OpenRaiser/Intern-Atlas huggingface dataset:
CoMAL: Contrastive Active Learning for Multi-Label Text Classification
Authors: Cheng Peng 0011, Haobo Wang 0001, Ke Chen 0005, Lidan Shou, Chang Yao 0001, Runze Wu 0001, Gang Chen 0001
Venue: KDD
Year: 2024

Multi-label text classification (MLTC) allows a given text to be associated with multiple labels, which well suits many real-world data mining scenarios. However, the annotation effort of MLTC is inevitably expensive and time-consuming. Although multi-label active learning provides a cost-effective solution, it still faces two major challenges: (i) constructing decent feature space to distinguish the confusing semantics of different labels; (ii) defining proper sampling criteria to measure a sample's joint effect over the entire label space. To bridge these gaps, we propose a Contrastive Multi-label Active Learning framework (CoMAL) that gives an effective data acquisition strategy. Specifically, a contrastive decoupling mechanism is introduced to fully release the semantic information of multiple labels into the latent space. Then, we devise a hybrid criterion that balances two data value measures: (i) similarity-enhanced label cardinality inconsistency reflects the uncertainty of data predictions. (ii) positive feature diversity evaluates the positive-propensity semantic diversity to handle the label sparsity. Extensive experiments demonstrate that our CoMAL outperforms the current state-of-the-art multi-label active learning approaches. Code for CoMAL is available at https://github.com/chengzju/CoMAL.
Workshop on Discovering Drift Phenomena in Evolving Data Landscape (DELTA)
Authors: Marco Piangerelli, Bardh Prenkaj, Ylenia Rotalinti, Ananya Joshi 0001, Giovanni Stilo
Venue: KDD
Year: 2024

Automated systems must adapt to evolving environments, yet many struggle with drift phenomena affecting healthcare, finance, and cybersecurity domains. The DELTA workshop addresses this by distinguishing between data and concept drift, aiming to create a practical, human-centric framework for managing drift. The workshop seeks innovative drift detection, prediction, and analysis solutions by uniting researchers and practitioners. DELTA fosters collaboration to advance the understanding and management of drift in dynamic data landscapes by featuring keynotes, paper presentations, interactive sessions, and discussions.
CASH via Optimal Diversity for Ensemble Learning
Authors: Pranav Poduval, Sanjay Kumar Patnala, Gaurav Oberoi, Nitish Srivasatava, Siddhartha Asthana
Venue: KDD
Year: 2024

The Combined Algorithm Selection and Hyperparameter Optimization (CASH) problem is pivotal in Automatic Machine Learning (AutoML). Most leading approaches combine Bayesian optimization with post-hoc ensemble building to create advanced AutoML systems. Bayesian optimization (BO) typically focuses on identifying a singular algorithm and its hyperparameters that outperform all other configurations. Recent developments have highlighted an oversight in prior CASH methods: the lack of consideration for diversity among the base learners of the ensemble. This oversight was overcome by explicitly injecting the search for diversity into the traditional CASH problem. However, despite recent developments, BO's limitation lies in its inability to directly optimize ensemble generalization error, offering no theoretical assurance that increased diversity correlates with enhanced ensemble performance. Our research addresses this gap by establishing a theoretical foundation that integrates diversity into the core of BO for direct ensemble learning. We explore a theoretically sound framework that describes the relationship between pair-wise diversity and ensemble performance, which allows our Bayesian optimization framework Optimal Diversity Bayesian Optimization (OptDivBO) to directly and efficiently minimize ensemble generalization error. OptDivBO guarantees an optimal balance between pairwise diversity and individual model performance, setting a new precedent in ensemble learning within CASH. Empirical results on 20 public datasets show that OptDivBO achieves the best average test ranks of 1.57 and 1.4 in classification and regression tasks.
Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals
Authors: Bardh Prenkaj, Mario Villaizán-Vallelado, Tobias Leemann, Gjergji Kasneci
Venue: KDD
Year: 2024

We present GRACIE (Graph Recalibration and Adaptive Counterfactual Inspection and Explanation), a novel approach for generative classification and counterfactual explanations of dynamically changing graph data. We study graph classification problems through the lens of generative classifiers. We propose a dynamic, self-supervised latent variable model that updates by identifying plausible counterfactuals for input graphs and recalibrating decision boundaries through contrastive optimization. Unlike prior work, we do not rely on linear separability between the learned graph representations to find plausible counterfactuals. Moreover, GRACIE eliminates the need for stochastic sampling in latent spaces and graph-matching heuristics. Our work distills the implicit link between generative classification and loss functions in the latent space, a key insight to understanding recent successes with this architecture. We further observe the inherent trade-off between validity and pulling explainee instances towards the central region of the latent space, empirically demonstrating our theoretical findings. In extensive experiments on synthetic and real-world graph data, we attain considerable improvements, reaching ~99% validity when sampling sets of counterfactuals even in the challenging setting of dynamic data landscapes.
The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs
Authors: Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen 0001, Stefan Zohren, Yuriy Nevmyvaka
Venue: KDD
Year: 2024

Time series data has become ubiquitous across various fields such as healthcare, finance, entertainment, and transportation, driven by advancements in sensing technologies that enable continuous monitoring and recording. This growth in data size and complexity presents new challenges for traditional analysis techniques, necessitating the development of advanced, interdisciplinary temporal mining algorithms. The goals of this workshop are to: (1) highlight significant challenges in learning and mining from time series data, such as irregular sampling, spatiotemporal structures, and uncertainty quantification; (2) discuss recent developments in algorithmic, theoretical, statistical, and systems-based approaches for addressing these challenges, including both classical methods and large language models (LLMs); and (3) synergize research efforts by exploring both new and open problems in time series analysis and mining. This workshop will focus on both the theoretical and practical aspects of time series data analysis, providing a platform for researchers and practitioners from academia, government, and industry to discuss potential research directions, critical technical issues, and present solutions for practical applications. Contributions from related fields such as AI, machine learning, data science, and statistics are also included.
Spatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature Prediction
Authors: Dekang Qi, Xiuwen Yi, Chengjie Guo, Yanyong Huang, Junbo Zhang 0004, Tianrui Li 0001, Yu Zheng 0004
Venue: KDD
Year: 2024

Indoor temperature prediction is crucial for decision-making in central heating systems. Beyond accuracy, predictions shall be interpretable, i.e. conform to the laws of physics; otherwise, it may lead to system failures or unsafe conditions. However, deep learning models often face criticism regarding interpretability, which limits their application in such settings. To this end, we propose a Spatio-Temporal Consistency enhanced Differential Network (CONST) for interpretable indoor temperature prediction. Our approach mainly consists of a differential predictive module and a spatio-temporal consistency module. Modeling the influential factors, the first module solves the issue of multicollinearity through the differential operation. Considering the heterogeneity of global and local data distributions, the second module characterizes the temporal and spatial consistency to mine the universal pattern by multi-task learning, thereby improving the prediction interpretability. Besides, we propose a set of interpretability metrics to overcome the drawbacks of partial dependence plot metric, which are more practical, zero-centered, flexible, and numerical. We conclude experiments on a real-world dataset with four heating stations. The results demonstrate the advantages of our approach over various baselines, where the interpretability can be improved by more than 8 times on cRPD while maintaining high accuracy. We developed CONST on the SmartHeat system, providing hourly indoor temperature forecasts for 13 heating stations in northern China.
Class-incremental Learning for Time Series: Benchmark and Evaluation
Authors: Zhongzheng Qiao, Quang Pham, Zhen Cao, Hoang H. Le, Ponnuthurai N. Suganthan, Xudong Jiang 0001, Savitha Ramasamy
Venue: KDD
Year: 2024

Real-world environments are inherently non-stationary, frequently introducing new classes over time. This is especially common in time series classification, such as the emergence of new disease classification in healthcare or the addition of new activities in human activity recognition. In such cases, a learning system is required to assimilate novel classes effectively while avoiding catastrophic forgetting of the old ones, which gives rise to the Class-incremental Learning (CIL) problem. However, despite the encouraging progress in the image and language domains, CIL for time series data remains relatively understudied. Existing studies suffer from inconsistent experimental designs, necessitating a comprehensive evaluation and benchmarking of methods across a wide range of datasets. To this end, we first present an overview of the Time Series Class-incremental Learning (TSCIL) problem, highlight its unique challenges, and cover the advanced methodologies. Further, based on standardized settings, we develop a unified experimental framework that supports the rapid development of new algorithms, easy integration of new datasets, and standardization of the evaluation process. Using this framework, we conduct a comprehensive evaluation of various generic and time-series-specific CIL methods in both standard and privacy-sensitive scenarios. Our extensive experiments not only provide a standard baseline to support future research but also shed light on the impact of various design factors such as normalization layers or memory budget thresholds. Codes are available at https://github.com/zqiao11/TSCIL.
Scalable Graph Learning for your Enterprise
Authors: Hema Raghavan
Venue: KDD
Year: 2024

Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations. However, building machine learning models using this data is both challenging and time consuming. The core problem is that no machine learning method is capable of learning on multiple tables interconnected by primary-foreign key relations. Current methods can only learn from a single table, so the data must first be manually joined and aggregated into a single training table, the process known as feature engineering. Feature engineering is slow, error prone and leads to suboptimal models. At Kumo.ai we have worked with researchers worldwide to develop an end-to-end deep representation learning approach to directly learn on data laid out across multiple tables [1]. We name our approach Relational Deep Learning (RDL). The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links. Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all input data, without any manual feature engineering. Our relational deep learning method to encode graph structure into low-dimensional embeddings brings several benefits: (1) automatic learning from the entire data spread across multiple relational tables (2) no manual feature engineering as the system learns optimal embeddings for a target problem; (3) state-of-the-art predictive performance.
A Fast Exact Algorithm to Enumerate Maximal Pseudo-cliques in Large Sparse Graphs
Authors: Ahsanur Rahman 0001, Kalyan Roy, Ramiza Maliha, Townim Faisal Chowdhury
Venue: KDD
Year: 2024

Pseudo-cliques (subgraphs with almost all possible edges) have many applications. But they do not satisfy the convertible antimonotone constraint (as we prove here). So, it is hard to reduce the search space of pseudo-cliques and list them efficiently. To our knowledge, only two exact algorithms, namely, ODES and PCE, were proposed for this purpose, but both have high execution times. Here, we present an exact algorithm named Fast Pseudo-Clique Enumerator (FPCE). It employs some pruning techniques we derived to reduce the search space. Our experiment on 15 real and 16 synthetic graphs shows that (i) on real graphs, FPCE is, on average, 38.6 and 6.5 times faster than ODES and PCE, respectively, whereas (ii) on synthetic graphs, FPCE is, on average, 39.7 and 3.1 times faster than ODES and PCE, respectively. We apply FPCE and a popular heuristic method on a PPI network to identify pseudo-cliques. FPCE outputs match with more known protein complexes, are more accurate, and are biologically more significant - suggesting that the exact computation of pseudo-cliques may give better insights. For its speed, FPCE is a suitable choice in such cases.
Leveraging Exposure Networks for Detecting Fake News Sources
Authors: Maor Reuben, Lisa Friedland, Rami Puzis, Nir Grinberg
Venue: KDD
Year: 2024

The scale and dynamic nature of the Web makes real-time detection of misinformation an extremely difficult task. Prior research mostly focused on offline (retrospective) detection of stories or claims using linguistic features of the content, flagging by users, and crowdsourced labels. Here, we develop a novel machine-learning methodology for detecting fake news sources using active learning, and examine the contribution of network, audience, and text features to the model accuracy. Importantly, we evaluate performance in both offline and online settings, mimicking the strategic choices fact-checkers have to make in practice as news sources emerge over time. We find that exposure networks provide information on considerably more sources than sharing networks (+49.6%), and that the inclusion of exposure features greatly improves classification PR-AUC in both offline (+33%) and online (+69.2%) settings. Textual features perform best in offline settings, but their performance deteriorates by 12.0-18.7% in online settings. Finally, the results show that a few iterations of active learning are sufficient for our model to attain predictive performance to comparable exhaustive labeling while incurring only 24.7% of the labeling costs. These results stress the importance of exposure networks as a source of valuable information for the investigation of information dissemination in social networks and question the robustness of textual features.
RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
Authors: Luis Roque, Carlos Soares, Luís Torgo
Venue: KDD
Year: 2024

We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-level forecasts must sum to upper-level ones, are prevalent in various contexts, such as retail sales across countries. Current empirical evaluations of forecasting methods are often limited to a small set of benchmark datasets, offering a narrow view of algorithm behavior. RHiOTS addresses this gap by systematically altering existing datasets and modifying the characteristics of individual series and their interrelations. It uses a set of parameterizable transformations to simulate those changes in the data distribution. Additionally, RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. This approach allows an in-depth analysis of algorithm and model behavior under diverse conditions. We illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. Furthermore, we found no significant differences in the robustness of the algorithms when applying specific reconciliation methods, such as MinT. RHiOTS provides researchers with a comprehensive tool for understanding the nuanced behavior of forecasting algorithms, offering a more reliable basis for selecting the most appropriate method for a given problem.
CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
Authors: Jingqing Ruan, Ziyue Li 0002, Hua Wei 0001, Haoyuan Jiang, Jiaming Lu, Xuantang Xiong, Hangyu Mao, Rui Zhao 0001
Venue: KDD
Year: 2024

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite a lot of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. Code is available at https://github.com/bonaldli/CoSLight.
A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability
Authors: Sakhawat Hossain Saimon, Tanzira Najnin, Jianhua Ruan
Venue: KDD
Year: 2024

Classification tasks in many real-world domains are exacerbated by class imbalance, relatively small sample sizes compared to high dimensionality, and measurement uncertainty. The problem of class imbalance has been extensively studied, and data augmentation methods based on interpolation of minority class instances have been proposed as a viable solution to mitigate imbalance. It remains to be seen whether augmentation can be applied to improve the overall performance while maintaining stability, especially with a limited number of samples. In this paper, we present a novel feature-space augmentation technique that can be applied to high-dimensional data for classification tasks and address these issues. Our method utilizes uniform random sampling and introduces synthetic instances by taking advantage of the local distributions of individual features in the observed instances. The core augmentation algorithm is class-invariant, which opens up an unexplored avenue of simultaneously improving and stabilizing performance by augmenting unlabeled instances. The proposed method is evaluated using a comprehensive performance analysis involving multiple classifiers and metrics. Comparative analysis with existing feature space augmentation methods strongly suggests that the proposed algorithm can result in improved classification performance while also increasing the overall reliability of the performance evaluation.
LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation
Authors: Rebecca Salganik, Xiaohao Liu, Yunshan Ma 0002, Jian Kang 0008, Tat-Seng Chua
Venue: KDD
Year: 2024

As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a personalized and musically cohesive manner, has become vital to the success of music streaming services. Currently, many existing playlist continuation approaches rely on collaborative filtering methods to perform their recommendations. However, such methods will struggle to recommend songs that lack interaction data, an issue known as the cold-start problem. Current approaches to this challenge design complex mechanisms for extracting relational signals from sparse collaborative signals and integrating them into content representations. However, these approaches leave content representation learning out of scope and utilize frozen, pre-trained content models that may not be aligned with the distribution or format of a specific musical setting. Furthermore, even the musical state-of-the-art content modules are either (1) incompatible with the cold-start setting or (2) unable to effectively integrate cross-modal and relational signals. In this paper, we introduce LARP, a multi-modal cold-start playlist continuation model, to effectively overcome these limitations. LARP is a three-stage contrastive learning framework that integrates both multi-modal and relational signals into its learned representations. Our framework uses increasing stages of task-specific abstraction: within-track (language-audio) contrastive loss, track-track contrastive loss, and track-playlist contrastive loss. Experimental results on two publicly available datasets demonstrate the efficacy of LARP over uni-modal and multi-modal models for playlist continuation in a cold-start setting. Finally, this work pioneers the perspective of addressing cold-start recommendation via relational representation learning. Code and dataset are released at: https://github.com/Rsalganik1123/LARP/
Scalable Temporal Motif Densest Subnetwork Discovery
Authors: Ilie Sarpe, Fabio Vandin, Aristides Gionis
Venue: KDD
Year: 2024

Finding dense subnetworks, with density based on edges or more complex structures, such as subgraphs or k-cliques, is a fundamental algorithmic problem with many applications. While the problem has been studied extensively in static networks, much remains to be explored for temporal networks.
DPHGNN: A Dual Perspective Hypergraph Neural Networks
Authors: Siddhant Saxena, Shounak Ghatak, Raghu Kolla, Debashis Mukherjee, Tanmoy Chakraborty 0002
Venue: KDD
Year: 2024

Message passing on hypergraphs has been a standard framework for learning higher-order correlations between hypernodes. Recently-proposed hypergraph neural networks (HGNNs) can be categorized into spatial and spectral methods based on their design choices. In this work, we analyze the impact of change in hypergraph topology on the suboptimal performance of HGNNs and propose DPHGNN, a novel dual-perspective HGNN that introduces equivariant operator learning to capture lower-order semantics by inducing topology-aware spatial and spectral inductive biases. DPHGNN employs a unified framework to dynamically fuse lower-order explicit feature representations from the underlying graph into the super-imposed hypergraph structure. We benchmark DPHGNN over eight benchmark hypergraph datasets for the semi-supervised hypernode classification task and obtain superior performance compared to seven state-of-the-art baselines. We also provide a theoretical framework and a synthetic hypergraph isomorphism test to express the power of spatial HGNNs and quantify the expressivity of DPHGNN beyond the Generalized Weisfeiler Leman (1-GWL) test. Finally, DPHGNN was deployed by our partner e-commerce company, Meesho for the Return-to-Origin (RTO) prediction task, which shows ~7% higher macro F1-Score than the best baseline.