OpenRaiser/Intern-Atlas huggingface dataset:
A new probabilistic model for top-k ranking problem
Authors: Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng
Venue: Conference CIKM 2012
Year: 2012

This paper is concerned with top-k ranking problem, which reflects the fact that people pay more attention to the top ranked objects in real ranking application like information retrieval. A popular approach to top-k ranking problem is based on probabilistic models, such as Luce model and Mallows model. However, whether the sequential generative process described in these models is a suitable way for top-k ranking remains a question. According to the riffled independence factorization proposed in recent literature, which is a natural structural assumption on top-k ranking, we propose a new generative process of top-k ranking data. Our approach decomposes distributions over the top-k ranking into two layers: the first layer describes the relative ordering between the top k objects and the rest n-k objects, and the second layer describes the full ordering on the top k objects. On this basis, we propose a new probabilistic model for top-k ranking problem, called hierarchical ordering model. Specifically, we use three different probabilistic models to describe different generative processes of the first layer, and Luce model to describe the sequential generative process of the second layer, thus we obtain three different specific hierarchical ordering models. We also conduct extensive experiments on benchmark datasets to show that our proposed models can outperform previous models significantly.
Adaptive graph enhancement for imbalanced multi-relation graph learning
Authors: Yiyue Qian, Tianyi Ma, Chuxu Zhang, Yanfang Ye
Venue: Conference WSDM 2025
Year: 2025

Graph Neural Networks (GNNs), as the mainstream graph representation learning method, has demonstrated its effectiveness in learning graph embeddings over benchmark datasets. However, existing GNNs still have limitations in handling real-world graphs in the following aspects: (i) nodes in most real-world graphs are inherently class-imbalanced; (ii) node degrees vary considerably in real-world graphs; (iii) most existing works study these two issues separately but ignore the co-occurrence between class imbalance and topology imbalance in graphs. They overlook the fact that topology imbalance varies significantly across different relation types. Hence, we propose a novel model called AD-GSMOTE (Adaptive Graph SMOTE) to tackle the class and topology issues simultaneously in multi-relation graphs. Specifically, we first design an adaptive topology-aware node generator and an efficient triadic edge generator to enhance the graph structure under each relation type by generating synthetic nodes for all tail nodes in minority classes and creating rich connections among tail nodes and others. Then, the enhanced multi-relation graph is fed into a GNN encoder to get node embeddings. Afterward, a class-aware logit adjustment module is designed to adjust the pre-softmax logit during model training, which enables the model to learn larger margins between minority and majority classes. To evaluate the performance of AD-GSMOTE, we build a new real-world graph (Twitter-Drug) to classify user roles in the drug trafficking community. The excellent performance on three real-world graphs demonstrates the effectiveness and efficiency of AD-GSMOTE compared with state-of-the-art methods. Source code and dataset are available at https://github.com/graphprojects/AD-GSMOTE https://github.com/graphprojects/AD-GSMOTE.
Synthesis of forgiving data extractors
Authors: Adi Omari, Sharon Shoham, Eran Yahav
Venue: Conference WSDM 2017
Year: 2017

We address the problem of synthesizing a robust data-extractor from a family of websites that contain the same kind of information. This problem is common when trying to aggregate information from many web sites, for example, when extracting information for a price-comparison site.
Rethinking and simplifying bootstrapped graph latents
Authors: Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng
Venue: Conference WSDM 2024
Year: 2024

Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations. Recent studies have shown that GCL without negative samples can achieve state-of-the-art performance as well as scalability improvement, with bootstrapped graph latent (BGRL) as a prominent step forward. However, BGRL relies on a complex architecture to maintain the ability to scatter representations, and the underlying mechanisms enabling the success remain largely unexplored. In this paper, we introduce an instance-level decorrelation perspective to tackle the aforementioned issue and leverage it as a springboard to reveal the potential unnecessary model complexity within BGRL. Based on our findings, we present SGCL, a simple yet effective GCL framework that utilizes the outputs from two consecutive iterations as positive pairs, eliminating the negative samples. SGCL only requires a single graph augmentation and a single graph encoder without additional parameters. Extensive experiments conducted on various graph benchmarks demonstrate that SGCL can achieve competitive performance with fewer parameters, lower time and space costs, and significant convergence speedup.
Str-gcl: Structural commonsense driven graph contrastive learning
Authors: Dongxiao He, Yongqi Huang, Jitao Zhao, Xiaobao Wang, Zhen Wang
Venue: Conference WWW 2025
Year: 2025

Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations. However, current GCL methods primarily focus on capturing implicit semantic relationships, often overlooking the structural commonsense embedded within the graph's structure and attributes, which contains underlying knowledge crucial for effective representation learning. Due to the lack of explicit information and clear guidance in general graph, identifying and integrating such structural commonsense in GCL poses a significant challenge. To address this gap, we propose a novel framework called Structural Commonsense Unveiling in Graph Contrastive Learning (Str-GCL). Str-GCL leverages first-order logic rules to represent structural commonsense and explicitly integrates them into the GCL framework. It introduces topological and attribute-based rules without altering the original graph and employs a representation alignment mechanism to guide the encoder in effectively capturing this commonsense. To the best of our knowledge, this is the first attempt to directly incorporate structural commonsense into GCL. Extensive experiments demonstrate that Str-GCL outperforms existing GCL methods, providing a new perspective on leveraging structural commonsense in graph representation learning.
Relief: Reinforcement learning empowered graph feature prompt tuning
Authors: Jiapeng Zhu, Zichen Ding, Jianxiang Yu, Jiaqi Tan, Xiang Li, Weining Qian
Venue: Conference KDD 2025
Year: 2025

The advent of the "pre-train, prompt'' paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP). Initial graph prompt tuning approaches tailored specialized prompting functions for Graph Neural Network (GNN) models pre-trained with specific strategies, such as edge prediction, thus limiting their applicability. In contrast, another pioneering line of research has explored universal prompting via adding prompts to the input graph's feature space, thereby removing the reliance on specific pre-training strategies. However, the necessity to add feature prompts to all nodes remains an open question. Motivated by findings from prompt tuning research in the NLP domain, which suggest that highly capable pre-trained models need less conditioning signal to achieve desired behaviors, we advocate for strategically incorporating necessary and lightweight feature prompts to certain graph nodes to enhance downstream task performance. This introduces a combinatorial optimization problem, requiring a policy to decide 1) which nodes to prompt and 2) what specific feature prompts to attach. We then address the problem by framing the prompt incorporation process as a sequential decision-making problem and propose our method, RELIEF, which employs Reinforcement Learning (RL) to optimize it. At each step, the RL agent selects a node (discrete action) and determines the prompt content (continuous action), aiming to maximize cumulative performance gain. Extensive experiments on graph and node-level tasks with various pre-training strategies in few-shot scenarios demonstrate that our RELIEF outperforms fine-tuning and other prompt-based approaches in classification performance and data efficiency. The code is available at https://github.com/JasonZhujp/RELIEF.
Embracing plasticity: Balancing stability and plasticity in continual recommender systems
Authors: Hyunsik Yoo, SeongKu Kang, Ruizhong Qiu, Charlie Xu, Fei Wang, Hanghang Tong
Venue: Conference SIGIR 2025
Year: 2025

In the era of big data and AI, recommender systems must adapt to evolving user preferences and new users/items to maintain high-quality recommendations. Fine-tuning, which updates model parameters using only new data, offers an efficient alternative to full retraining but struggles to balance stability (retaining past knowledge) and plasticity (adapting to new knowledge). While existing methods prioritize stability to address catastrophic forgetting, we argue that plasticity must also be explicitly strengthened, especially for users with rapidly changing preferences. In this work, we propose PlastIcity and StAbility balancing continual recommender systems (PISA), a novel framework that adaptively balances stability and plasticity based on user preference shifts. PISA quantifies preference shifts as changes in user distances to item clusters, and then guides user embeddings by prioritizing stability for stable users and plasticity for dynamic users. To achieve this, PISA leverages backward knowledge from the previous model and forward knowledge from fine-tuning on current data. During training, PISA maximizes mutual information between user-specific parameters and the relevant reference knowledge. Theoretically, we show that enhancing plasticity mitigates distribution shifts more effectively than fine-tuning alone. Empirically, extensive experiments on three real-world datasets validate PISA's superiority over existing methods and highlight the contributions of its components.
Generalizable recommender system during temporal popularity distribution shifts
Authors: Hyunsik Yoo, Ruizhong Qiu, Charlie Xu, Fei Wang, Hanghang Tong
Venue: Conference KDD 2025
Year: 2025

Many modern recommender systems represent user and item attributes as embedding vectors, relying on them for accurate recommendations. However, entangled embeddings often capture not only intrinsic property factors (e.g., user interest in item property) but also popularity factors (e.g., user conformity to item popularity) indistinguishably. These embeddings, influenced by popularity distribution, may face challenges when the popularity distribution at test time differs from historical distribution. Existing remedies in the literature involve disentangled embedding learning, which aims to separately capture intrinsic and popularity factors, demonstrating plausible generalization during popularity distribution shifts. However, we highlight that these methods often overlook a crucial aspect of popularity shifts-their temporal nature-in both training and inference phases. To address this, we propose Temporal Popularity distribution shift generalizABle recommender system (TPAB), a novel disentanglement framework incorporating temporal popularity. TPAB introduce a new (1) temporal-aware embedding design for users and items. Within this design, (2) popularity coarsening and (3) popularity bootstrapping are proposed to enhance generalization further. We also provide theoretical analysis showing that the bootstrapping loss eliminates the effect of popularity on the learned model. During inference, we infer test-time popularity and corresponding embeddings, using them alongside property embeddings for prediction. Extensive experiments on real-world datasets validate TPAB, showcasing its outstanding generalization ability during temporal popularity distribution shifts.
Graph learning with distributional edge layouts
Authors: Xinjian Zhao, Chaolong Ying, Yaoyao Xu, Tianshu Yu
Venue: Conference KDD 2025
Year: 2025

Graph Neural Networks (GNNs) learn from graph-structured data by passing messages between neighboring nodes along edges on certain topological layouts. While layouts can be essential to GNNs' performance, extant methods generally consider obtaining layouts from limited perspectives. In this paper, we introduce Distributional Edge Layouts (DELs), a first-of-its-kind method to sample a collection of topological layouts from a Boltzmann distribution under physical energies. By integrating DELs into GNNs, a wide landscape of feasible graph layouts can be captured from a holistic perspective, overcoming the intrinsic drawbacks in existing GNN designs.In practice, DELs can complement various GNN architectures with high versatility. Our theoretical analysis proves that GNNs equipped with DELs maintain at least the same expressive as their original counterparts, with empirical potential offering extra expressivity. Extensive experiments demonstrate that DELs consistently and substantially improve the performance of a wide range of GNN baselines across multiple datasets, achieving state-of-the-art results. This improvement suggests that DELs capture important distributional information previously overlooked by traditional GNN approaches. DEL is open-sourced at https://github.com/LOGO-CUHKSZ/DEL.
Memengine: A unified and modular library for developing advanced memory of llm-based agents
Authors: Zeyu Zhang, Quanyu Dai, Xu Chen, Rui Li, Zhongyang Li, Zhenhua Dong
Venue: Conference WWW 2025
Year: 2025

Recently, large language model based (LLM-based) agents have been widely applied across various fields. As a critical part, their memory capabilities have captured significant interest from both industrial and academic communities. Despite the proposal of many advanced memory models in recent research, however, there remains a lack of unified implementations under a general framework. To address this issue, we develop a unified and modular library for developing advanced memory models of LLM-based agents, called MemEngine. Based on our framework, we implement abundant memory models from recent research works. Additionally, our library facilitates convenient and extensible memory development, and offers user-friendly and pluggable memory usage. For benefiting our community, we have made our project publicly available at https://github.com/nuster1128/MemEngine.
An observed value consistent diffusion model for imputing missing values in multivariate time series
Authors: Xu Wang 0029, Hongbo Zhang, Pengkun Wang 0001, Yudong Zhang 0005, Binwu Wang, Zhengyang Zhou, Yang Wang 0015
Venue: Conference KDD 2023
Year: 2023

Missing values, which are common in multivariate time series, is most important obstacle towards the utilization and interpretation of those data. Great efforts have been employed on how to accurately impute missing values in multivariate time series, and existing works either use deep learning networks to achieve deterministic imputations or aim at generating different plausible imputations by sampling multiple noises from a same distribution and then denoising them. However, these models either fall short of modeling the uncertainties of imputations due to their deterministic nature or perform poorly in terms of interpretability and imputation accuracy due to their ignorance of the correlations between the latent representations of both observed and missing values which are parts of samples from a same distribution. To this end, in this paper, we explicitly take the correlations between observed and missing values into account, and theoretically re-derive the Evidence Lower BOund (ELBO) of conditional diffusion model in the scenario of multivariate time series imputation. Based on the newly derived ELBO, we further propose a novel multivariate imputation diffusion model (MIDM) which is equipped with novel noise sampling, adding and denoising mechanisms for multivariate time series imputation, and the series of newly designed technologies jointly ensure the involving of the consistency between observed and missing values. Extensive experiments on both the tasks of multivariate time series imputation and forecasting witness the superiority of our proposed MIDM model on generating conditional estimations.
Continuous-time autoencoders for regular and irregular time series imputation
Authors: Hyowon Wi, Yehjin Shin, Noseong Park
Venue: Conference WSDM 2024
Year: 2024

Time series imputation is one of the most fundamental tasks for time series. Real-world time series datasets are frequently incomplete (or irregular with missing observations), in which case imputation is strongly required. Many different time series imputation methods have been proposed. Recent self-attention-based methods show the state-of-the-art imputation performance. However, it has been overlooked for a long time to design an imputation method based on continuous-time recurrent neural networks (RNNs), i.e., neural controlled differential equations (NCDEs). To this end, we redesign time series (variational) autoencoders based on NCDEs. Our method, called continuous-time autoencoder (CTA), encodes an input time series sample into a continuous hidden path (rather than a hidden vector) and decodes it to reconstruct and impute the input. In our experiments with 4 datasets and 19 baselines, our method shows the best imputation performance in almost all cases.
Traffic speed imputation with spatio-temporal attentions and cycle-perceptual training
Authors: Qianxiong Xu, Sijie Ruan, Cheng Long, Liang Yu, Chen Zhang
Venue: Conference CIKM 2022
Year: 2022

The phenomena of data missing are common in the field of traffic, yet existing solutions for data imputation are not sufficient due to challenges of data sparsity, complex traffic situations and the lack of complete ground truths. In this paper, we propose a novel solution called STCPA for the speed imputation problem. STCPA captures complex traffic correlations among the spatial and temporal dimensions via the attention mechanism, which helps mitigate the data sparsity issue. In addition, STCPA adopts an imputation cycle consistency constraint for providing reliable supervisions on unobserved entries, which improves the training. Furthermore, it incorporates an extra Road-aware Perceptual Loss, which helps encourage to preserve more meaningful semantics for imputation. Extensive experiments are conducted on two real-world datasets, namely, Chengdu and New York, to demonstrate the effectiveness of STCPA, e.g., it outperforms the best baseline by 7.64% and 5.00% on Chengdu and New York datasets, respectively. The code is available at https://github.com/Sam1224/STCPA.
Towards a framework for context-aware intelligent traffic management system in smart cities
Authors: Zeenat Rehena, Marijn Janssen
Venue: Conference WWW 2018
Year: 2018

In the last few years, the smart city concept resulted in the development and deployment of platforms for providing innovative services to improve sustainability and the living standards. These platforms integrate data collected from devices and citizen-generated data and thereafter employ big data analytics to create insights from the data. These platform enable the creation of context-aware Intelligent traffic management systems (ITMS), however the involvement of various actors at different stages hampers development. In this paper, we propose a framework to support sustainable traffic management system for providing better commute, safety and security during travel based on real-time information. The framework should help to integrate the activities performed by the various actors. The main key elements of this framework are Datasets, Traffic Management Analytics, Actors and Actions which are taken by these users. The framework helps to create an overall overview of the activities needed. In this way it can be used to improve the quality of the traffic flow, increase efficient use of resources, smooth and safe commute of the citizens.
Improving synthetic image detection towards generalization: An image transformation perspective
Authors: Ouxiang Li, Jiayin Cai, Yanbin Hao, Xiaolong Jiang, Yao Hu, Fuli Feng
Venue: Conference KDD 2025
Year: 2025

With recent generative models facilitating photo-realistic image synthesis, the proliferation of synthetic images has also engendered certain negative impacts on social platforms, thereby raising an urgent imperative to develop effective detectors. Current synthetic image detection (SID) pipelines are primarily dedicated to crafting universal artifact features, accompanied by an oversight about SID training paradigm. In this paper, we re-examine the SID problem and identify two prevalent biases in current training paradigms, i.e., weakened artifact features and overfitted artifact features. Meanwhile, we discover that the imaging mechanism of synthetic images contributes to heightened local correlations among pixels, suggesting that detectors should be equipped with local awareness. In this light, we propose SAFE, a lightweight and effective detector with three simple image transformations. Firstly, for weakened artifact features, we substitute the down-sampling operator with the crop operator in image pre-processing to help circumvent artifact distortion. Secondly, for overfitted artifact features, we include ColorJitter and RandomRotation as additional data augmentations, to help alleviate irrelevant biases from color discrepancies and semantic differences in limited training samples. Thirdly, for local awareness, we propose a patch-based random masking strategy tailored for SID, forcing the detector to focus on local regions at training. Comparative experiments are conducted on an open-world dataset, comprising synthetic images generated by 26 distinct generative models. Our pipeline achieves a new state-of-the-art performance, with remarkable improvements of 4.5% in accuracy and 2.9% in average precision against existing methods. Our code is available at: https://github.com/Ouxiang-Li/SAFE.