OpenRaiser/Intern-Atlas huggingface dataset:
Recommending Inferior Results: A General and Feature-Free Model for Spam Detection
Authors: Yuli Liu
Venue: Conference CIKM 2020
Year: 2020

Spam activities on multifarious online platforms, such as the opinion spam and fake following relationships have been extensively studied for years. Existing works separately employ hand-crafted features --- mainly extracted from user behavior, text information, and relational network, to detect the specific spamming phenomenon on a certain kind of online platform. Although these attempts have made some headway, rapidly emerging spamming categories and frequently changing cheating strategies lead detection models to be subject to circumscribed usability and fragile effectiveness. This paper is the first attempt to develop a general and feature-free fraud detection model, tackling the longstanding and thorny challenges in spam detection area. To achieve this, we first transform diverse relational networks that are contaminated by fraudsters into the unified matrix form. We then deal with the spam problem from a fresh perspective inspired by the pairwise learning thought in the area of recommender system. By comparing pairwise ranking relations of all the entities in the unified matrix, a new pairwise loss objective function is formulated to identify instances that occupy higher rankings as inferior (spamming) results. To further boost detection performance, we incorporate the pairwise ranking detection method and the widely used structure-based algorithm into an integrated framework. Experiments on real-word datasets of different Web applications show significant improvements of our proposed framework over competitive methods.
Geographic and Temporal Trends in Fake News Consumption During the 2016 US Presidential Election
Authors: Adam Fourney, Miklós Z. Rácz, Gireeja Ranade, Markus Mobius, Eric Horvitz
Venue: Conference CIKM 2017
Year: 2017

We present an analysis of traffic to websites known for publishing fake news in the months preceding the 2016 US presidential election. The study is based on the combined instrumentation data from two popular desktop web browsers: Internet Explorer 11 and Edge. We find that social media was the primary outlet for the circulation of fake news stories and that aggregate voting patterns were strongly correlated with the average daily fraction of users visiting websites serving fake news. This correlation was observed both at the state level and at the county level, and remained stable throughout the main election season. We propose a simple model based on homophily in social networks to explain the linear association. Finally, we highlight examples of different types of fake news stories: while certain stories continue to circulate in the population, others are short-lived and die out in a few days.
Graph-based approach for studying spread of radical online sentiment
Authors: Le Nguyen, Nidhi Rastogi
Venue: Conference WWW 2023
Year: 2023

The spread of radicalization through the Internet is a growing problem. We are witnessing a rise in online hate groups, inspiring the impressionable and vulnerable population towards extreme actions in the real world. In this paper, we study the spread of hate sentiments in online forums by collecting 1,973 long comment threads (30+ comments per thread) posted on dark-web forums and containing a combination of benign posts and radical comments on the Islamic religion. This framework allows us to leverage network analysis tools to investigate sentiment propagation through a social network. By combining sentiment analysis, social network analysis, and graph theory, we aim to shed light on the propagation of hate speech in online forums and the extent to which such speech can influence individuals. The results of the intra-thread analysis suggests that sentiment tends to cluster within comment threads, with around 75% of connected members sharing similar sentiments. They also indicate that online forums can act as echo chambers where people with similar views reinforce each other’s beliefs and opinions. On the other hand, the inter-thread shows that 64% of connected threads share similar sentiments, suggesting similarities between the ideologies present in different threads and that there likely is a wider network of individuals spreading hate speech across different forums. Finally, we plan to study this work with a larger dataset, which could provide further insights into the spread of hate speech in online forums and how to mitigate it.
Higpt: Heterogeneous graph language model
Authors: Jiabin Tang, Yuhao Yang 0002, Wei Wei 0027, Lei Shi, Long Xia, Dawei Yin 0001, Chao Huang 0001
Venue: Conference KDD 2024
Year: 2024

Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for nodes and edges. Recent advancements in heterogeneous graph neural networks (HGNNs) have achieved state-of-the-art performance by considering relation heterogeneity and using specialized message functions and aggregation rules. However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets. Most of these frameworks follow the "pre-train" and "fine-tune" paradigm on the same dataset, which restricts their capacity to adapt to new and unseen data. This raises the question: "Can we generalize heterogeneous graph models to be well-adapted to diverse downstream learning tasks with distribution shifts in both node token sets and relation type heterogeneity?" To tackle those challenges, we propose HiGPT, a general large graph model with <u>H</u>eterogeneous graph <u>i</u>nstruction-tuning paradigm. Our framework enables learning from arbitrary heterogeneous graphs without the need for any fine-tuning process from downstream datasets. To handle distribution shifts in heterogeneity, we introduce an in-context heterogeneous graph tokenizer that captures semantic relationships in different heterogeneous graphs, facilitating model adaptation. We incorporate a large corpus of heterogeneity-aware graph instructions into our HiGPT, enabling the model to effectively comprehend complex relation heterogeneity and distinguish between various types of graph tokens. Furthermore, we introduce the Mixture-of-Thought (MoT) instruction augmentation paradigm to mitigate data scarcity by generating diverse and informative instructions. Through comprehensive evaluations conducted in various settings, our proposed framework demonstrates exceptional performance in terms of generalization performance, surpassing current leading benchmarks. We make our model implementation openly available, along with comprehensive details at: https://github.com/HKUDS/HiGPT.
Informative feature selection for object recognition via sparse PCA
Authors: Nikhil Naikal, Allen Y. Yang, Shankar Sastry 0001
Venue: Conference ICCV 2011
Year: 2011

Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g., SIFT) to form visual dictionaries and subsequent histogram vectors to represent object images in the recognition process. The accuracy of the BoW classifiers, however, is often limited by the presence of uninformative features extracted from the background or irrelevant image segments. Most existing solutions to prune out uninformative features rely on enforcing pairwise epipolar geometry via an expensive structure-from-motion (SfM) procedure. Such solutions are known to break down easily when the camera transformation is large or when the features are extracted from low-resolution, low-quality images. In this paper, we propose a novel method to select informative object features using a more efficient algorithm called Sparse PCA. First, we show that using a large-scale multiple-view object database, informative features can be reliably identified from a highdimensional visual dictionary by applying Sparse PCA on the histograms of each object category. Our experiment shows that the new algorithm improves recognition accuracy compared to the traditional BoW methods and SfM methods. Second, we present a new solution to Sparse PCA as a semidefinite programming problem using the Augmented Lagrangian Method. The new solver outperforms the state of the art for estimating sparse principal vectors as a basis for a low-dimensional subspace model.
Addressing missing data issue for diffusion-based recommendation
Authors: Wenyu Mao, Zhengyi Yang, Jiancan Wu, Haozhe Liu, Yancheng Yuan, Xiang Wang, Xiangnan He
Venue: Conference SIGIR 2025
Year: 2025

Diffusion models have shown significant potential in generating oracle items that best match user preference with guidance from user historical interaction sequences. However, the quality of guidance is often compromised by unpredictable missing data in observed sequence, leading to suboptimal item generation. Since missing data is uncertain in both occurrence and content, recovering it is impractical and may introduce additional errors. To tackle this challenge, we propose a novel dual-side Thompson sampling-based Diffusion Model (TDM), which simulates extra missing data in the guidance signals and allows diffusion models to handle existing missing data through extrapolation. To preserve user preference evolution in sequences despite extra missing data, we introduce Dual-side Thompson Sampling to implement simulation with two probability models, sampling by exploiting user preference from both item continuity and sequence stability. TDM strategically removes items from sequences based on dual-side Thompson sampling and treats these edited sequences as guidance for diffusion models, enhancing models' robustness to missing data through consistency regularization. Additionally, to enhance the generation efficiency, TDM is implemented under the denoising diffusion implicit models to accelerate the reverse process. Extensive experiments and theoretical analysis validate the effectiveness of TDM in addressing missing data in sequential recommendations.
Learnable prompt as pseudo-imputation: Rethinking the necessity of traditional ehr data imputation in downstream clinical prediction
Authors: Weibin Liao, Yinghao Zhu, Zhongji Zhang, Yuhang Wang, Zixiang Wang, Xu Chu, Yasha Wang, Liantao Ma
Venue: Conference KDD 2025
Year: 2025

Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient's health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models. The code of this work is deployed publicly available at https://github.com/MrBlankness/PAI to help the research community reproduce the results and assist the EHR analysis tasks.
Fast and accurate text classification: Skimming, rereading and early stopping
Authors: Keyi Yu, Yang Liu 0097, Alexander G. Schwing, Jian Peng 0001
Venue: Conference ICLR 2018
Year: 2018

Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing. For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision. We argue that reading the entire input is not always necessary in practice, since a lot of reviews are often easy to classify, i.e., a decision can be formed after reading some crucial sentences or words in the provided text. In this paper, we present an approach of fast reading for text classification. Inspired by several well-known human reading techniques, our approach implements an intelligent recurrent agent which evaluates the importance of the current snippet in order to decide whether to make a prediction, or to skip some texts, or to re-read part of the sentence. Our agent uses an RNN module to encode information from the past and the current tokens, and applies a policy module to form decisions. With an end-to-end training algorithm based on policy gradient, we train and test our agent on several text classification datasets and achieve both higher efficiency and better accuracy compared to previous approaches.
Interactive spatial keyword querying with semantics
Authors: Jiabao Sun, Jiajie Xu, Kai Zheng, Chengfei Liu
Venue: Conference CIKM 2017
Year: 2017

Conventional spatial keyword queries confront the difficulty of returning desired objects that are synonyms but morphologically different to query keywords. To overcome this flaw, this paper investigates the interactive spatial keyword querying with semantics. It aims to enhance the conventional queries by not only making sense of the query keywords, but also refining the understanding of query semantics through interactions. On top of the probabilistic topic model, a novel interactive strategy is proposed to precisely infer the latent query semantics by learning from user feedbacks. In each interaction, the returned objects are carefully selected to ensure effective inference of user intended query semantics. Query processing is carried out on a small candidate object set at each round of interaction, and the whole querying process terminates when the latent query semantics learned from user feedback becomes explicit enough. The experimental results on real check-in dataset demonstrates that the quality of results has been significantly improved through limited number of interactions.
A Diffusion-based method for learning the multi-outcome distribution of medical treatments
Authors: Yuchen Ma, Jonas Schweisthal, Hengrui Zhang, Stefan Feuerriegel
Venue: Conference KDD 2025
Year: 2025

In medicine, treatments often influence multiple, interdependent outcomes, such as primary endpoints, complications, adverse events, or other secondary endpoints. Hence, to make optimal treatment decisions, clinicians are interested in learning the distribution of multi-dimensional treatment outcomes. However, the vast majority of machine learning methods for predicting treatment effects focus on single-outcome settings, despite the fact that medical data often include multiple, interdependent outcomes. To address this limitation, we propose a novel diffusion-based method called DIME to learn the joint distribution of multiple outcomes of medical treatments. Our DIME method addresses three challenges relevant in medical practice: (i) our method is tailored to learn the joint interventional distribution of multiple medical outcomes, which enables reliable decision-making with uncertainty quantification rather than relying solely on point estimates; (ii) our method explicitly captures the dependence structure between outcomes; and (iii) our method can handle outcomes of mixed type, including binary, categorical, and continuous variables. In our method, we take into account the fundamental problem of causal inference, where only outcomes for the assigned treatment are observed, through causal masking. For training, our method decomposes the joint distribution into a series of conditional distributions with a customized conditional masking to account for the dependence structure across outcomes. For inference, our method auto-regressively generates predictions. This allows our method to move beyond point estimates of causal quantities and thus learn the joint interventional distribution. To the best of our knowledge, DIME is the first neural method tailored to learn the joint, multi-outcome distribution of medical treatments. Across various experiments, we demonstrate that our method effectively learns the joint distribution and captures shared information among multiple outcomes.
Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space
Authors: Ruikun Li 0002, Huandong Wang, Jinghua Piao, Qingmin Liao, Yong Li 0008
Venue: Conference KDD 2024
Year: 2024

Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future states of nodes on networks, the capability of capturing long-term dynamics remains largely limited. This is because they overlook the fact that long-term dynamics in complex network are predominantly governed by their inherent low-dimensional manifolds, i.e., skeletons. Therefore, we propose the Dynamics-Invariant Skeleton Neural Net}work (DiskNet), which identifies skeletons of complex networks based on the renormalization group structure in hyperbolic space to preserve both topological and dynamics properties. Specifically, we first condense complex networks with various dynamics into simple skeletons through physics-informed hyperbolic embeddings. Further, we design graph neural ordinary differential equations to capture the condensed dynamics on the skeletons. Finally, we recover the skeleton networks and dynamics to the original ones using a degree-based super-resolution module. Extensive experiments across three representative dynamics as well as five real-world and two synthetic networks demonstrate the superior performances of the proposed DiskNet, which outperforms the state-of-the-art baselines by an average of 10.18\% in terms of long-term prediction accuracy. Code for reproduction is available at: https://github.com/tsinghua-fib-lab/DiskNet.
Predicting the dynamics of complex system via multiscale diffusion autoencoder
Authors: Ruikun Li, Jingwen Cheng, Huandong Wang, Qingmin Liao, Yong Li
Venue: Conference KDD 2025
Year: 2025

Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the intrinsic dynamics. While existing methods capture key dynamics by encoding a low-dimensional latent space, they overlook the inherent multiscale structure of complex systems, making it difficult to accurately predict complex spatiotemporal evolution. Therefore, we propose a Multiscale Diffusion Prediction Network (MDPNet) that leverages the multiscale structure of complex systems to discover the latent space of intrinsic dynamics. First, we encode multiscale features through a multiscale diffusion autoencoder to guide the diffusion model for reliable reconstruction. Then, we introduce an attention-based graph neural ordinary differential equation to model the co-evolution across different scales. Extensive evaluations on representative systems demonstrate that the proposed method achieves an average prediction error reduction of 53.23% compared to baselines, while also exhibiting superior robustness and generalization.
Learning slow and fast system dynamics via automatic separation of time scales
Authors: Ruikun Li 0002, Huandong Wang, Yong Li 0008
Venue: Conference KDD 2023
Year: 2023

Learning the underlying slow and fast dynamics of a system is instrumental for many practical applications related to the system. However, existing approaches are limited in discovering the appropriate time scale to separate the slow and fast variables and effectively learning their dynamics based on correct-dimensional representation vectors. In this paper, we introduce a framework that effectively learns slow and fast system dynamics in an integrated manner. We propose a novel intrinsic dimensionality (ID) driven learning method based on a time-lagged autoencoder framework to identify appropriate time scales to separate slow and fast variables and their IDs simultaneously. Further, we propose an integrated framework to concurrently learn the system's slow and fast dynamics, which is able to integrate prior knowledge of time scale and IDs and model the complex coupled slow and fast variables. Extensive experimental results on two representative dynamical systems show that our proposed framework is able to efficiently learn slow and fast system dynamics. Specifically, the long-time prediction performance is able to be improved by 36% on average compared with four representative baselines based on our proposed framework. Furthermore, our proposed system is able to extract interpretable slow and fast dynamics highly correlated with the known slow and fast variables in the dynamical systems. Our codes and datasets are open-sourced at: https://github.com/tsinghua-fib-lab/SlowFastSeparation.
Process-supervised llm recommenders via flow-guided tuning
Authors: Chongming Gao, Mengyao Gao, Chenxiao Fan, Shuai Yuan, Wentao Shi, Xiangnan He
Venue: Conference SIGIR 2025
Year: 2025

While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likelihood maximization objective, compromising recommendation diversity and fairness. To address this, we present Flow-guided fine-tuning recommender (Flower), which replaces SFT with a Generative Flow Network (GFlowNet) [6] framework that enacts process supervision through token-level reward propagation. Flower's key innovation lies in decomposing item-level rewards into constituent token rewards, enabling direct alignment between token generation probabilities and their reward signals. This mechanism achieves three critical advancements: (1) popularity bias mitigation and fairness enhancement through empirical distribution matching (2) preservation of diversity through GFlowNet's proportional sampling, and (3) flexible integration of personalized preferences via adaptable token rewards. Experiments demonstrate Flower's superior distribution-fitting capability and its significant advantages over traditional SFT in terms of accuracy, fairness, and diversity, highlighting its potential to improve LLM-based recommendation systems. The implementation is available via https://github.com/MrPeach0301/Flower.