OpenRaiser/Intern-Atlas huggingface dataset:
Workplace Recommendation with Temporal Network Objectives
Authors: Kiran Tomlinson, Jennifer Neville, Longqi Yang 0001, Mengting Wan, Cao Lu
Venue: KDD
Year: 2023

Workplace communication software such as Microsoft Teams, Slack, and Google Workspace have become integral to workplace collaboration, especially due to the rise of remote work. By making it easier to access relevant or useful information, recommender systems for these platforms have the potential to improve efficient cross-team information flow through a company's communication network. While there has been some recent work on recommendation approaches that optimize network objectives, these have focused on static graphs. In this work, we focus on optimizing information flow, which is highly temporal and presents a number of novel algorithmic challenges. To overcome these, we develop tractable measures of temporal information flow and design efficient online recommendation algorithms that jointly optimize for relevance and cross-team information flow. We demonstrate the potential for impact of these approaches on a rich multi-modal dataset capturing one month of communication between 180k Microsoft employees through email, chats and posts on Microsoft Teams, and file sharing on SharePoint. We design an offline model-based evaluation pipeline to estimate the effects of recommendations on the temporal communication network. We show that our recommendation algorithms can significantly improve cross-team information flow with only a small decrease in traditional relevance metrics.
Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments
Authors: Ha Xuan Tran, Thuc Duy Le, Jiuyong Li, Lin Liu 0003, Xiaomei Li, Jixue Liu, Tony Waters
Venue: KDD
Year: 2023

In Disability Employment Services (DES), an emerging problem is to make job survival analysis stable in unseen environments without prior knowledge of these environments. Existing survival analysis methods cannot adequately solve this problem since they assume that distribution of unseen data is similar to that observed during training. However, this assumption can be violated in practice where unanticipated events such as COVID19 and inflation can change the work and life patterns of people with disability. Models trained before the COVID19 pandemic may make unreliable job survival predictions in COVID19 or inflation situations. It is also costly and time consuming to frequently re-train and deploy the models. This paper proposes a stable survival analysis method for the DES sector without requiring prior knowledge of deployment environments. Latent representations are learned to capture non-linear relationships between relevant features and job survival time. Two reweighting stages are developed to remove censoring and conditional spurious correlations between irrelevant features and the survival outcome. The case study of Australian workers with disability shows that our method can make stable risk predictions. It can also help workers with disability determine the most effective skills for improvement to increase their job survival time. Further evaluations with public datasets show the promising stable performance of our method in other applications.
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Authors: Vy Vo, Trung Le 0001, Van Nguyen 0002, He Zhao 0001, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung
Venue: KDD
Year: 2023

Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach is through counterfactual explanations, which provide suggestions on what a user can do to alter an outcome. Not only must a counterfactual example counter the original prediction from the black-box classifier but it should also satisfy various constraints for practical applications. Diversity is one of the critical constraints that however remains less discussed. While diverse counterfactuals are ideal, it is computationally challenging to simultaneously address some other constraints. Furthermore, there is a growing privacy concern over the released counterfactual data. To this end, we propose a feature-based learning framework that effectively handles the counterfactual constraints and contributes itself to the limited pool of private explanation models. We demonstrate the flexibility and effectiveness of our method in generating diverse counterfactuals of actionability and plausibility. Our counterfactual engine is more efficient than counterparts of the same capacity while yielding the lowest re-identification risks.
Incremental Causal Graph Learning for Online Root Cause Analysis
Authors: Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, Haifeng Chen
Venue: KDD
Year: 2023

The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure recovery and mitigate system damages or financial losses. However, previous research has mostly focused on developing offline RCA algorithms, which often require manually initiating the RCA process, a significant amount of time and data to train a robust model, and then being retrained from scratch for a new system fault.
A Message Passing Neural Network Space for Better Capturing Data-dependent Receptive Fields
Authors: Zhili Wang, Shimin Di, Lei Chen 0002
Venue: KDD
Year: 2023

Recently, the message passing neural network (MPNN) has attracted a lot of attention, which learns node representations based on the receptive field of the given node. Despite its success in many graph-related tasks, recent studies find that conventional MPNNs are incapable of handling variant receptive fields required in different graphs, and thereby some upgraded MPNNs have been developed. However, these methods are limited to designing a common solution for different graphs, which fails to capture the impact of different graph properties on the receptive fields. To alleviate such issues, we propose a novel MPNN space for data-dependent receptive fields (MpnnDRF), which enables us to dynamically design suitable MPNNs to capture the receptive field for the given graph. More concretely, we systemically investigate the capability of existing designs and propose several key design dimensions to improve them. Then, to fully explore the proposed designs and useful designs in existing works, we propose a novel search space to incorporate them and formulate a search framework. In the empirical study, the proposed MpnnDRF shows very strong robustness against the increased receptive field, which allows MpnnDRF to learn node representations based on a larger perceptual field. Therefore, MpnnDRF consistently achieves outstanding performance on benchmark node and graph classification tasks.
Federated Few-shot Learning
Authors: Song Wang 0013, Xingbo Fu, Kaize Ding, Chen Chen 0022, Huiyuan Chen, Jundong Li
Venue: KDD
Year: 2023

Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients can only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as federated few-shot learning. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines.
Learning to Discover Various Simpson's Paradoxes
Authors: Jingwei Wang 0001, Jianshan He, Weidi Xu, Ruopeng Li, Wei Chu
Venue: KDD
Year: 2023

Simpson's paradox is a well-known statistical phenomenon that has captured the attention of statisticians, mathematicians, and philosophers for more than a century. The paradox often confuses people when it appears in data, and ignoring it may lead to incorrect decisions. Recent studies have found many examples of Simpson's paradox in social data and proposed a few methods to detect the paradox automatically. However, these methods suffer from many limitations, such as being only suitable for categorical variables or one specific paradox. To address these problems, we develop a learning-based approach to discover various Simpson's paradoxes. Firstly, we propose a framework from a statistical perspective that unifies multiple variants of Simpson's paradox currently known. Secondly, we present a novel loss function, Multi-group Pearson Correlation Coefficient (MPCC), to calculate the association strength of two variables of multiple subgroups. Then, we design a neural network model, coined SimNet, to automatically disaggregate data into multiple subgroups by optimizing the MPCC loss. Experiments on various datasets demonstrate that SimNet can discover various Simpson's paradoxes caused by discrete and continuous variables, even hidden variables. The code is available at https://github.com/ant-research/Learning-to-Discover-Various-Simpson-Paradoxes.
Empower Post-hoc Graph Explanations with Information Bottleneck: A Pre-training and Fine-tuning Perspective
Authors: Jihong Wang 0003, Minnan Luo, Jundong Li, Yun Lin 0001, Yushun Dong, Jin Song Dong 0001, Qinghua Zheng
Venue: KDD
Year: 2023

Researchers recently investigated to explain Graph Neural Networks (GNNs) on the access to a task-specific GNN, which may hinder their wide applications in practice. Specifically, task-specific explanation methods are incapable of explaining pretrained GNNs whose downstream tasks are usually inaccessible, not to mention giving explanations for the transferable knowledge in pretrained GNNs. Additionally, task-specific methods only consider target models' output in the label space, which are coarse-grained and insufficient to reflect the model's internal logic. To address these limitations, we consider a two-stage explanation strategy, i.e., explainers are first pretrained in a task-agnostic fashion in the representation space and then further fine-tuned in the task-specific label space and representation space jointly if downstream tasks are accessible. The two-stage explanation strategy endows post-hoc graph explanations with the applicability to pretrained GNNs where downstream tasks are inaccessible and the capacity to explain the transferable knowledge in the pretrained GNNs. Moreover, as the two-stage explanation strategy explains the GNNs in the representation space, the fine-grained information in the representation space also empowers the explanations. Furthermore, to achieve a trade-off between the fidelity and intelligibility of explanations, we propose an explanation framework based on the Information Bottleneck principle, named Explainable Graph Information Bottleneck (EGIB). EGIB subsumes the task-specific explanation and task-agnostic explanation into a unified framework. To optimize EGIB objective, we derive a tractable bound and adopt a simple yet effective explanation generation architecture. Based on the unified framework, we further theoretically prove that task-agnostic explanation is a relaxed sufficient condition of task-specific explanation, which indicates the transferability of task-agnostic explanations. Extensive experimental results demonstrate the effectiveness of our proposed explanation method.
ECGGAN: A Framework for Effective and Interpretable Electrocardiogram Anomaly Detection
Authors: Huazhang Wang, Zhaojing Luo, James Wei Luen Yip, Chuyang Ye, Meihui Zhang 0001
Venue: KDD
Year: 2023

Heart is the most important organ of the human body, and Electrocardiogram (ECG) is an essential tool for clinical monitoring of heart health and detecting cardiovascular diseases. Automatic detection of ECG anomalies is of great significance and clinical value in healthcare. However, performing automatic anomaly detection for the ECG data is challenging because we not only need to accurately detect the anomalies but also need to provide clinically meaningful interpretation of the results. Existing works on automatic ECG anomaly detection either rely on hand-crafted designs of feature extraction algorithms which are typically too simple to deliver good performance, or deep learning for automatically extracting features, which is not interpretable.
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
Authors: Yifan Wang 0017, Peijie Sun, Min Zhang 0006, Qinglin Jia, Jingjie Li, Shaoping Ma
Venue: KDD
Year: 2023

Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retraining models on recent data daily or weekly. However, the delay of conversion behavior usually leads to incorrect labeling, which is called delayed feedback problem. Existing work may fail to introduce the correct information about false negative samples due to data sparsity and dynamic data distribution. To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. Firstly, we theoretically prove that the label-corrected loss is an unbiased estimate of the oracle loss using true labels. Then, as there are no ready training data for label correction, counterfactual labeling is used to construct artificial training data. Furthermore, since counterfactual labeling utilizes only partial training data, we design an embedding-based alternative training method to enhance performance. Comparative experiments on both public and private datasets and detailed analyses show that our proposed approach effectively alleviates the delayed feedback problem and consistently outperforms the previous state-of-the-art methods.
Contrastive Meta-Learning for Few-shot Node Classification
Authors: Song Wang 0013, Zhen Tan 0001, Huan Liu 0001, Jundong Li
Venue: KDD
Year: 2023

Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. To tackle such a label shortage issue, existing works generally leverage the meta-learning framework, which utilizes a number of episodes to extract transferable knowledge from classes with abundant labeled nodes and generalizes the knowledge to other classes with limited labeled nodes. In essence, the primary aim of few-shot node classification is to learn node embeddings that are generalizable across different classes. To accomplish this, the GNN encoder must be able to distinguish node embeddings between different classes, while also aligning embeddings for nodes in the same class. Thus, in this work, we propose to consider both the intra-class and inter-class generalizability of the model. We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs. First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes. Second, we strengthen the inter-class generalizability by generating hard node classes for classification via a novel similarity-sensitive mix-up strategy. Extensive experiments on prevalent few-shot node classification datasets verify the effectiveness of our framework and demonstrate its superiority over other state-of-the-art baselines.
Learning to Schedule in Diffusion Probabilistic Models
Authors: Yunke Wang, Xiyu Wang, Anh-Dung Dinh, Bo Du 0001, Charles Xu 0002
Venue: KDD
Year: 2023

Recently, the field of generative models has seen a significant advancement with the introduction of Diffusion Probabilistic Models (DPMs). The Denoising Diffusion Implicit Model (DDIM) was designed to reduce computational time by skipping a number of steps in the inference process of DPMs. However, the hand-crafted sampling schedule in DDIM, which relies on human expertise, has its limitations in considering all relevant factors in the sampling process. Additionally, the assumption that all instances should have the same schedule is not always valid. To address these problems, this paper proposes a method that leverages reinforcement learning to automatically search for an optimal sampling schedule for DPMs. This is achieved by a policy network that predicts the next step to visit based on the current state of the noisy image. The optimization of the policy network is accomplished using an episodic actor-critic framework, which incorporates reinforcement learning. Empirical results demonstrate the superiority of our approach over various datasets with different timesteps. We also observe that the trained sampling schedule has a strong generalization ability across different DPM baselines.
2nd Workshop on Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond
Authors: Chu Wang, Yingfei Wang, Haipeng Luo, Daniel R. Jiang, Jinghai He, Zeyu Zheng 0002
Venue: KDD
Year: 2023

The areas of reinforcement learning and multi-armed bandits have recently seen significant innovation, while many application domains, such as e-commerce, are full of problems and challenges to which vanilla RL or MAB methods cannot directly apply. This workshop aims at filling this communication gap by creating a platform for researchers and practitioners from both the method/theory side and application side of the community. Having this platform now instead of at a later time is beneficial to all sides of the community: practitioners and frontline scientists are able to avoid re-inventing existing techniques; theory-oriented researchers can find motivation in industry problems, working within more realistic settings, and making real-world impact. The 2nd Multi-armed Bandits and Reinforcement Learning Workshop was a half day workshop co-located with the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2023) in Long Beach, California.
Efficient Sparse Linear Bandits under High Dimensional Data
Authors: Xue Wang 0010, Mike Mingcheng Wei, Tao Yao
Venue: KDD
Year: 2023

We propose a computationally efficient Lasso Random Project Bandit (LRP-Bandit) algorithm for sparse linear bandit problems under high-dimensional settings with limited samples. LRP-Bandit bridges Lasso and Random Projection as feature selection and dimension reduction techniques to alleviate the computational complexity and improve the regret performance. We demonstrate that for the total feature dimension d, the significant feature dimension s, and the sample size T, the expected cumulative regret under LRP-Bandit is upper bounded by Õ (T2 over 3 s 3 over 2 log7 over 6 d), where Õ suppresses the logarithmic dependence on T. Further, we show that when available samples are larger than a problem-dependent threshold, the regret upper bound for LRP-Bandit can be further improved to Õ (s√T log d). These regret upper bounds on T for both data-poor and data-rich regimes match the theoretical minimax lower bounds up to logarithmic factors. Through experiments, we show that LRP-Bandit is computationally efficient and outperforms other benchmarks on the expected cumulative regret.
Accelerating Antimicrobial Peptide Discovery with Latent Structure
Authors: Danqing Wang, Zeyu Wen, Fei Ye, Lei Li 0005, Hao Zhou 0012
Venue: KDD
Year: 2023

Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previous studies mainly focus on peptide sequence attributes and do not consider crucial structure information. In this paper, we propose a latent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploits multi-scale vector quantization in the latent space to represent secondary structures (e.g. alpha helix and beta sheet). By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures. Experimental results show that the peptides generated by LSSAMP have a high probability of antimicrobial activity. Our wet laboratory experiments verified that two of the 21 candidates exhibit strong antimicrobial activity. The code is released at https://github.com/dqwang122/LSSAMP.
Efficient and Effective Edge-wise Graph Representation Learning
Authors: Hewen Wang, Renchi Yang, Keke Huang, Xiaokui Xiao
Venue: KDD
Year: 2023

Graph representation learning (GRL) is a powerful tool for graph analysis, which has gained massive attention from both academia and industry due to its superior performance in various real-world applications. However, the majority of existing works for GRL are dedicated to node-based tasks and thus focus on producing node representations. Despite such methods can be used to derive edge representations by regarding edges as nodes, they suffer from sub-par result utility in practical edge-wise applications, such as financial fraud detection and review spam combating, due to neglecting the unique properties of edges and their inherent drawbacks. Moreover, to our knowledge, there is a paucity of research devoted to edge representation learning. These methods either require high computational costs in sampling random walks or yield severely compromised representation quality because of falling short of capturing high-order information between edges. To address these challenges, we present TER and AER, which generate high-quality edge representation vectors based on the graph structure surrounding edges and edge attributes, respectively. In particular, TER can accurately encode high-order proximities of edges into low-dimensional vectors in a practically efficient and theoretically sound way, while AER augments edge attributes through a carefully-designed feature aggregation scheme. Our extensive experimental study demonstrates that the combined edge representations of TER and AER can achieve significantly superior performance in terms of edge classification on 8 real-life datasets, while being up to one order of magnitude faster than 16 baselines on large graphs.