OpenRaiser/Intern-Atlas huggingface dataset:
Chainlet Orbits: Topological Address Embedding for Blockchain
Authors: Poupak Azad, Baris Coskunuzer, Murat Kantarcioglu, Cuneyt Gurcan Akcora
Venue: KDD
Year: 2025

The rise of cryptocurrencies like Bitcoin has not only increased trade volumes but also broadened the use of graph machine learning techniques, such as address embeddings, to analyze transactions and decipher user patterns. Traditional analysis methods rely on simple heuristics and extensive data gathering, while more advanced Graph Neural Networks encounter challenges such as scalability, poor interpretability, and label scarcity in massive blockchain transaction networks. To overcome existing techniques' computational and interpretability limitations, we introduce a topological approach, Chainlet Orbits, which embeds blockchain addresses by leveraging their topological characteristics in temporal transactions. We employ our innovative address embeddings to investigate financial behavior and e-crime in the Bitcoin and Ethereum networks, focusing on distinctive substructures that arise from user behavior. Our model demonstrates exceptional performance in node classification experiments compared to GNN-based approaches. Furthermore, our approach embeds all daily nodes of the largest blockchain transaction network, Bitcoin, and creates explainable machine learning models in less than 17 minutes which takes days for GNN-based approaches.
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning
Authors: Ehtesamul Azim, Dongjie Wang, Tae Hyun Hwang, Yanjie Fu, Wei Zhang
Venue: KDD
Year: 2025

Gene selection in high-dimensional genomic data is essential for understanding disease mechanisms and improving therapeutic outcomes. Traditional feature selection methods effectively identify predictive genes but often ignore complex biological pathways and regulatory networks, leading to unstable and biologically irrelevant signatures. Prior approaches, such as Lasso-based methods and statistical filtering, either focus solely on individual gene-outcome associations or fail to capture pathway-level interactions, presenting a key challenge: how to integrate biological pathway knowledge while maintaining statistical rigor in gene selection? To address this gap, we propose a novel two-stage framework that integrates statistical selection with biological pathway knowledge using multi-agent reinforcement learning (MARL). First, we introduce a pathway-guided pre-filtering strategy that leverages multiple statistical methods alongside KEGG pathway information for initial dimensionality reduction. Next, for refined selection, we model genes as collaborative agents in a MARL framework, where each agent optimizes both predictive power and biological relevance. Our framework incorporates pathway knowledge through Graph Neural Network-based state representations, a reward mechanism combining prediction performance with gene centrality and pathway coverage, and collaborative learning strategies using shared memory and a centralized critic component. Extensive experiments on multiple gene expression datasets demonstrate that our approach significantly improves both prediction accuracy and biological interpretability compared to traditional methods.
GRAND : Graph Reconstruction from Potential Partial Adjacency and Neighborhood Data
Authors: Sofiane Azogagh, Zelma Aubin Birba, Josée Desharnais, Sébastien Gambs, Marc-Olivier Killijian, Nadia Tawbi
Venue: KDD
Year: 2025

Cryptographic approaches, such as secure multiparty computation, can be used to securely compute a function of a distributed graph without centralizing the data of each participant. However, the output of the protocol can leak sensitive information about the structure of the original graph. In particular, we propose an approach by which an adversary observing the result of a private protocol for the computation of the number of common neighbors between all pairs of vertices, can reconstruct the adjacency matrix of the graph. In fact, this can only be done up to co-squareness, a notion we introduce, as two different graphs can have the same matrix of common neighbors. To realize this, we consider two adversary models, one who observes the common neighbors matrix only and a more informed one that has partial knowledge of the original graph. Our results demonstrate that, from their common neighbors matrix, graphs can be reconstructed with high accuracy (up to co-squareness). The proposed reconstruction is also interesting in itself from the point of view of graph theory.
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
Authors: Anirudhan Badrinath, Alex Yang, Kousik Rajesh, Prabhat Agarwal, Jaewon Yang, Haoyu Chen, Jiajing Xu, Charles Rosenberg
Venue: KDD
Year: 2025

Representation learning, a task of learning latent vectors to represent entities, is a key task in improving search and recommender systems in web applications. Various representation learning methods have been developed, including graph-based approaches for relationships among entities, sequence-based methods for capturing the temporal evolution of user activities, and content-based models for leveraging text and visual content. However, the development of a unifying framework that integrates these diverse techniques to support multiple applications remains a significant challenge. This paper presents OmniSage, a large-scale representation framework that learns universal representations for a variety of applications at Pinterest. OmniSage integrates graph neural networks with content-based models and user sequence models by employing multiple contrastive learning tasks to effectively process graph data, user sequence data, and content signals. To support the training and inference of OmniSage, we developed an efficient infrastructure capable of supporting Pinterest graphs with billions of nodes. The universal representations generated by OmniSage have significantly enhanced user experiences on Pinterest, leading to an approximate 2.5% increase in sitewide repins (saves) across five applications. This paper highlights the impact of unifying representation learning methods, and we make the model code publicly available https://github.com/pinterest/atg-research/tree/main/omnisage.
Responsible AI Day
Authors: Ebrahim Bagheri, Faezeh Ensan, Calvin Hillis, Reihaneh Rabbany, Robin Cohen, Benjamin C. M. Fung, Sébastien Gambs
Venue: KDD
Year: 2025

This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore how data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.
AdKDD 2025
Authors: Abraham Bagherjeiran, Nemanja Djuric, Kuang-chih Lee, Linsey Pang, Vladan Radosavljevic, Suju Rajan
Venue: KDD
Year: 2025

The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website, which hosts slides and abstracts, continues to receive a large number of monthly visits and active users. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2025 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives, such as those enforced by GDPR or CCPA, the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.
Progressive Stacking for Scalable Graph Condensation
Authors: Yibing Bai, Min Gao, Zongwei Wang, Xinyi Gao, Wentao Li
Venue: KDD
Year: 2025

Large-scale graph data has demonstrated significant success in graph representation learning, but the associated high computational cost and inefficiency hinder its widespread adoption across diverse applications. Graph condensation has emerged as a promising solution to reduce time and memory demands while preserving generalization performance comparable to the original graph. Although existing graph condensation methods have proven effective, they are constrained by their reliance on repeatedly optimizing a condensed graph at a fixed scale, which demands significant computational resources and lacks flexibility to accommodate varying training requirements. This motivates us to explore alternative approaches that incrementally refine and expand condensed graphs. To tackle above challenges in graph condensation, this paper presents Progressive Stacking (ProStack), a method that progressively constructs condensed graphs by incrementally expanding their structure and semantics. ProStack utilizes graph memory to retain global structural and feature information from the original graph, facilitating the reuse of essential features and patterns. The method employs a memory-guided dual optimization approach, focusing on structural consistency in the feature generation stage and optimizing the alignment of the label features in the label prediction stage. By stage-wise graph stacking, ProStack progressively refines the condensed graph, ensuring feature coherence and maintaining structural integrity throughout the process. Experimental results demonstrate that ProStack outperforms existing graph condensation techniques in efficiency and effectiveness.
A Unified Solution to Diverse Heterogeneities in One-Shot Federated Learning
Authors: Jun Bai, Yiliao Song, Di Wu, Atul Sajjanhar, Yong Xiang, Wei Zhou, Xiaohui Tao, Yan Li, Yue Li
Venue: KDD
Year: 2025

One-Shot Federated Learning (OSFL) restricts communication between the server and clients to a single round, significantly reducing communication costs and minimizing privacy leakage risks compared to traditional Federated Learning (FL), which requires multiple rounds of communication. However, existing OSFL frameworks remain vulnerable to distributional heterogeneity, as they primarily focus on model heterogeneity while neglecting data heterogeneity. To bridge this gap, we propose FedHydra, a unified, data-free, OSFL framework designed to effectively address both model and data heterogeneity. Unlike existing OSFL approaches, FedHydra introduces a novel two-stage learning mechanism. Specifically, it incorporates model stratification and heterogeneity-aware stratified aggregation to mitigate the challenges posed by both model and data heterogeneity. By this design, the data and model heterogeneity issues are simultaneously monitored from different aspects during learning. Consequently, FedHydra can effectively mitigate both issues by minimizing their inherent conflicts. We compared FedHydra with five SOTA baselines on four benchmark datasets. Experimental results show that our method outperforms the previous OSFL methods in both homogeneous and heterogeneous settings. The code is available at https://github.com/Jun-B0518/FedHydra.
UnG-MoCha: Neural Motif Counting in Uncertain Graphs
Authors: Lujie Ban, Xiaolin Han, Jinyang Li, Chenhao Ma
Venue: KDD
Year: 2025

Motif counting is fundamental in graph analytic tasks (e.g., clustering and recommendation) but #P-hard. Recent research has focused on exploring and applying deep learning-based solutions to tackle this problem. However, these solutions assume a deterministic graph where edge existence is certain, which may not hold due to the measurement and statistical prediction errors. Meanwhile, existing methods for uncertain graphs still face considerable time costs. To address the above issues, we propose UnG-MoCha, a novel deep-learning approach to efficiently count motifs in uncertain graphs. UnG-MoCha extracts representative subgraphs via graph structure learning and learns graph and motif representations using hierarchical and classic graph neural networks, respectively. Canonical correlation analysis is used to exploit correlations between graph and motif representations, boosting accuracy. Experiments on real-world graphs demonstrate UnG-MoCha's superior performance for scalable motif counting on uncertain graphs.
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Authors: Yuanchen Bei, Weizhi Chen, Hao Chen, Sheng Zhou, Carl Ji Yang, Jiapei Fan, Longtao Huang, Jiajun Bu
Venue: KDD
Year: 2025

Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Although a few efforts have been made by utilizing Graph Convolution Networks (GCNs) to learn node representations and model correlations between multiple labels in the embedding space, they still suffer from the ambiguous feature and ambiguous topology induced by multiple labels, which reduces the credibility of the messages delivered in graphs and overlooks the label correlations on graph data. Therefore, it is crucial to reduce the ambiguity and empower the GCNs for accurate classification. However, this is quite challenging due to the requirement of retaining the distinctiveness of each label while fully harnessing the correlation between labels simultaneously. To address these issues, in this paper, we propose a Correlation-aware Graph Convolutional Network (CorGCN) for multi-label node classification. By introducing a novel Correlation-Aware Graph Decomposition module, CorGCN can learn a graph that contains rich label-correlated information for each label. It then employs a Correlation-Enhanced Graph Convolution to model the relationships between labels during message passing to further bolster the classification process. Extensive experiments on five datasets demonstrate the effectiveness of our proposed CorGCN.
RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Authors: Federico Berto, Chuanbo Hua, Junyoung Park, Laurin Luttmann, Yining Ma, Fanchen Bu, Jiarui Wang, Haoran Ye, Minsu Kim, Sanghyeok Choi, Nayeli Gast Zepeda, André Hottung, Jianan Zhou, Jieyi Bi, Yu Hu, Fei Liu, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Davide Angioni, Wouter Kool, Zhiguang Cao, Qingfu Zhang, Joungho Kim, Jie Zhang, Kijung Shin, Cathy Wu, Sungsoo Ahn, Guojie Song, Changhyun Kwon, Kevin Tierney, Lin Xie, Jinkyoo Park
Venue: KDD
Year: 2025

Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation. RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering. We finally provide extensive benchmark studies to inspire new insights and future work. RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co.
Characterizing the Risk of Atrial Fibrillation in Cardiac Patients with Exceptional Electrocardiogram Phenotypes
Authors: Lieke van den Biggelaar, Rianne Margaretha Schouten, Ashley De Bie, R. Arthur Bouwman, Wouter Duivesteijn
Venue: KDD
Year: 2025

We provide a transparent method to characterize Atrial Fibrillation (AF) caused by cardiac surgery, using Electrocardiogram (ECG) phenotypes. Current practice in the hospital is reactive rather than preventive and is based on a third party's proprietary alarms on vitals. Assistance with detection and prediction methods often lacks sufficient insights into their decisions toward the users, i.e., the hospital workers. This aspect is necessary to gain the trust of medical workers and patients in the decisions that are made. Our objective of transparently identifying risk factors for AF helps experts increase their understanding of the problem, and assists in decision-making about administering preventive medication to risk groups. With the deployment of the Exceptional Model Mining (EMM) framework on AF-related ECG phenotypes, we introduce a transparent and actionable method that assists the hospital in preventive treatment. We find several subgroups with EMM that align with known risk factors in the existing literature, confirming the ability of our method to identify risk groups of AF successfully. In addition, new hypotheses on found characteristics and combinations thereof have originated from the deployment. The hospital is advised to administer preventive medications to patients who match the descriptions of the risk groups found and perform follow-up clinical studies to validate the found hypotheses.
Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever
Authors: Xingyan Bin, Jianfei Cui, Wujie Yan, Zhichen Zhao, Xintian Han, Chongyang Yan, Feng Zhang, Xun Zhou, Xiao Yang, Zuotao Liu
Venue: KDD
Year: 2025

Retrievers, which form one of the most important recommendation stages, are responsible for efficiently selecting possible positive samples to the later stages under strict latency limitations. Because of this, large-scale systems always rely on approximate calculations and indexes to roughly shrink candidate scale, with a simple ranking model. Most of the existing methods mainly focus on incorporating complicated ranking models. However, index structure is not improved, which also bottlenecks the whole effectiveness. In this paper, we propose a novel index structure: streaming Vector Quantization model, as a new generation of retrieval paradigm. Streaming VQ attaches items with indexes in real time, granting it immediacy. Moreover, through meticulous verification of possible variants, it achieves additional benefits like index balancing and reparability, enabling it to support complicated ranking models as existing approaches. Streaming VQ has been deployed and replaced all major retrievers in Douyin and Douyin Lite, resulting in remarkable user engagement gain.
Quantizing Text-attributed Graphs for Semantic-Structural Integration
Authors: Jianyuan Bo, Hao Wu, Yuan Fang
Venue: KDD
Year: 2025

Text-attributed graphs (TAGs) have emerged as a powerful representation for modeling complex relationships across diverse domains. With the rise of large language models (LLMs), there is growing interest in leveraging their capabilities for graph learning. However, current approaches face significant challenges in embedding structural information into LLM-compatible formats, requiring either computationally expensive alignment mechanisms or manual graph verbalization techniques that often lose critical structural details. Moreover, these methods typically require labeled data from source domains for effective transfer learning, significantly constraining their adaptability. We propose STAG, a novel self-supervised framework that directly quantizes graph structural information into discrete tokens using a frozen codebook. Unlike traditional quantization approaches, our method employs soft assignment and KL divergence guided quantization to address the unique challenges of graph data, which lacks natural tokenization structures. Our framework enables both LLM-based and traditional learning approaches, supporting true zero-shot transfer learning without requiring labeled data even in the source domain. Extensive experiments demonstrate state-of-the-art performance across multiple node classification benchmarks while maintaining compatibility with different LLM architectures, offering an elegant solution to bridging graph learning with LLMs.
Fast and Effective GNN Training through Sequences of Random Path Graphs
Authors: Francesco Bonchi, Claudio Gentile, Francesco Paolo Nerini, André Panisson, Fabio Vitale
Venue: KDD
Year: 2025

We present GERN, a novel scalable framework for training GNNs in node classification tasks, based on effective resistance, a standard tool in spectral graph theory. Our method progressively refines the GNN weights on a sequence of random spanning trees suitably transformed into path graphs which, despite their simplicity, are shown to retain essential topological and node information of the original input graph. The sparse nature of these path graphs substantially lightens the computational burden of GNN training. This not only enhances scalability but also improves accuracy in subsequent test phases, especially under small training set regimes, which are of great practical importance, as in many real-world scenarios labels may be hard to obtain. In these settings, our framework yields very good results as it effectively counters the training deterioration caused by overfitting when the training set is small. Our method also addresses common issues like over-squashing and over-smoothing while avoiding under-reaching phenomena. Although our framework is flexible and can be deployed in several types of GNNs, in this paper we focus on graph convolutional networks and carry out an extensive experimental investigation on a number of real-world graph benchmarks, where we achieve simultaneous improvement of training speed and test accuracy over a wide pool of representative baselines.