OpenRaiser/Intern-Atlas huggingface dataset:
Prost: Prompt future snapshot on dynamic graphs for spatio-temporal prediction
Authors: Kaiwen Xia, Li Lin, Shuai Wang, Qi Zhang, Shuai Wang, Tian He
Venue: Conference KDD 2025
Year: 2025

Spatio-temporal prediction focuses on jointly modeling spatial correlations and temporal evolution and has a wide range of applications. Due to the heterogeneity of spatio-temporal data, accurate prediction relies on effectively integrating topological structures and sequential patterns. Although recurrent graph learning methods excel at capturing dynamic graph patterns, explicitly inferring future snapshots from historical dynamic graphs remains a significant challenge. Recently, prompt-based graph learning has shown the potential to improve future snapshot inference by leveraging node or task-specific prompts. However, these methods fail to fully capture edge information resulting in incomplete and less accurate representations of future snapshot structures. To bridge this gap, we propose ProST, a framework that Prompts future snapshots on dynamic graphs for Spatio-Temporal prediction, which leverages dynamic graph pre-training to generate a premise graph containing historical graph information and then employs prompts on the premise graph to infer explicit future snapshots. Specifically, this framework comprises three steps: Firstly, dynamic graph pre-training is performed using multi-granularity evolution graph convolution to obtain the premise graph with both local and global features of dynamic graphs. Secondly, prompt subgraphs are used to prompt node pairs and edge features within the premise graph. The subgraph prompt aggregation mechanism propagates this information to generate future snapshots. Finally, we freeze the parameters of the pre-trained model and update the subgraph prompt parameters using meta-learning to adapt to downstream spatio-temporal prediction tasks. Extensive experiments on real-world datasets validate that ProST achieves state-of-the-art performance.
Neurocut: A neural approach for robust graph partitioning
Authors: Rishi Shah, Krishnanshu Jain, Sahil Manchanda, Sourav Medya, Sayan Ranu
Venue: Conference KDD 2024
Year: 2024

Graph partitioning aims to divide a graph into k disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhibit NP-hardness due to their combinatorial nature. Conventional methods, like approximation algorithms or heuristics, are designed for distinct partitioning objectives and fail to achieve generalization across other important partitioning objectives. Recently machine learning-based methods have been developed that learn directly from data. Further, these methods have a distinct advantage of utilizing node features that carry additional information. However, these methods assume differentiability of target partitioning objective functions and cannot generalize for an unknown number of partitions, i.e., they assume the number of partitions is provided in advance. In this study, we develop NeuroCut with two key innovations over previous methodologies. First, by leveraging a reinforcement learning-based framework over node representations derived from a graph neural network and positional features, NeuroCut can accommodate any optimization objective, even those with non-differentiable functions. Second, we decouple the parameter space and the partition count making NeuroCut inductive to any unseen number of partition, which is provided at query time. Through empirical evaluation, we demonstrate that NeuroCut excels in identifying high-quality partitions, showcases strong generalization across a wide spectrum of partitioning objectives, and exhibits strong generalization to unseen partition count.
3DGraphX: Explaining 3D molecular graph models via incorporating chemical priors
Authors: Xufeng Liu, Dongsheng Luo, Wenhan Gao, Yi Liu
Venue: Conference KDD 2025
Year: 2025

We consider the explanation of 3D graph neural networks (GNNs) in the field of molecular learning. Recent studies have modeled molecules as 3D graphs, but there exist formidable challenges for 3D graph explanation. In this work, we propose a novel and principled paradigm, known as 3DGraphX, for 3D molecular graph explanation. Unlike existing 2D GNN explanation methods, 3DGraphX focuses on 3D motifs, which are subgraphs showing great occurrence and function significance in molecular activities. Once generated, 3D motifs are fixed in the explanation model; hence, 3DGraphX produces more accurate and chemically plausible explanations in an efficient manner. 3DGraphX contains two branches with several novel methods for instance-level and geometry-level explanations, respectively. Two novel components, known as the mask pooling component and mask unpooling component, are developed to discover important motifs for each 3D molecule as the instance-level explanation. Local spherical coordinate systems are built to investigate the relative positions among motifs for geometry-level explanation. Altogether, 3DGraphX sheds light on the characteristics of molecules as well as the behaviors of 3D GNNs in molecular learning. Experimental results show that 3DGraphX significantly outperforms baselines in instance-level explanation with various explanation budgets. Additional experiments show that 3DGraphX reveals the important geometries taken by 3D GNNs for accurate molecular learning. The code is publicly available at https://github.com/xufliu/3DGraphX.
Pail: Performance based adversarial imitation learning engine for carbon neutral optimization
Authors: Yuyang Ye 0002, Lu-An Tang, Haoyu Wang 0003, Runlong Yu, Wenchao Yu, Erhu He, Haifeng Chen, Hui Xiong 0001
Venue: Conference KDD 2024
Year: 2024

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.
Dsgpt: Domain-specific generative pre-training of transformers for text generation in e-commerce title and review summarization
Authors: Xueying Zhang, Yunjiang Jiang, Yue Shang, Zhaomeng Cheng, Chi Zhang 0012, Xiaochuan Fan, Yun Xiao, Bo Long
Venue: Conference SIGIR 2021
Year: 2021

We propose a novel domain-specific generative pre-training (DS-GPT) method for text generation and apply it to the product title and review summarization problems on E-commerce mobile display. First, we adopt a decoder-only transformer architecture, which fits well for fine-tuning tasks by combining input and output all together. Second, we demonstrate utilizing only small amount of pretraining data in related domains is powerful. Pre-training a language model from a general corpus such as Wikipedia or the Common Crawl requires tremendous time and resource commitment, and can be wasteful if the downstream tasks are limited in variety. Our DSGPT is pre-trained on a limited dataset, the Chinese short text summarization dataset (LCSTS). Third, our model does not require product-related human-labeled data. For title summarization task, the state of art explicitly uses additional background knowledge in training and predicting stages. In contrast, our model implicitly captures this knowledge and achieves significant improvement over other methods, after fine-tuning on the public Taobao.com dataset. For review summarization task, we utilize JD.com in-house dataset, and observe similar improvement over standard machine translation methods which lack the flexibility of fine-tuning. Our proposed work can be simply extended to other domains for a wide range of text generation tasks.
Conservation-informed graph learning for spatiotemporal dynamics prediction
Authors: Yuan Mi, Pu Ren, Hongteng Xu, Hongsheng Liu, Zidong Wang, Yike Guo, Ji-Rong Wen, Hao Sun, Yang Liu
Venue: Conference KDD 2025
Year: 2025

Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep learning models often lack interpretability, fail to obey intrinsic physics, and struggle to cope with the various domains. While geometry-based methods, e.g., graph neural networks (GNNs), have been proposed to further tackle these challenges, they still need to find the implicit physical laws from large datasets and rely excessively on rich labeled data. In this paper, we herein introduce the conservation-informed GNN (CiGNN), an end-to-end explainable learning framework, to learn spatiotemporal dynamics based on limited training data. The network is designed to conform to the general conservation law via symmetry, where conservative and non-conservative information passes over a multiscale space enhanced by a latent temporal marching strategy. The efficacy of our model has been verified in various spatiotemporal systems based on synthetic and real-world datasets, showing superiority over baseline models. Results demonstrate that CiGNN exhibits remarkable accuracy and generalizability, and is readily applicable to learning for prediction of various spatiotemporal dynamics in a spatial domain with complex geometry.
Rapid image labeling via neuro-symbolic learning
Authors: Yifeng Wang 0002, Zhi Tu, Yiwen Xiang, Shiyuan Zhou, Xiyuan Chen 0002, Bingxuan Li, Tianyi Zhang 0001
Venue: Conference KDD 2023
Year: 2023

The success of Computer Vision (CV) relies heavily on manually annotated data. However, it is prohibitively expensive to annotate images in key domains such as healthcare, where data labeling requires significant domain expertise and cannot be easily delegated to crowd workers. To address this challenge, we propose a neuro-symbolic approach called RAPID, which infers image labeling rules from a small amount of labeled data provided by domain experts and automatically labels unannotated data using the rules. Specifically, RAPID combines pre-trained CV models and inductive logic learning to infer the logic-based labeling rules. RAPID achieves a labeling accuracy of 83.33% to 88.33% on four image labeling tasks with only 12 to 39 labeled samples. In particular, RAPID significantly outperforms finetuned CV models in two highly specialized tasks. These results demonstrate the effectiveness of RAPID in learning from small data and its capability to generalize among different tasks. Code and our dataset are publicly available at https://github.com/Neural-Symbolic-Image-Labeling/Rapid/
Differentially private selection from secure distributed computing
Authors: Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh
Venue: Conference WWW 2024
Year: 2024

Given a collection of vectors \boldsymbolx ^(1), \dots,\boldsymbolx ^(n) \in \0,1\ ^d, the selection problem asks to report the index of an "approximately largest'' entry in \boldsymbolx =\sum_j=1 ^n \boldsymbolx ^(j) . Selection abstracts a host of problems, for example: Recommendation of a popular item based on user feedback; releasing statistics on the most popular web sites; hyperparameter tuning and feature selection in machine learning. We study selection under differential privacy, where a released index guarantees privacy for individual vectors. Though selection can be solved with an excellent utility guarantee in the central model of differential privacy, the distributed setting where no single entity is trusted to aggregate the data lacks solutions. Specifically, strong privacy guarantees with high utility are offered in high trust settings, but not in low trust settings. For example, in the popular shuffle model of distributed differential privacy, there are strong lower bounds suggesting that the utility of the central model cannot be obtained. In this paper we design a protocol for differentially private selection in a trust setting similar to the shuffle model---with the crucial difference that our protocol tolerates corrupted servers while maintaining privacy. Our protocol uses techniques from secure multi-party computation (MPC) to implement a protocol that: (i) has utility on par with the best mechanisms in the central model, (ii) scales to large, distributed collections of high-dimensional vectors, and (iii) uses k\geq 3 servers that collaborate to compute the result, where the differential privacy guarantee holds assuming an honest majority. Since general-purpose MPC techniques are not sufficiently scalable, we propose a novel application of integer secret sharing, and evaluate the utility and efficiency of our protocol both theoretically and empirically. Our protocol improves on previous work by Champion, shelat and Ullman (CCS '19) by significantly reducing the communication costs, demonstrating that large-scale differentially private selection with information-theoretical guarantees is feasible in a distributed setting.
Swat: Statistical modeling of video watch time through user behavior analysis
Authors: Shentao Yang, Haichuan Yang, Linna Du, Adithya Ganesh, Bo Peng, Boying Liu, Serena Li, Ji Liu
Venue: Conference KDD 2025
Year: 2025

The significance of estimating video watch time has been highlighted by the rising importance of (short) video recommendation, which has become a core product of mainstream social media platforms. Modeling video watch time, however, has been challenged by the complexity of user-video interaction, such as different user behavior modes in watching the recommended videos and varying watching probability over the video progress bar. Despite the importance and challenges, existing literature on modeling video watch time mostly focuses on relatively black-box mechanical enhancement of the classical regression/classification losses, without factoring in user behavior in a principled manner. In this paper, we for the first time take on a user-centric perspective to model video watch time, from which we propose a white-box statistical framework that directly translates various user behavior assumptions in watching (short) videos into statistical watch time models. These behavior assumptions are portrayed by our domain knowledge on users' behavior modes in video watching. We further employ bucketization to cope with user's non-stationary watching probability over the video progress bar, which additionally helps to respect the constraint of video length and facilitate the practical compatibility between the continuous regression event of watch time and other binary classification events. We test our models extensively on two public datasets, a large-scale offline industrial dataset, and an online A/B test on a short video platform with hundreds of millions of daily-active users. On all experiments, our models perform competitively against strong relevant baselines, demonstrating the efficacy of our user-centric perspective and proposed framework.
Partial least squares regression for graph mining
Authors: Hiroto Saigo, Nicole Krämer, Koji Tsuda
Venue: Conference KDD 2008
Year: 2008

Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS). To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm. The mining algorithm is iteratively called with different weight vectors, creating one latent component per one mining call. Our method, graph PLS, is efficient and easy to implement, because the weight vector is updated with elementary matrix calculations. In experiments, our graph PLS algorithm showed competitive prediction accuracies in many chemical datasets and its efficiency was significantly superior to graph boosting (gBoost) and the naive method based on frequent graph mining.
FALCONS: Searching and browsing entities on the Semantic Web
Authors: Gong Cheng, Weiyi Ge, Yuzhong Qu
Venue: Conference WWW 2008
Year: 2008

As of today, the amount of data on the Semantic Web has grown considerably. The services for searching and browsing entities on the Semantic Web are in demand. To provide such services, we developed the Falcons system. In this poster, we present the features of the Falcons system.
A personalized search engine based on web-snippet hierarchical clustering
Authors: Paolo Ferragina, Antonio Gulli
Venue: Conference WWW 2005
Year: 2005

In this paper we propose a hierarchical clustering engine, called SnakeT, that is able to organize on-the-fly the search results drawn from 16 commodity search engines into a hierarchy of labeled folders. The hierarchy o#ers a complementary view to the flat-ranked list of results returned by current search engines. Users can navigate through the hierarchy driven by their search needs. This is especially useful for informative, polysemous and poor queries.
Semi-supervised discriminative preference elicitation for cold-start recommendation
Authors: Xi Zhang, Jian Cheng, Ting Yuan, Biao Niu, Hanqing Lu
Venue: Conference CIKM 2013
Year: 2013

Recommendation for cold users is fairly challenging because no prior rating can be used in preference prediction. To tackle this cold-start scenario, rating elicitation is usually employed through an initial interview in which users are queried by some carefully selected items. In this paper, we propose a novel framework to mine the most valuable items to construct query set using a semi-supervised discriminative selection (SSDS) model. To learn a low dimensional representation for users in item space which can reflect their tastes to a large extent, the model incorporates category labels as discriminative information. To ensure the used labels reliable as well as all users considered, the model utilizes a semi-supervised scheme leveraging expert guidance with graph regularization. Experimental results on real-world dataset MovieLens demonstrate that the proposed SSDS model outperforms traditional preference elicitation methods on top-N measures for cold-start recommendation.
Customized tour recommendations in urban areas
Authors: Aristides Gionis, Theodoros Lappas, Konstantinos Pelechrinis, Evimaria Terzi
Venue: Conference WSDM 2014
Year: 2014

The ever-increasing urbanization coupled with the unprecedented capacity to collect and process large amounts of data have helped to create the vision of intelligent urban environments. One key aspect of such environments is that they allow people to effectively navigate through their city. While GPS technology and route-planning services have undoubtedly helped towards this direction, there is room for improvement in intelligent urban navigation. This vision can be fostered by the proliferation of location-based social networks, such as Foursquare or Path, which record the physical presence of users in different venues through check-ins. This information can then be used to enhance intelligent urban navigation, by generating customized path recommendations for users.
Image based regression using boosting method
Authors: Shaohua Kevin Zhou, Bogdan Georgescu, Xiang Sean Zhou, Dorin Comaniciu
Venue: Conference ICCV 2005
Year: 2005

We present a general algorithm of image based regression that is applicable to many vision problems. The proposed regressor that targets a multiple-output setting is learned using boosting method. We formulate a multiple-output regression problem in such a way that overfitting is decreased and an analytic solution is admitted. Because we represent the image via a set of highly redundant Haar-like features that can be evaluated very quickly and select relevant features through boosting to absorb the knowledge of the training data, during testing we require no storage of the training data and evaluate the regression function almost in no time. We also propose an efficient training algorithm that breaks the computational bottleneck in the greedy feature selection process. We validate the efficiency of the proposed regressor using three challenging tasks of age estimation, tumor detection, and endocardial wall localization and achieve the best performance with a dramatic speed, e.g., more than 1000 times faster than conventional data-driven techniques such as support vector regressor in the experiment of endocardial wall localization.
A graph-theoretic approach to extract storylines from search results
Authors: Ravi Kumar, Uma Mahadevan, D. Sivakumar
Venue: Conference KDD 2004
Year: 2004

We present a graph-theoretic approach to discover storylines from search results. Storylines are windows that offer glimpses into interesting themes latent among the top search results for a query; they are different from, and complementary to, clusters obtained through traditional approaches. Our framework is axiomatically developed and combinatorial in nature, based on generalizations of the maximum induced matching problem on bipartite graphs. The core algorithmic task involved is to mine for signature structures in a robust graph representation of the search results. We present a very fast algorithm for this task based on local search. Experiments show that the collection of storylines extracted through our algorithm offers a concise organization of the wealth of information hidden beyond the first page of search results.
Community-based topic modeling for social tagging
Authors: Daifeng Li, Bing He, Ying Ding, Jie Tang, Cassidy R. Sugimoto, Zheng Qin, Erjia Yan, Juanzi Li, Tianxi Dong
Venue: Conference CIKM 2010
Year: 2010

Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular social tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA-Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.