OpenRaiser/Intern-Atlas huggingface dataset:
Diga: Guided diffusion model for graph recovery in anti-money laundering
Authors: Xujia Li, Yuan Li, Xueying Mo, Hebing Xiao, Yanyan Shen, Lei Chen 0002
Venue: Conference KDD 2023
Year: 2023

With the upsurge of online banking, mobile payment, and virtual currency, new money-laundering crimes easily conceal in the enormous transaction volume. The traditional rule-based methods with large amounts of alerting thresholds are already incapable of handling the fast-changing transaction networks. Recently, the DL models represented by the graph neural networks (GNNs) show the potential to capture money-laundering modes with high accuracy. However, most related works are still far from practical deployment in the industry. Based on our practice at WeBank, there are three major challenges: Firstly, supervised learning is infeasible facing the extraordinarily large-scale but imbalanced data, with hundreds of millions of active accounts but only thousands of anomalies. Secondly, the real-world transactions form a sparse network with millions of isolated user groups, which overflows the expressive ability of current node-level GNNs. Thirdly, the explanation for each suspicious account is mandatory by the government for double check, which conflicts with the black-box nature of most DL models. Therefore, we proposed Diga, the first work to apply the diffusion probabilistic model to a graph anomaly detection problem with three novel techniques: the biased K-hop PageRank, the semi-supervised guided diffusion and the novel weight-sharing GNN layer. The effectiveness and efficiency of Diga are verified via intensive experiments on both industrial and public datasets.
Eeg2rep: enhancing self-supervised eeg representation through informative masked inputs
Authors: Navid Mohammadi Foumani, Geoffrey Mackellar, Soheila Ghane, Saad Irtza, Nam Nguyen, Mahsa Salehi
Venue: Conference KDD 2024
Year: 2024

Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks the models to be dominated by higher amplitude ranges, and (3) The absence of explicit segmentation in the continuous-valued sequences which can result in less informative representations. To address these challenges, we introduceEEG2Rep, a self-prediction approach for self-supervised representation learning from EEG. Two core novel components of EEG2Rep are as follows: 1) Instead of learning to predict the masked input from raw EEG, EEG2Rep learns to predict masked input in latent representation space, and 2) Instead of conventional masking methods, EEG2Rep uses a new semantic subsequence preserving (SSP) method which provides informative masked inputs to guide EEG2Rep to generate rich semantic representations. In experiments on 6 diverse EEG tasks with subject variability, EEG2Rep significantly outperforms state-of-the-art methods. We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50% of EEG recordings will result in the most accurate results on all 6 tasks on average. Finally, we show that EEG2Rep is robust to noise addressing a significant challenge that exists in EEG data. Models and code are available at:https://github.com/Navidfoumani/EEG2Rep
Unigraph: Learning a unified cross-domain foundation model for text-attributed graphs
Authors: Yufei He, Yuan Sui, Xiaoxin He, Bryan Hooi
Venue: Conference KDD 2025
Year: 2025

Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence, exhibiting remarkable abilities to generalize across a wide array of tasks and applications beyond their initial training objectives. However, graph learning has predominantly focused on single-graph models, tailored to specific tasks or datasets, lacking the ability to transfer learned knowledge to different domains. This limitation stems from the inherent complexity and diversity of graph structures, along with the different feature and label spaces specific to graph data. In this paper, we recognize text as an effective unifying medium and employ Text-Attributed Graphs (TAGs) to leverage this potential. We present our UniGraph framework, designed to learn a foundation model for TAGs, which is capable of generalizing to unseen graphs and tasks across diverse domains. Unlike single-graph models that use pre-computed node features of varying dimensions as input, our approach leverages textual features for unifying node representations, even for graphs such as molecular graphs that do not naturally have textual features. We propose a novel cascaded architecture of Language Models (LMs) and Graph Neural Networks (GNNs) as backbone networks. Additionally, we propose the first pre-training algorithm specifically designed for large-scale self-supervised learning on TAGs, based on Masked Graph Modeling. We introduce graph instruction tuning using Large Language Models (LLMs) to enable zero-shot prediction ability. Our comprehensive experiments across various graph learning tasks and domains demonstrate the model's effectiveness in self-supervised representation learning on unseen graphs, few-shot in-context transfer, and zero-shot transfer, even surpassing or matching the performance of GNNs that have undergone supervised training on target datasets.
Psa-gan: Progressive self attention gans for synthetic time series
Authors: Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Shubham Kapoor, Rajbir-Singh Nirwan, Valentin Flunkert, Jan Gasthaus, Tim Januschowski
Venue: Conference ICLR 2022
Year: 2022

Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using progressive growing of GANs and self-attention. We show that PSA-GAN can be used to reduce the error in two downstream forecasting tasks over baselines that only use real data. We also introduce a Frechet-Inception Distance-like score, Context-FID, assessing the quality of synthetic time series samples. In our downstream tasks, we find that the lowest scoring models correspond to the best-performing ones. Therefore, Context-FID could be a useful tool to develop time series GAN models.
Gsl4rec: Session-based recommendations with collective graph structure learning and next interaction prediction
Authors: Chunyu Wei, Bing Bai, Kun Bai, Fei Wang
Venue: Conference WWW 2022
Year: 2022

Users’ social connections have recently shown significant benefits to session-based recommendations, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm presumes a given social network, which is not necessarily consistent with the fast-evolving shared interests and is expensive to collect. We propose a novel idea to learn the graph structure among users and make recommendations collectively in a coupled framework. This idea raises two challenges, i.e., scalability and effectiveness. We introduce a novel graph-structure learning framework for session-based recommendations (GSL4Rec) for solving both challenges simultaneously. Our framework has a two-stage strategy, i.e., the coarse neighbor screening and the self-adaptive graph structure learning, to enable the exploration of potential links among all users while maintaining a tractable amount of computation for scalability. We also propose a phased heuristic learning strategy to sequentially and synergistically train the graph learning part and recommendation part of GSL4Rec, thus improving the effectiveness by making the model easier to achieve good local optima. Experiments on five public datasets demonstrate that our proposed model significantly outperforms strong baselines, including state-of-the-art social network-based methods.
Revisiting fake news detection: Towards temporality-aware evaluation by leveraging engagement earliness
Authors: Junghoon Kim, Junmo Lee, Yeonjun In, Kanghoon Yoon, Chanyoung Park
Venue: Conference WSDM 2025
Year: 2025

Social graph-based fake news detection aims to identify news articles containing false information by utilizing social contexts, e.g., user information, tweets and comments. However, conventional methods are evaluated under less realistic scenarios, where the model has access to future knowledge on article-related and context-related data during training. In this work, we newly formalize a more realistic evaluation scheme that mimics real-world scenarios, where the data is temporality-aware and the detection model can only be trained on data collected up to a certain point in time. We show that the discriminative capabilities of conventional methods decrease sharply under this new setting, and further propose DAWN, a method more applicable to such scenarios. Our empirical findings indicate that later engagements (e.g., consuming or reposting news) contribute more to noisy edges that link real news-fake news pairs in the social graph. Motivated by this, we utilize feature representations of engagement earliness to guide an edge weight estimator to suppress the weights of such noisy edges, thereby enhancing the detection performance of DAWN. Through extensive experiments, we demonstrate that DAWN outperforms existing fake news detection methods under real-world environments. The source code is available at https://github.com/LeeJunmo/DAWN.
Tractable dendritic rnns for reconstructing nonlinear dynamical systems
Authors: Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz
Venue: Conference ICML 2022
Year: 2022

In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic behavior and noise. Previous deep learning approaches toward this goal often suffered from a lack of interpretability and tractability. In particular, the high-dimensional latent spaces often required for a faithful embedding, even when the underlying dynamics lives on a lower-dimensional manifold, can hamper theoretical analysis. Motivated by the emerging principles of dendritic computation, we augment a dynamically interpretable and mathematically tractable piecewise-linear (PL) recurrent neural network (RNN) by a linear spline basis expansion. We show that this approach retains all the theoretically appealing properties of the simple PLRNN, yet boosts its capacity for approximating arbitrary nonlinear dynamical systems in comparatively low dimensions. We employ two frameworks for training the system, one combining back-propagation-through-time (BPTT) with teacher forcing, and another based on fast and scalable variational inference. We show that the dendritically expanded PLRNN achieves better reconstructions with fewer parameters and dimensions on various dynamical systems benchmarks and compares favorably to other methods, while retaining a tractable and interpretable structure.
Optimizing sequential experimental design with deep reinforcement learning
Authors: Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli
Venue: Conference ICML 2022
Year: 2022

Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging. Recently, techniques using amortization have been proposed to make these Bayesian approaches practical, by training a parameterized policy that proposes designs efficiently at deployment time. However, these methods may not sufficiently explore the design space, require access to a differentiable probabilistic model and can only optimize over continuous design spaces. Here, we address these limitations by showing that the problem of optimizing policies can be reduced to solving a Markov decision process (MDP). We solve the equivalent MDP with modern deep reinforcement learning techniques. Our experiments show that our approach is also computationally efficient at deployment time and exhibits state-of-the-art performance on both continuous and discrete design spaces, even when the probabilistic model is a black box.
Fairness-aware learning with prejudice free representations
Authors: Ramanujam Madhavan, Mohit Wadhwa
Venue: Conference CIKM 2020
Year: 2020

Machine learning models are extensively being used to make decisions that have a significant impact on human life. These models are trained over historical data that may contain information about sensitive attributes such as race, sex, religion, etc. The presence of such sensitive attributes can impact certain population subgroups unfairly. It is straightforward to remove sensitive features from the data; however, a model could pick up prejudice from latent sensitive attributes that may exist in the training data. This has led to the growing apprehension about the fairness of the employed models. In this paper, we propose a novel algorithm that can effectively identify and treat latent discriminating features. The approach is agnostic of the learning algorithm and generalizes well for classification as well as regression tasks. It can also be used as a key aid in proving that the model is free of discrimination towards regulatory compliance if the need arises. The approach helps to collect discrimination-free features that would improve the model performance while ensuring the fairness of the model. The experimental results from our evaluations on publicly available real-world datasets show a near-ideal fairness measurement in comparison to other methods.
Query-specific Variable Depth Pooling via Query Performance Prediction
Authors: Debasis Ganguly, Emine Yilmaz
Venue: Conference SIGIR 2023
Year: 2023

Due to the massive size of test collections, a standard practice in IR evaluation is to construct a 'pool' of candidate relevant documents comprised of the top-k documents retrieved by a wide range of different retrieval systems - a process called depth-k pooling. A standard practice is to set the depth (k) to a constant value for each query constituting the benchmark set. However, in this paper we argue that the annotation effort can be substantially reduced if the depth of the pool is made a variable quantity for each query, the rationale being that the number of documents relevant to the information need can widely vary across queries. Our hypothesis is that a lower depth for queries with a small number of relevant documents, and a higher depth for those with a larger number of relevant documents can potentially reduce the annotation effort without a significant change in IR effectiveness evaluation.We make use of standard query performance prediction (QPP) techniques to estimate the number of potentially relevant documents for each query, which is then used to determine the depth of the pool. Our experiments conducted on standard test collections demonstrate that this proposed method of employing query-specific variable depths is able to adequately reflect the relative effectiveness of IR systems with a substantially smaller annotation effort.
Sets2Sets: Learning from Sequential Sets with Neural Networks
Authors: Haoji Hu, Xiangnan He
Venue: Conference KDD 2019
Year: 2019

Given past sequential sets of elements, predicting the subsequent sets of elements is an important problem in different domains. With the past orders of customers given, predicting the items that are likely to be bought in their following orders can provide information about the future purchase intentions. With the past clinical records of patients at each visit to the hospitals given, predicting the future clinical records in the subsequent visits can provide information about the future disease progression. These useful information can help to make better decisions in different domains. However, existing methods have not studied this problem well. In this paper, we formulate this problem as a sequential sets to sequential sets learning problem. We propose an end-to-end learning approach based on an encoder-decoder framework to solve the problem. In the encoder, our approach maps the set of elements at each past time step into a vector. In the decoder, our method decodes the set of elements at each subsequent time step from the vectors with a set-based attention mechanism. The repeated elements pattern is also considered in our method to further improve the performance. In addition, our objective function addresses the imbalance and correlation existing among the predicted elements. The experimental results on three real-world data sets showthat our method outperforms the best performance of the compared methods with respect to recall and person-wise hit ratio by 2.7-20.6% and 2.1-26.3%, respectively. Our analysis also shows that our decoder has good generalization to output sequential sets that are even longer than the output of training instances.
Parametric models for intransitivity in pairwise rankings
Authors: Rahul Makhijani, Johan Ugander
Venue: Conference WWW 2019
Year: 2019

There is a growing need for discrete choice models that account for the complex nature of human choices, escaping traditional behavioral assumptions such as the transitivity of pairwise preferences. Recently, several parametric models of intransitive comparisons have been proposed, but in all cases the model log-likelihood is non-concave, making inference difficult. In this work we generalize this trend, showing that there cannot exist an parametric model that both (i) has a log-likelihood function that is concave in item-level parameters and (ii) can exhibit intransitive preferences. Given this observation, we also contribute a new simple model for analyzing intransitivity in pairwise comparisons, taking inspiration from the Condorcet method (majority vote) in social choice theory. The majority vote model we analyze is defined as a voting process over independent Random Utility Models (RUMs). We infer a multidimensional embedding of each object or player, in contrast to the traditional one-dimensional embedding used by models such as the Thurstone or Bradley-Terry-Luce (BTL) models. We show that a three-dimensional majority vote model is capable of modeling arbitrarily strong and long intransitive cycles, and can also represent arbitrary pairwise comparison probabilities on any triplet. We provide experimental results that substantiate our claims regarding the effectiveness of our model in capturing intransitivity for various pairwise choice tasks such as predicting choices in recommendation systems, winners in online video games, and elections.
Learning interpretable feature context effects in discrete choice
Authors: Kiran Tomlinson, Austin R. Benson
Venue: Conference KDD 2021
Year: 2021

Individuals are constantly making choices---purchasing products, consuming Web content, making social connections---so understanding what contributes to these decisions is crucial in many settings. A major interest is understanding context effects, which occur when the set of available options itself affects an individual's relative preferences. These violate traditional rationality assumptions but are commonly observed in human behavior. At the same time, identifying context effects from choice data remains a challenge; existing models posit a specific context effect a priori and then measure its effect from (often effect-targeting) data. Here, we develop discrete choice models that capture a broad range of context effects, which are learned from choice data rather than baked into the model. Our models yield intuitive, interpretable, and statistically testable context effects, all while being simple to train. We evaluate our model on several empirical choice datasets, discovering, e.g., that people are more willing to book higher-priced hotels when presented with options that are on sale. We also provide the first analysis of context effects in online social network growth, finding that users forming connections place relatively more emphasis on shared neighbors when popular users are an option.
On early detection of hallucinations in factual question answering
Authors: Ben Snyder, Marius Moisescu, Muhammad Bilal Zafar
Venue: Conference KDD 2024
Year: 2024

While large language models (LLMs) have taken great strides towards helping humans with a plethora of tasks, hallucinations remain a major impediment towards gaining user trust. The fluency and coherence of model generations even when hallucinating makes detection a difficult task. In this work, we explore if the artifacts associated with the model generations can provide hints that the generation will contain hallucinations. Specifically, we probe LLMs at 1) the inputs via Integrated Gradients based token attribution, 2) the outputs via the Softmax probabilities, and 3) the internal state via self-attention and fully-connected layer activations for signs of hallucinations on open-ended question answering tasks. Our results show that the distributions of these artifacts tend to differ between hallucinated and non-hallucinated generations. Building on this insight, we train binary classifiers that use these artifacts as input features to classify model generations into hallucinations and non-hallucinations. These hallucination classifiers achieve up to $0.80$ AUROC. We also show that tokens preceding a hallucination can already predict the subsequent hallucination even before it occurs.
Node-wise Diffusion for Scalable Graph Learning
Authors: Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, Xiaokui Xiao
Venue: Conference WWW 2023
Year: 2023

Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis of online social networks, and recommendation in e-commerce. The state of the art derives representations for all nodes in graphs following the same diffusion (message passing) model without discriminating their uniqueness. However, (i) labeled nodes involved in model training usually account for a small portion of graphs in the semi-supervised setting, and (ii) different nodes locate at different graph local contexts and it inevitably degrades the representation qualities if treating them undistinguishedly in diffusion. To address the above issues, we develop NDM, a universal node-wise diffusion model, to capture the unique characteristics of each node in diffusion, by which NDM is able to yield high-quality node representations. In what follows, we customize NDM for semi-supervised learning and design the NIGCN model. In particular, NIGCN advances the efficiency significantly since it (i) produces representations for labeled nodes only and (ii) adopts well-designed neighbor sampling techniques tailored for node representation generation. Extensive experimental results on various types of web datasets, including citation, social and co-purchasing graphs, not only verify the state-of-the-art effectiveness of NIGCN but also strongly support the remarkable scalability of NIGCN. In particular, NIGCN completes representation generation and training within 10 seconds on the dataset with hundreds of millions of nodes and billions of edges, up to orders of magnitude speedups over the baselines, while achieving the highest F1-scores on classification.