.3% increase in the number of accurate extractions. Furthermore, we utilize GenToC to regenerate the training dataset to expand attribute-value annotations. This bootstrapping substantially improves the data quality for training other standard NER models, which are typically faster but less capable in handling partially-labeled data, enabling them to achieve comparable performance to GenToC. Our results demonstrate GenToC's unique ability to learn from a limited set of partially-labeled data and improve the training of more efficient models, advancing the automated extraction of attribute-value pairs. Finally, our model has been successfully integrated into IndiaMART, India's largest B2B e-commerce platform, achieving a significant increase of 20.2% in the number of correctly identified attribute-value pairs over the existing deployed system while achieving a high precision of 89.5%. We have released the code for GenToC model at https://github.com/KnowDisAI/GenToC.
SILO: Semantic Integration for Location Prediction with Large Language Models
Authors: Tianao Sun, Meng Chen, Bowen Zhang, Genan Dai, Weiming Huang, Kai Zhao
Venue: KDD
Year: 2025

Next location prediction is a critical task in human mobility modeling, with broad applications in personalized recommendation, urban planning, and location-based services. Recently, researchers have used prompt-based large language models (LLMs) to improve next location prediction with pre-trained knowledge. However, they face inherent challenges in bridging the gap between textual prompts for semantic contextual understanding and human mobility data for transition pattern modeling. In this paper, we introduce SILO, a framework designed for Semantic Integration in LOcation prediction via LLMs. We first construct a hybrid semantic space that seamlessly integrates ID-based embeddings, text-derived semantics, and auxiliary contextual information, enabling comprehensive modeling of sequential mobility patterns alongside contextual nuances. We then propose user-centric prompts that specify the prediction task for LLMs while embedding user context within a special token. Further, we utilize LLMs as the prediction backbone to process both user-specific prompts and hybrid ID-context embeddings of location sequences. To enhance predictive performance, we finally introduce a dual-logits strategy, combining sequential transition logits with user profile-guided semantic preference logits. Extensive experiments on two large-scale real-world mobility datasets demonstrate that SILO significantly outperforms state-of-the-art baselines, validating its effectiveness in modeling complex mobility patterns through semantic integration using LLMs.