OpenRaiser/Intern-Atlas huggingface dataset:
Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through Recommendations
Authors: Erica Coppolillo, Giuseppe Manco 0001, Aristides Gionis
Venue: KDD
Year: 2024

Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-off, as higher diversity typically comes at the cost of relevance, resulting in lower user engagement. Existing recommendation algorithms try to resolve this trade-off by combining the two measures, relevance and diversity, into one aim and then seeking recommendations that optimize the combined objective, for a given number of items to recommend. Traditional approaches, however, do not consider the user interaction with the recommended items. In this paper, we put the user at the central stage, and build on the interplay between relevance, diversity, and user behavior. In contrast to applications where the goal is solely to maximize engagement, we focus on scenarios aiming at maximizing the total amount of knowledge encountered by the user. We use diversity as a surrogate of the amount of knowledge obtained by the user while interacting with the system, and we seek to maximize diversity. We propose a probabilistic user-behavior model in which users keep interacting with the recommender system as long as they receive relevant recommendations, but they may stop if the relevance of the recommended items drops. Thus, for a recommender system to achieve a high-diversity measure, it will need to produce recommendations that are both relevant and diverse. Finally, we propose a novel recommendation strategy that combines relevance and diversity by a copula function. We conduct an extensive evaluation of the proposed methodology over multiple datasets, and we show that our strategy outperforms several state-of-the-art competitors. Our implementation is publicly available at https://github.com/EricaCoppolillo/EXPLORE.
AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
Authors: Siddhartha Shankar Das, S. M. Ferdous, Mahantesh M. Halappanavar, Edoardo Serra, Alex Pothen
Venue: KDD
Year: 2024

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs). AGS-GNN exploits the node features and the connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. In homophilic graphs, vertices of the same class are more likely to be adjacent, but vertices of different classes tend to be adjacent in heterophilic graphs. GNNs have been successfully applied to homophilic graphs, but their utility to heterophilic graphs remains challenging. The state-of-the-art GNNs for heterophilic graphs use the full neighborhood of a node instead of sampling it, and hence do not scale to large graphs and are not inductive. We develop dual-channel sampling techniques based on feature-similarity and feature-diversity to select subsets of neighbors for a node that capture adaptive information from homophilic and heterophilic neighborhoods. Currently, AGS-GNN is the only algorithm that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, a novel contribution in this context. We pre-compute the sampling distribution in parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (< 100K nodes) and large (- 100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compared to the state-of-the-art approaches. AGS-GNN achieves test accuracy comparable to the best-performing heterophilic GNNs, even outperforming methods that use the entire graph for node classification. AGS-GNN converges faster than methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.
Unraveling Block Maxima Forecasting Models with Counterfactual Explanation
Authors: Yue Deng 0004, Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo
Venue: KDD
Year: 2024

Disease surveillance, traffic management, and weather forecasting are some of the key applications that could benefit from block maxima forecasting of a time series as the extreme block maxima values often signify events of critical importance such as disease outbreaks, traffic gridlock, and severe weather conditions. As the use of deep neural network models for block maxima forecasting increases, so does the need for explainable AI methods that could unravel the inner workings of such black box models. To fill this need, this paper presents a novel counterfactual explanation framework for block maxima forecasting models. Unlike existing methods, our proposed framework, DiffusionCF, combines deep anomaly detection with a conditional diffusion model to identify unusual patterns in the time series that could help explain the forecasted extreme block maxima. Experimental results on several real-world datasets demonstrate the superiority of DiffusionCF over other baseline methods when evaluated according to various metrics, particularly their informativeness and closeness. Our data and codes are available at https://github.com/yue2023cs/DiffusionCF.
Advances in Human Event Modeling: From Graph Neural Networks to Language Models
Authors: Songgaojun Deng, Maarten de Rijke, Yue Ning 0001
Venue: KDD
Year: 2024

Human events such as hospital visits, protests, and epidemic outbreaks directly affect individuals, communities, and societies. These events are often influenced by factors such as economics, politics, and public policies of our society. The abundance of online data sources such as social networks, official news articles, and personal blogs chronicle societal events, facilitating the development of AI models for social science, public health care, and decision making. Human event modeling generally comprises both the forecasting stage, which estimates future events based on historical data, and interpretation, which seeks to identify influential factors of such events to understand their causative attributes. Recent achievements, fueled by deep learning and the availability of public data, have significantly advanced the field of human event modeling.
Time-Aware Attention-Based Transformer (TAAT) for Cloud Computing System Failure Prediction
Authors: Lingfei Deng, Yunong Wang, Haoran Wang, Xuhua Ma, Xiaoming Du, Xudong Zheng, Dongrui Wu
Venue: KDD
Year: 2024

Log-based failure prediction helps identify and mitigate system failures ahead of time, increasing the reliability of cloud elastic computing systems. However, most existing log-based failure prediction approaches only focus on semantic information, and do not make full use of the information contained in the timestamps of log messages. This paper proposes time-aware attention-based transformer (TAAT), a failure prediction approach that extracts semantic and temporal information simultaneously from log messages and their timestamps. TAAT first tokenizes raw log messages into specific exceptions, and then performs: 1) exception sequence embedding that reorganizes the exceptions of each node as an ordered sequence and converts them to vectors; 2) time relation estimation that computes time relation matrices from the timestamps; and, 3) time-aware attention that computes semantic correlation matrices from the exception sequences and then combines them with time relation matrices. Experiments on Alibaba Cloud demonstrated that TAAT achieves an approximately 10% performance improvement compared with the state-of-the-art approaches. TAAT is now used in the daily operation of Alibaba Cloud. Moreover, this paper also releases the real-world cloud computing failure prediction dataset used in our study, which consists of about 2.7 billion syslogs from about 300,000 node controllers during a 4-month period. To our knowledge, this is the largest dataset of its kind, and is expected to be very useful to the community.
MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion
Authors: Jiaxin Deng, Shiyao Wang 0001, Yuchen Wang, Jiansong Qi, Liqin Zhao, Guorui Zhou, Gaofeng Meng
Venue: KDD
Year: 2024

Live streaming services are becoming increasingly popular due to real-time interactions and entertainment. Viewers can chat and send comments or virtual gifts to express their preferences for the streamers. Accurately modeling the gifting interaction not only enhances users' experience but also increases streamers' revenue. Previous studies on live streaming gifting prediction treat this task as a conventional recommendation problem, and model users' preferences using categorical data and observed historical behaviors. However, it is challenging to precisely describe the real-time content changes in live streaming using limited categorical information. Moreover, due to the sparsity of gifting behaviors, capturing the preferences and intentions of users is quite difficult. In this work, we propose MMBee based on real-time <u>M</u>ulti-<u>M</u>odal Fusion and <u>Be</u>haviour <u>E</u>xpansion to address these issues. Specifically, we first present a Multi-modal Fusion Module with Learnable Query (MFQ) to perceive the dynamic content of streaming segments and process complex multi-modal interactions, including images, text comments and speech. To alleviate the sparsity issue of gifting behaviors, we present a novel Graph-guided Interest Expansion (GIE) approach that learns both user and streamer representations on large-scale gifting graphs with multi-modal attributes. It consists of two main parts: graph node representations pre-training and metapath-based behavior expansion, all of which help model jump out of the specific historical gifting behaviors for exploration and largely enrich the behavior representations. Comprehensive experiment results show that MMBee achieves significant performance improvements on both public datasets and Kuaishou real-world streaming datasets and the effectiveness has been further validated through online A/B experiments. MMBee has been deployed and is serving hundreds of millions of users at Kuaishou.
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
Authors: Xueying Ding, Yue Zhao 0016, Leman Akoglu
Venue: KDD
Year: 2024

Deep neural network based Outlier Detection (DOD) has seen a recent surge of attention thanks to the many advances in deep learning. In this paper, we consider a critical-yet-understudied challenge with unsupervised DOD, that is, effective hyperparameter (HP) tuning/model selection. While several prior work report the sensitivity of OD models to HP settings, the issue is ever so critical for the modern DOD models that exhibit a long list of HPs. We introduce HYPER for tuning DOD models, tackling two fundamental challenges: (1) validation without supervision (due to lack of labeled outliers), and (2) efficient search of the HP/model space (due to exponential growth in the number of HPs). A key idea is to design and train a novel hypernetwork (HN) that maps HPs onto optimal weights of the main DOD model. In turn, HYPER capitalizes on a single HN that can dynamically generate weights for many DOD models (corresponding to varying HPs), which offers significant speed-up. In addition, it employs meta-learning on historical OD tasks with labels to train a proxy validation function, likewise trained with our proposed HN efficiently. Extensive experiments on different OD tasks show that HYPER achieves competitive performance against 8 baselines with significant efficiency gains.
Overview of ACM SIGKDD 2024 AI4Science4AI Special Day
Authors: Wei Ding 0003, Gustau Camps-Valls
Venue: KDD
Year: 2024

This paper provides an overview of the ACM SIGKDD 2024 AI4Science4AI special day. It includes information about the organizers, invited speakers, keynote speakers, the event agenda, and insights from related workshops. The AI4Science4AI special day aims to bring together experts in artificial intelligence (AI) and science to discuss the latest developments, challenges, and future directions.
Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise
Authors: Kaize Ding, Xiaoxiao Ma 0002, Yixin Liu 0001, Shirui Pan
Venue: KDD
Year: 2024

Graph neural networks (GNNs) based on message passing have achieved remarkable performance in graph machine learning. By combining it with the power of pseudo labeling, one can further push forward the performance on the task of semi-supervised node classification. However, most existing works assume that the training node labels are purely noise-free, while this strong assumption usually does not hold in practice. GNNs will overfit the noisy training labels and the adverse effects of mislabeled nodes can be exaggerated by being propagated to the remaining nodes through the graph structure, exacerbating the model failure. Worse still, the noisy pseudo labels could also largely undermine the model's reliability without special treatment. In this paper, we revisit the role of (1) message passing and (2) pseudo labels in the studied problem and try to address two denoising subproblems from the model architecture and algorithm perspective, respectively. Specifically, we first develop a label-noise robust GNN that discards the coupled message-passing scheme. Despite its simple architecture, this learning backbone prevents overfitting to noisy labels and also inherently avoids the noise propagation issue. Moreover, we propose a novel reliable graph pseudo labeling algorithm that can effectively leverage the knowledge of unlabeled nodes while mitigating the adverse effects of noisy pseudo labels. Based on those novel designs, we can attain exceptional effectiveness and efficiency in solving the studied problem. We conduct extensive experiments on benchmark datasets for semi-supervised node classification with different levels of label noise and show new state-of-the-art performance. The code is available at https://github.com/DND-NET/DND-NET.
Unsupervised Alignment of Hypergraphs with Different Scales
Authors: Manh Tuan Do, Kijung Shin
Venue: KDD
Year: 2024

People usually interact in groups, and such groups may appear on different platforms. For instance, people often create various group chats on messaging apps (e.g., Facebook Messenger and WhatsApp) to communicate with families, friends, or colleagues. How do we identify the same people across the two platforms based on the information about the groups? This gives rise to the hypergraph alignment problem, whose objective is to find the correspondences between the sets of nodes of two hypergraphs. In a hypergraph, a node represents a person, and each hyperedge represents a group of several people. In addition, the two sets of hyperedges in the two hypergraphs can vary significantly in scales as people may use different apps at different time periods.
Next-generation Intelligent Assistants for Wearable Devices
Authors: Xin Luna Dong
Venue: KDD
Year: 2024

An intelligent assistant shall be an agent that knows you and the world, can receive your requests or predict your needs, and provide you the right services at the right time with your permission. As smart devices such as Amazon Alexa, Google Home, Ray-ban Meta get popular, Intelligent Assistants are gradually playing an important role in people's lives. The Emergence of wearable devices brings more opportunities and calls for the next generation of Intelligent Assistants. In this talk, we discuss the many challenges and opportunities we face to grow intelligent assistants from voice-only to multi-modal, from context-agnostic to context-aware, from listening to the users' requests to predicting the user's needs, and from server-side to on-device. We describe our LLM-based solutions toward multi-modality, contextualization, personalization, and retrieval-augmentation, and discuss how they are enabled on devices. We expect these new challenges to open doors to new research areas and start a new chapter for providing personal assistance services
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Authors: Yushun Dong, Binchi Zhang, Zhenyu Lei 0004, Na Zou 0001, Jundong Li
Venue: KDD
Year: 2024

Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of the involved individuals. Once trained, GNNs typically encode such information in their learnable parameters. As a consequence, privacy leakage may happen when the trained GNNs are deployed and exposed to potential attackers. Facing such a threat, machine unlearning for GNNs has become an emerging technique that aims to remove certain personal information from a trained GNN. Among these techniques, certified unlearning stands out, as it provides a solid theoretical guarantee of the information removal effectiveness. Nevertheless, most of the existing certified unlearning methods for GNNs are only designed to handle node and edge unlearning requests. Meanwhile, these approaches are usually tailored for either a specific design of GNN or a specially designed training objective. These disadvantages significantly jeopardize their flexibility. In this paper, we propose a principled framework named IDEA to achieve flexible and certified unlearning for GNNs. Specifically, we first instantiate four types of unlearning requests on graphs, and then we propose an approximation approach to flexibly handle these unlearning requests over diverse GNNs. We further provide theoretical guarantee of the effectiveness for the proposed approach as a certification. Different from existing alternatives, IDEA is not designed for any specific GNNs or optimization objectives to perform certified unlearning, and thus can be easily generalized. Extensive experiments on real-world datasets demonstrate the superiority of IDEA in multiple key perspectives.
Estimated Judge Reliabilities for Weighted Bradley-Terry-Luce Are Not Reliable
Authors: Andrew F. Dreher, Etienne Vouga, Donald S. Fussell
Venue: KDD
Year: 2024

There are many applications for which we want to learn a latent scale for subjective properties, such as the excitement of a photo or the legibility of a font; however, obtaining human-labeled data is costly and time-consuming. One oft-used method for acquiring these labels, despite the cost being quadratic in the number of items, is the method of pairwise comparisons since this method minimizes the effect of biases and generally can be used effectively outside of a controlled environment.
Representation Learning of Temporal Graphs with Structural Roles
Authors: Huaming Du, Long Shi 0002, Xingyan Chen, Yu Zhao 0019, Hegui Zhang, Carl Yang 0001, Fuzhen Zhuang, Gang Kou
Venue: KDD
Year: 2024

Temporal graph representation learning has drawn considerable attention in recent years. Most existing works mainly focus on modeling local structural dependencies of temporal graphs. However, underestimating the inherent global structural role information in many real-world temporal graphs inevitably leads to sub-optimal graph representations. To overcome this shortcoming, we propose a novel <u>R</u>ole-based <u>T</u>emporal <u>G</u>raph <u>C</u>onvolution <u>N</u>etwork (RTGCN) that fully leverages the global structural role information in temporal graphs. Specifically, RTGCN can effectively capture the static global structural roles by using hypergraph convolution neural networks. To capture the evolution of nodes' structural roles, we further design structural role-based gated recurrent units. Finally, we integrate structural role proximity in our objective function to preserve global structural similarity, further promoting temporal graph representation learning. Experimental results on multiple real-world datasets demonstrate that RTGCN consistently outperforms state-of-the-art temporal graph representation learning methods by significant margins in various temporal link prediction and node classification tasks. Specifically, RTGCN achieves AUC improvement of up to 5.1% for link prediction and F1 improvement of up to 6.2% for new link prediction. In addition, RTGCN achieves AUC improvement up to 4.6% for node classification and 2.7% for structural role classification.
Disentangled Multi-interest Representation Learning for Sequential Recommendation
Authors: Yingpeng Du, Ziyan Wang, Zhu Sun 0001, Yining Ma 0001, Hongzhi Liu 0001, Jie Zhang 0002
Venue: KDD
Year: 2024

Recently, much effort has been devoted to modeling users' multi-interests (aka multi-faceted preferences) based on their behaviors, aiming to accurately capture users' complex preferences. Existing methods attempt to model each interest of users through a distinct representation, but these multi-interest representations easily collapse into similar ones due to a lack of effective guidance. In this paper, we propose a generic multi-interest method for sequential recommendation, achieving disentangled representation learning of diverse interests technically and theoretically. To alleviate the collapse issue of multi-interests, we propose to conduct item partition guided by their likelihood of being co-purchased in a global view. It can encourage items in each group to focus on a discriminated interest, thus achieving effective disentangled learning of multi-interests. Specifically, we first prove the theoretical connection between item partition and spectral clustering, demonstrating its effectiveness in alleviating item-level and facet-level collapse issues that hinder existing disentangled methods. To efficiently optimize this problem, we then propose a Markov Random Field (MRF)-based method that samples small-scale sub-graphs from two separate MRFs, thus it can be approximated with a cross-entropy loss and optimized through contrastive learning. Finally, we perform multi-task learning to seamlessly align item partition learning with multi-interest modeling for more accurate recommendation. Experiments on three real-world datasets show that our method significantly outperforms state-of-the-art methods and can flexibly integrate with existing multi-interest models as a plugin to enhance their performances.
Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks
Authors: Wenying Duan, Tianxiang Fang, Hong Rao, Xiaoxi He
Venue: KDD
Year: 2024

In this paper, we present a novel method to significantly enhance the computational efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing the concept of the Graph Winning Ticket (GWT), derived from the Lottery Ticket Hypothesis (LTH). By adopting a pre-determined star topology as a GWT prior to training, we balance edge reduction with efficient information propagation, reducing computational demands while maintaining high model performance. Both the time and memory computational complexity of generating adaptive spatial-temporal graphs is significantly reduced from O(N2) to O(N). Our approach streamlines the ASTGNN deployment by eliminating the need for exhaustive training, pruning, and retraining cycles, and demonstrates empirically across various datasets that it is possible to achieve comparable performance to full models with substantially lower computational costs. Specifically, our approach enables training ASTGNNs on the largest scale spatial-temporal dataset using a single A6000 equipped with 48 GB of memory, overcoming the out-of-memory issue encountered during original training and even achieving state-of-the-art performance. Furthermore, we delve into the effectiveness of the GWT from the perspective of spectral graph theory, providing substantial theoretical support. This advancement not only proves the existence of efficient sub-networks within ASTGNNs but also broadens the applicability of the LTH in resource-constrained settings, marking a significant step forward in the field of graph neural networks. Code is available at https://anonymous.4open.science/r/paper-1430.