OpenRaiser/Intern-Atlas huggingface dataset:
From Swath to Full-Disc: Advancing Precipitation Retrieval with Multimodal Knowledge Expansion
Authors: Zheng Wang, Kai Ying, Bin Xu, Chunjiao Wang, Cong Bai
Venue: KDD
Year: 2025

Accurate near-real-time precipitation retrieval has been enhanced by satellite-based technologies. However, infrared-based algorithms have low accuracy due to weak relations with surface precipitation, whereas passive microwave and radar-based methods are more accurate but limited in range. This challenge motivates the Precipitation Retrieval Expansion (PRE) task, which aims to enable accurate, infrared-based full-disc precipitation retrievals beyond the scanning swath. We introduce Multimodal Knowledge Expansion, a two-stage pipeline with the proposed PRE-Net model. In the Swath-Distilling stage, PRE-Net transfers knowledge from a multimodal data integration model to an infrared-based model within the scanning swath via Coordinated Masking and Wavelet Enhancement (CoMWE). In the Full-Disc Adaptation stage, Self-MaskTune refines predictions across the full disc by balancing multimodal and full-disc infrared knowledge. Experiments on the introduced PRE benchmark demonstrate that PRE-Net significantly advanced precipitation retrieval performance, outperforming leading products like PERSIANN-CCS, PDIR, and IMERG. The code will be available at https://github.com/Zjut-MultimediaPlus/PRE-Net.
DimCL: Dimension-Aware Augmentation in Contrastive Learning for Recommendation
Authors: Chi Zhang, Qilong Han, Qiaoyu Tan, Shengjie Wang, Xiangyu Zhao, Rui Chen
Venue: KDD
Year: 2025

Contrastive learning (CL) has achieved remarkable success in addressing data sparsity issues in collaborative filtering (CF) for recommender systems (RSs). The key principle is to generate different augmented views given a user-item interaction graph. However, prior endeavors mainly focus on performing augmentation via stochastic functions, e.g., by injecting perturbations into different hidden dimensions uniformly. Without fine control, the hidden representations of augmentations may contain noisy dimensions that are harmful to CL and irrelevant to RSs. Removing dimension-specific noise is a challenging task due to the following two major bottlenecks. It is difficult to (i) distinguish different dimensions' efficacy for CL and (ii) bridge the semantic gap between CL and RSs. Overlooking these limitations may cause redundant, false-positive, and irrelevant noise in hidden dimensions of the augmented views. In this paper, we solve the above challenges from the perspective of robust learning and curriculum learning, and propose a novel Dimension-aware augmentation in Ceontrastive Leearning for recommendation (DimCL). In DimCL, we first theoretically analyze the easiness and hardness of different dimensions for CL and RSs. With thorough analysis, we propose two propositions, which reveal that the gradients of different dimensions of augmented views are potentially related to the learning difficulty of optimizing CL and RSs. The comparison of gradients can provide detectable signals to reflect the efficacy of different dimensions for CL and the semantic gap between CL and RSs. Based on the analysis results, we devise three denoising factors, which can help DimCL to identify hard-to-learn dimensions as redundant or false-positive noise and pinpoint dimensions in different augmented views with inconsistent difficulties of RSs as irrelevant noise without requiring additional supervised labels. After denoising, DimCL can remove dimension-level noise to reduce unnecessary difficulty, making CL easier and maintaining more consistent difficulty in RSs. Extensive experiments on four public datasets demonstrate DimCL's superiority and flexible applications over various traditional and CL-based CF methods. The source code is publicly available online at https://github.com/zc-97/DimCL.
KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing
Authors: Rui Li, Quanyu Dai, Zeyu Zhang, Xu Chen, Zhenhua Dong, Ji-Rong Wen
Venue: KDD
Year: 2025

Recent advances in retrieval-augmented generation (RAG) furnish large language models (LLMs) with iterative retrievals of relevant information to handle complex multi-hop questions. These methods typically alternate between LLM reasoning and retrieval to accumulate external information into the LLM's context. However, the ever-growing context inherently imposes an increasing burden on the LLM to perceive connections among critical information pieces, with futile reasoning steps further exacerbating this overload issue. In this paper, we present KnowTrace, an elegant RAG framework to (1) mitigate the context overload and (2) bootstrap higher-quality multi-step reasoning. Instead of simply piling the retrieved contents, KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question. Such a structured workflow not only empowers the LLM with an intelligible context for inference, but also naturally inspires a reflective mechanism of knowledge backtracing to identify contributive LLM generations as process supervision data for self-bootstrapping. Extensive experiments show that KnowTrace consistently surpasses existing methods across three multi-hop question answering benchmarks, and the bootstrapped version further amplifies the gains.
Learning Reliable and Intuitive Temporal Logic Rules for Interpretable Time Series Classification
Authors: Yang Wang, Jiaqi Zhu, Miaomiao Li, Jiang Liu, Yilin Li, Yi Yang, Jiafan Li, Hongan Wang
Venue: KDD
Year: 2025

Recently, rule-based time series classification models are widely used in safety-critical scenarios demanding strong interpretability, through providing explicit and rigorous rationales. However, due to the discrepancy between discrete rules and continuous neural networks, the generated rules are not completely consistent with the actual decision-making process, rendering users hesitant to trust the model. Additionally, existing methods learn the role (weight) of each time point independently, which is not in line with human understanding and the consecutive nature of temporal properties. In this paper, we propose a novel neuro-symbolic model named TemporalRule, aiming to automatically learn Signal Temporal Logic (STL) rules for interpretable time series classification. Our model directly optimizes the neural networks representing discrete rules via gradient grafting, producing reliable rules that can exactly determine the classification results. Notably, a temporal logical layer is designed to simulate expressive temporal operators, including one 2-predicate operator (Until) and two 2-level composite operators (EA and AE), through adaptively learning the time bounds of involved intervals. That creates consistent weights for consecutive time points, enhancing the intuitiveness of derived rules. Extensive experiments on diverse real-world datasets in safety-critical domains show that TemporalRule achieves superior and stable classification accuracy compared to state-of-the-art rule-based approaches, and the learned rules can precisely and concisely explicate the classification results, leading to a trustable model.
Proxy-Validated Importance-Aware Federated Sample Selection with Meta Learning
Authors: Yan Zhang, Xiaoye Miao, Bin Li, Yangyang Wu, Yongheng Shang
Venue: KDD
Year: 2025

Federated data selection strategically chooses a group of high-quality samples to train a global model, and it is promising to optimize the convergence and resource overhead of federated learning (FL). However, existing studies either fail to account for the dynamic importance of training samples or rely on external unbiased validation datasets. These shortcomings can compromise FL model performance, potentially complicating their application in real-world scenarios. In this paper, we propose a novel proxy-validated importance-aware federated sample selection framework, termed FedSelect. It employs a novel meta learning approach with a proxy validation dataset to select the most positively important clients and their most important samples, thereby accelerating the training process and optimizing FL model performance. To eliminate the dependency on external unbiased data, we present a momentum-based meta-margin function to discover influential samples as the proxy validation dataset, providing an adaptive reward for sample selection. Furthermore, we also develop an online meta model update strategy to guarantee the efficiency of FedSelect. Comprehensive experiments on four benchmark datasets demonstrate that FedSelect is superior in both effectiveness and efficiency, while maintaining strong scalability across diverse scenarios. The source code can be accessed at: https://github.com/nameyzhang/FedSelect.
Network Interdiction Goes Neural
Authors: Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao
Venue: KDD
Year: 2025

Network interdiction problems, arising in critical applications from military strategy to disease control, involve a complex attacker-defender dynamic: one player optimizes a network-based objective, while the other strategically modifies the network to impede that objective. The inherent bi-level optimization and combinatorial nature of these problems pose a significant computational challenge, often rendering traditional exact solvers impractical and hindering the development of effective heuristics. While Graph Neural Networks (GNNs) have demonstrated promise in solving single-level combinatorial optimization problems on graphs, their direct application to bi-level interdiction problems remains limited. In this paper, we bridge this gap by introducing a novel approach that leverages the power of GNNs to learn Mixed-Integer Linear Programming (MILP) formulations of network interdiction problems. By representing the problem in this structured mathematical form, we empower a multipartite GNN with the representational capacity to effectively capture the complex interplay between the two players. This approach aligns the neural network with the underlying mathematical structure of interdiction problems, leading to improved performance. Through extensive experiments on two network interdiction tasks, we demonstrate the superiority of our proposed method over both baseline GNN models and traditional exact solvers, showcasing its potential for real-world applications.
Machine Learning on Graphs in the Era of Generative Artificial Intelligence
Authors: Yu Wang, Yu Zhang, Zhichun Guo, Harry Shomer, Haoyu Han, Tyler Derr, Nesreen K. Ahmed, Mahantesh Halappanavar, Jiliang Tang
Venue: KDD
Year: 2025

Graphs, which encode pairwise relations between entities, serve as a fundamental data structure across real-world domains. Many critical applications can be formulated as graph-based tasks, and graph machine learning (GML), from the shallow embedding models to graph neural networks and further advanced to the most powerful graph transformers, has been well-established to automate knowledge discovery and decision-making on graphs. In parallel, the recent emergence of large foundational models has driven machine learning into a new era of Generative Artificial Intelligence (Gen-AI), and this revolution presents both unprecedented opportunities and profound challenges for the well-established GML paradigms. However, few investigations have analyzed and envisioned how GML should evolve to harness these opportunities, address these challenges, and embrace this new Gen-AI era. To fill in this gap, we organize the first international Workshop on Machine Learning on Graphs in the Era of Generative Artificial Intelligence (MLoG-GenAI), held in connection with the 31st ACM Conference on Knowledge Discovery and Data Mining, which provides a venue to gather academic researchers and industry practitioners to discuss and picture the development of GML in the new Gen-AI era.
Mitigating Data Imbalance in Time Series Classification Based on Counterfactual Minority Samples Augmentation
Authors: Lei Wang, Shanshan Huang, Chunyuan Zheng, Jun Liao, Xiaofei Zhu, Haoxuan Li, Li Liu
Venue: KDD
Year: 2025

Imbalanced Time-Series Classification is a critical, yet challenging task across a spectrum of real-world applications. Previous oversampling and generative approaches primarily target the minority class and often rely on static decision boundaries or similarity-based heuristics. However, these methods overlook the underlying causal factors that govern the distinction between majority and minority classes, particularly in scenarios with ambiguous class boundaries. As a result, the generated samples may fail to enhance class separability, thereby limiting improvements in classification performance. To this end, we propose a CounterFactual Augmentation Minority Generation (CFAMG) method based on generative models that aims to discover the causal factors that determine different classes from a causality perspective. Specifically, our method first utilizes a disentangled classifier to distinguish between causal and non-causal factors. Next, we perform counterfactual intervention by replacing the causal factors of majority class samples with those from minority class samples, creating an intervened latent representation that reflects minority characteristics while preserving essential structures. Finally, the trained minority class decoder generates counterfactual minority samples that resemble real minority instances yet remain distinguishable from the original majority class. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in both univariate and multivariate imbalanced time-series classification tasks. The code is published at https://github.com/WangLei-CQU/CFAMG.
Graph Positional Autoencoders as Self-supervised Learners
Authors: Yang Liu, Deyu Bo, Wenxuan Cao, Yuan Fang, Yawen Li, Chuan Shi
Venue: KDD
Year: 2025

Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency and scalability. Typically, GAEs take incomplete graphs as input and predict missing elements, such as masked node features or edges. Although effective, our experimental investigation reveals that traditional feature or edge masking paradigms primarily capture low-frequency signals in the graph and fail to learn expressive structural information. To address these issues, we propose Graph Positional Autoencoders (GraphPAE), which employ a dual-path architecture to reconstruct both node features and positions. Specifically, the feature path uses positional encoding to enhance the message-passing processing, improving the GAEs' ability to predict the corrupted information. The position path, on the other hand, leverages node representations to refine positions and approximate eigenvectors, thereby enabling the encoder to learn diverse frequency information. We conduct extensive experiments to verify the effectiveness of GraphPAE, including heterophilic node classification, graph property prediction, and transfer learning. The results demonstrate that GraphPAE achieves state-of-the-art performance and consistently outperforms the baselines by a large margin.
K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
Authors: Tassallah Abdullahi, Ioanna Gemou, Nihal V. Nayak, Ghulam Murtaza, Stephen H. Bach, Carsten Eickhoff, Ritambhara Singh
Venue: KDD
Year: 2025

Biomedical knowledge graphs (KGs) encode rich, structured information critical for drug discovery tasks, but extracting meaningful insights from large-scale KGs remains challenging due to their complex structure. Existing biomedical subgraph retrieval methods are tailored for graph neural networks (GNNs), limiting compatibility with other paradigms, including large language models (LLMs). We introduce K-Paths, a model-agnostic retrieval framework that extracts structured, diverse, and biologically meaningful multi-hop paths from dense biomedical KGs. These paths enable prediction of unobserved drug-drug and drug-disease interactions, including those involving entities not seen during training, thus supporting inductive reasoning. K-Paths is training-free and employs a diversity-aware adaptation of Yen's algorithm to extract the K shortest loopless paths between entities in a query, prioritizing biologically relevant and relationally diverse connections. These paths serve as concise, interpretable reasoning chains that can be directly integrated with LLMs or GNNs to improve generalization, accuracy, and enable explainable inference. Experiments on benchmark datasets show that K-Paths improves zero-shot reasoning across state-of-the-art LLMs. For instance, Tx-Gemma 27B improves by 19.8 and 4.0 F1 points on interaction severity prediction and drug repurposing tasks, respectively. Llama 70B achieves gains of 8.5 and 6.2 points on the same tasks. K-Paths also boosts the training efficiency of EmerGNN, a state-of-the-art GNN, by reducing the KG size by 90% while maintaining predictive performance. Beyond efficiency, K-Paths bridges the gap between KGs and LLMs, enabling scalable and explainable LLM-augmented scientific discovery. We release our code and the retrieved paths as a benchmark for inductive reasoning.
Bridging the Binary Analysis Gap: A Cross-Compiler Dataset and Neural Framework for Industrial Control Systems
Authors: Yonatan Gizachew Achamyeleh, Shih-Yuan Yu, Gustavo Quiros Araya, Mohammad Abdullah Al Faruque
Venue: KDD
Year: 2025

Industrial Control Systems (ICS) rely heavily on Programmable Logic Controllers (PLCs) to manage critical infrastructure, yet analyzing PLC executables remains challenging due to diverse proprietary compilers and limited access to source code.To bridge this gap, we introduce PLC-BEAD, a comprehensive dataset containing 2431 compiled binaries from 700+ PLC programs across four major industrial compilers (CoDeSys, GEB, OpenPLC-V2, OpenPLC-V3). This novel dataset uniquely pairs each binary with its original Structured Text source code and standardized functionality labels, enabling both binary-level and source-level analysis. We demonstrate the dataset's utility through PLCEmbed, a transformer-based framework for binary code analysis that achieves 93% accuracy in compiler provenance identification and 42% accuracy in fine-grained functionality classification across 22 industrial control categories. Through comprehensive ablation studies, we analyze how compiler optimization levels, code patterns, and class distributions influence model performance. We provide detailed documentation of the dataset creation process, labeling taxonomy, and benchmark protocols to ensure reproducibility. Both PLC-BEAD and PLCEmbed are released as open-source resources to foster research in PLC security, reverse engineering, and ICS forensics, establishing new baselines for data-driven approaches to industrial cybersecurity.
Fair Diversity Maximization with Few Representatives
Authors: Florian Adriaens, Nikolaj Tatti
Venue: KDD
Year: 2025

Diversity maximization problem is a well-studied problem where the goal is to find k diverse items. Fair diversity maximization aims to select a diverse subset of k items from a large dataset, while requiring that each group of items be well represented in the output. More formally, given a set of items with labels, our goal is to find k items that maximize the minimum pairwise distance in the set, while maintaining that each label is represented within some budget. In many cases, one is only interested in selecting a handful (say a constant) number of items from each group. In such scenario we show that a randomized algorithm based on padded decompositions improves the state-of-the-art approximation ratio to √log(m) /(3m), where m is the number of labels. The algorithms work in several stages: (i) a preprocessing pruning which ensures that points with the same label are far away from each other(ii) a decomposition phase, where points are randomly placed in clusters such that there is a feasible solution with maximum one point per cluster and that any feasible solution will be diverse(iii) assignment phase, where clusters are assigned to labels, and a representative point with the corresponding label is selected from each cluster. We experimentally verify the effectiveness of our algorithm on large datasets.
Separating Graph Generative Models via Degree Distributions
Authors: Daniel Alabi, Dimitris Kalimeris
Venue: KDD
Year: 2025

We analyze the power and limitations of a family of graph generative models, including the original Stochastic Kronecker Graph (SKG) models, often used to ensure privacy of network data. The SKG model cannot always generate graphs with degree distribution that follows a power-law or log-normal distribution (Seshadhri, Pinar, and Kolda, J. ACM 2013). As a result, variants of the SKG model have been proposed to generate graphs which approximately follow degree distributions, without any significant oscillations. But all existing solutions either require significant additional parameterization or have no provable guarantees on important properties, such as the degree distribution. In this work, we analyze a novel and efficient set of models, Relative Prime Stochastic Kronecker Graphs (RPSKG), which can get rid of oscillations in the degree distribution by mixing initiator matrices of relative prime dimensions without additional parameters. The use of 3x3 initiator matrices alone cannot eliminate significant oscillations. We prove that such matrices result in degree distribution that is bounded above by an exponential tail and thus cannot result in a power-law or log-normal.
Large Language Models: Architecture and Training. From Next-Word Prediction to Reasoning
Authors: Jay Alammar
Venue: KDD
Year: 2025

This talk is a highly visual and accessible look at large language models, their architecture, and their training. Attendees will be presented with the intuitions for tens of LLM concepts like tokenizers, the internals of the latest Transformer neural networks, mixture-of-expert models, reward models, reasoning LLMs, model merging, and more.
Hypergraph Motif Representation Learning
Authors: Alessia Antelmi, Gennaro Cordasco, Daniele De Vinco, Valerio Di Pasquale, Mirko Polato, Carmine Spagnuolo
Venue: KDD
Year: 2025

Hypergraphs have emerged as a powerful tool for representing high-order connections in real-world complex systems. Similar to graphs, local structural patterns in hypergraphs, known as high-order motifs (h-motifs), play a crucial role in network dynamics and serve as fundamental building blocks across various domains. For this reason, predicting h-motifs can be highly beneficial in different fields. In this paper, we aim to advance our understanding of such complex high-order dynamics by introducing and formalizing the problem of h-motifs prediction. To address this task, we propose a novel solution that leverages both high-order and pairwise information by combining hypergraph and graph convolutions to capture hyperedges correlation within h-motifs, along with an innovative negative sampling approach designed to generate close-to-positive negative samples. To evaluate the effectiveness of our approach, we defined several baselines inspired by existing literature on hyperedge prediction methods. Our extensive experimental assessments demonstrate that our approach consistently outperforms all the considered baselines, showcasing its superior performance and robustness in predicting h-motifs.