OpenRaiser/Intern-Atlas huggingface dataset:
Causal Discovery in Temporal Domain from Interventional Data
Authors: Peiwen Li, Yuan Meng, Xin Wang, Fang Shen, Yue Li, Jialong Wang, Wenwu Zhu
Venue: CIKM
Year: 2023

Causal learning from observational data has garnered attention as controlled experiments can be costly. To enhance identifiability, incorporating intervention data has become a mainstream approach. However, these methods have yet to be explored in the context of time series data, despite their success in static data. To address this research gap, this paper presents a novel contribution. Firstly, a temporal interventional dataset with causal labels is introduced, derived from a data center IT room of a cloud service company. Secondly, this paper introduces TECDI, a novel approach for temporal causal discovery. TECDI leverages the smooth, algebraic characterization of acyclicity in causal graphs to efficiently uncover causal relationships. Experimental results on simulated and proposed real-world datasets validate the effectiveness of TECDI in accurately uncovering temporal causal relationships. The introduction of the temporal interventional dataset and the superior performance of TECDI contribute to advancing research in temporal causal discovery. Our datasets and codes have released at~\hrefhttps://github.com/lpwpower/TECDI https://github.com/lpwpower/TECDI.
AutoMRM: A Model Retrieval Method Based on Multimodal Query and Meta-learning
Authors: Zhaotian Li, Binhang Qi, Hailong Sun, Xiang Gao
Venue: CIKM
Year: 2023

With more and more Deep Neural Network (DNN) models are publicly available on model sharing platforms (e.g., HuggingFace), model reuse has become a promising way in practice to improve the efficiency of DNN model construction by avoiding the costs of model training. To that end, a pivotal step for model reuse is model retrieval, which facilitates discovering suitable models from a model hub that match the requirements of users. However, the existing model retrieval methods have inadequate performance and efficiency, since they focus on matching user requirements with the model names, and thus cannot work well for high-dimensional data such as images. In this paper, we propose a user-task-centric multimodal model retrieval method named AutoMRM. AutoMRM can retrieve DNN models suitable for the user's task according to both the dataset and description of the task. Moreover, AutoMRM utilizes meta-learning to retrieve models for previously unseen task queries. Specifically, given a task, AutoMRM extracts the latent meta-features from the dataset and description for training meta-learners offline and obtaining the representation of user task queries online. Experimental results demonstrate that AutoMRM outperforms existing model retrieval methods including the state-of-the-art method in both effectiveness and efficiency.
REST: Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum Learning
Authors: Xinhang Li, Zhaopeng Qiu, Xiangyu Zhao, Yong Zhang, Chunxiao Xing, Xian Wu
Venue: CIKM
Year: 2023

Accurate prediction of drug-drug interaction (DDI) is crucial to achieving effective decision-making in medical treatment for both doctors and patients. Recently, many deep learning based methods have been proposed to learn from drug-related features and conduct DDI prediction. These works have achieved promising results. However, the extreme imbalance of medical data poses a serious problem to DDI prediction, where a small fraction of DDI types occupy the majority training data. A straightforward way is to develop an appropriate policy to sample the data. Due to the high complexity and speciality of medical science, a dynamic learnable policy is required instead of a heuristic, uniform or static one. Therefore, we propose a REinforced Student-Teacher curriculum learning model (REST) for effective sampling to tackle this imbalance problem. Specifically, REST consists of two interactive parts, which are a heterogeneous graph neural network as the student and a reinforced sampler as the teacher. In each interaction, the teacher model takes action to sample an appropriate batch to train the student model according to the student model state while the cumulated improvement in performance of the student model is treated as the reward for policy gradient of the teacher model. The experimental results on two benchmarking datasets have demonstrated the significant effectiveness of our proposed model in DDI prediction, especially for the DDI types with low frequency.
Multi-Order Relations Hyperbolic Fusion for Heterogeneous Graphs
Authors: Junlin Li, Yueheng Sun, Minglai Shao
Venue: CIKM
Year: 2023

Heterogeneous graphs with multiple node and edge types are prevalent in real-world scenarios. However, most methods use meta-paths on the original graph structure to learn information in heterogeneous graphs, and these methods only consider pairwise relations and rely on meta-paths. In this paper, we use simplicial complexes to extract higher-order relations containing multiple nodes from heterogeneous graphs. We also discover power-law structures in both the heterogeneous graph and the extracted simplicial complex. Thus, we propose the Simplicial Hyperbolic Attention Network (SHAN), a graph neural network for heterogeneous graphs. SHAN extracts simplicial complexes and the original graph structure from the heterogeneous graph to represent multi-order relations between nodes. Next, SHAN uses hyperbolic multi-perspective attention to learn the importance of different neighbors and relations in hyperbolic space. Finally, SHAN integrates multi-order relations to obtain a more comprehensive node representation. We conducted extensive experiments to verify the effectiveness of SHAN and the results of node classification experiments on three publicly available heterogeneous graph datasets demonstrate that SHAN outperforms representative baseline models.
Temporal and Topological Augmentation-based Cross-view Contrastive Learning Model for Temporal Link Prediction
Authors: Dongyuan Li, Shiyin Tan, Yusong Wang, Kotaro Funakoshi, Manabu Okumura
Venue: CIKM
Year: 2023

With the booming development of social media, temporal link prediction (TLP), as a core technology, has been receiving increasing attention. However, current methods are based on graph neural networks, which suffer from the over-smoothing issue and easily yield indistinguishable node representations, degrading the prediction accuracy. Besides, they lack the ability to eliminate noisy temporal information and ignore the importance of high-order neighbor information for measuring the link probability between nodes. To solve these issues, we design a cross-view graph contrastive learning (GCL) framework for TLP, called Tacl. We first design two augmented views for GCL by enhancing the temporal and topological information to obtain distinguishable node representations. Then, we learn the evolution rule of temporal networks to help constrain consistency of node representations and eliminate noise. Finally, we incorporate the high-order neighbor information to measure the link probability between nodes. Extensive experiments demonstrate the effectiveness and robustness of Tacl.
Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation
Authors: Qingyao Li, Wei Xia, Li&apos, ang Yin, Jian Shen, Renting Rui, Weinan Zhang, Xianyu Chen, Ruiming Tang, Yong Yu
Venue: CIKM
Year: 2023

Goal-oriented Learning path recommendation aims to recommend learning items (concepts or exercises) step-by-step to a learner to promote the mastery level of her specific learning goals. By formulating this task as a Markov decision process, reinforcement learning (RL) methods have demonstrated great power. Although extensive research efforts have been made, previous methods still fail to recommend effective goal-oriented paths due to the under-utilizing of goals. Specifically, it is mainly reflected in two aspects: (1)The lack of goal planning. When learners have multiple goals with different difficulties, the previous methods can't fully utilize the difficulties and dependencies between goal learning items to plan the sequence of achieving these goals, making the path chaotic and inefficient; (2)The lack of efficiency in goal achieving. When pursuing a single goal, the path may contain learning items unrelated to the goal, which makes realizing a certain goal inefficient. To address these challenges, we present a novel Graph Enhanced Hierarchical Reinforcement Learning (GEHRL) framework for goal-oriented learning path recommendation. The framework divides learning path recommendation into two parts: sub-goal selection(planning) and sub-goal achieving(learning item recommendation). Specifically, we employ a high-level agent as a sub-goal selector to select sub-goals for the low-level agent to achieve. The low-level agent in the framework is to recommend learning items to the learner. To make the path only contain goal-related learning items to improve the efficiency of achieving the goal, we develop a graph-based candidate selector to constrain the action space of the low-level agent based on the sub-goal and knowledge graph. We also develop test-based internal reward for low-level training so that the sparsity problem of external reward can be alleviated. Extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.
Class-Specific Word Sense Aware Topic Modeling via Soft Orthogonalized Topics
Authors: Wenbo Li, Yao Yang, Einoshin Suzuki
Venue: CIKM
Year: 2023

We propose a word sense aware topic model for document classification based on soft orthogonalized topics. An essential problem for this task is to capture word senses related to classes, i.e., class-specific word senses. Traditional models mainly introduce semantic information of knowledge libraries for word sense discovery. However, this information may not align with the classification targets, because these targets are often subjective and task-related. We aim to model the class-specific word senses in topic space. The challenge is to optimize the class separability of the senses, i.e., obtaining sense vectors with (a) high intra-class and (b) low inter-class similarities. Most existing models predefine specific topics for each class to specify the class-specific sense vectors. We call them hard orthogonalization based methods. These methods can hardly achieve both (a) and (b) since they assume the conditional independence of topics to classes and inevitably lose topic information. To this problem, we propose soft orthogonalization for topics. Specifically, we reserve all the topics and introduce a group of class-specific weights for each word to handle the importance of topic dimensions to class separability. Besides, we detect and use highly class-specific words in each document to guide sense estimation. Our experiments on two standard datasets show that our proposal outperforms other state-of-the-art models in terms of accuracy of sense estimation, document classification, and topic modeling. In addition, our joint learning experiments with the pre-trained language model BERT showcased the best complementarity of our model in most cases compared to other topic models.
Heterogeneous Temporal Graph Neural Network Explainer
Authors: Jiazheng Li, Chunhui Zhang, Chuxu Zhang
Venue: CIKM
Year: 2023

Graph Neural Networks (GNNs) have been a prominent research area and have been widely deployed in various high-stakes applications in recent years, leading to a growing demand for explanations. While existing explainer methods focus on explaining homogeneous and static GNNs, none of them have attempted to explain heterogeneous temporal GNNs. However, in practice, many real-world databases should be represented as heterogeneous temporal graphs (HTGs), which serve as the fundamental data structure for GNN backbone models in applications. To address this gap, in this paper, we propose HTGExplainer, a novel method for explaining heterogeneous temporal GNNs by considering temporal dependencies and preserving heterogeneity when generating subgraphs as explanations. HTGExplainer employs a deep neural network to re-parameterize the generation process of explanations and incorporates effective heterogeneous and temporal edge embeddings to capture informative semantics used for generating explanatory subgraphs. Extensive experiments are conducted on multiple HTG datasets constructed from real-world scenarios, and the results demonstrate the superior performance of HTGExplainer compared to state-of-the-art baselines.
Towards Automatic ICD Coding via Knowledge Enhanced Multi-Task Learning
Authors: Xinhang Li, Xiangyu Zhao, Yong Zhang, Chunxiao Xing
Venue: CIKM
Year: 2023

The aim of ICD coding is to assign International Classification of Diseases (ICD) codes to unstructured clinical notes or discharge summaries. Numerous methods have been proposed for automatic ICD coding in an effort to reduce human labor and errors. However, existing works disregard the data imbalance problem of clinical notes. In addition, the noisy clinical note issue has not been thoroughly investigated. To address such issues, we propose a knowledge enhanced Graph Attention Network (GAT) under multi-task learning setting. Specifically, multi-level information transitions and interactions have been implemented. On the one hand, a large heterogeneous text graph is constructed to capture both intra- and inter-note correlations between various semantic concepts, thereby alleviating the data imbalance issue. On the other hand, two auxiliary healthcare tasks have been proposed to facilitate the sharing of information across tasks. Moreover, to tackle the issue of noisy clinical notes, we propose to utilize the rich structured knowledge facts and information provided by medical domain knowledge, thereby encouraging the model to focus on the clinical notes' noteworthy portion and valuable information. The experimental results on the widely-used medical dataset, MIMIC-III, demonstrate the advantages of our proposed framework.
Retrieving GNN Architecture for Collaborative Filtering
Authors: Fengqi Liang, Huan Zhao, Zhenyi Wang, Wei Fang, Chuan Shi
Venue: CIKM
Year: 2023

Graph Neural Networks (GNNs) have been widely used in Collaborative Filtering (CF). However, when given a new recommendation scenario, the current options are either selecting from existing GNN architectures or employing Neural Architecture Search (NAS) to obtain a well-performing GNN model, both of which are expensive in terms of human expertise or computational resources.To address the problem, in this work,we propose a novel neural retrieval approach, dubbed RGCF, to search a well-performing architecture for GNN-based CF rapidly when handling new scenarios. Specifically, we design the neural retrieval approach based on meta-learning by developing two-level meta-features, ranking loss, and task-level data augmentation, and in a retrieval paradigm, RGCF can directly return a well-performing architecture given a new dataset (query), thus being efficient inherently. Experimental results on two mainstream tasks, i.e., rating prediction and item ranking, show that RGCF outperforms all models either by human-designed or NAS on two new datasets in terms of effectiveness and efficiency. Particularly, the efficiency improvement is significant, taking as an example that RGCF is 61.7-206.3x faster than a typical reinforcement learning based NAS approach on the two new datasets. Code and data are available at https://github.com/BUPT-GAMMA/RGCF.
Bias Invariant Approaches for Improving Word Embedding Fairness
Authors: Siyu Liao, Rongting Zhang, Barbara Poblete, Vanessa Murdock
Venue: CIKM
Year: 2023

Many public pre-trained word embeddings have been shown to encode different types of biases. Embeddings are often obtained from training on large pre-existing corpora, and therefore resulting biases can be a reflection of unfair representations in the original data. Bias, in this scenario, is a challenging problem since current mitigation techniques require knowing and understanding existing biases in the embedding, which is not always possible. In this work, we propose to improve word embedding fairness by borrowing methods from the field of data privacy. The idea behind this approach is to treat bias as if it were a special type of training data leakage. This has the unique advantage of not requiring prior knowledge of potential biases in word embeddings. We investigated two types of privacy algorithms, and measured their effect on bias using four different metrics. To investigate techniques from differential privacy, we applied Gaussian perturbation to public pre-trained word embeddings. To investigate noiseless privacy, we applied vector quantization during training. Experiments show that both approaches improve fairness for commonly used embeddings, and additionally, noiseless privacy techniques reduce the size of the resulting embedding representation.
On the Thresholding Strategy for Infrequent Labels in Multi-label Classification
Authors: Yu-Jen Lin, Chih-Jen Lin
Venue: CIKM
Year: 2023

In multi-label classification, the imbalance between labels is often a concern. For a label that seldom occurs, the default threshold used to generate binarized predictions of that label is usually sub-optimal. However, directly tuning the threshold to optimize F-measure has been observed to overfit easily. In this work, we explain why this overfitting occurs. Then, we analyze the FBR heuristic, a previous technique proposed to address the overfitting issue. We explain its success but also point out some problems unobserved before. Then, we first propose a variant of the FBR heuristic that not only fixes the problems but is also more justifiable. Second, we propose a new technique based on smoothing the F-measure when tuning the threshold. We theoretically prove that, with proper parameters, smoothing results in desirable properties of the tuned threshold. Based on the idea of smoothing, we then propose jointly optimizing micro-F and macro-F as a lightweight alternative free from extra hyperparameters. Our methods are empirically evaluated on text and node classification datasets. The results show that our methods consistently outperform the FBR heuristic.
Personalized Differentially Private Federated Learning without Exposing Privacy Budgets
Authors: Junxu Liu, Jian Lou, Li Xiong, Xiaofeng Meng
Venue: CIKM
Year: 2023

The meteoric rise of cross-silo Federated Learning (FL) is due to its ability to mitigate data breaches during collaborative training. To further provide rigorous privacy protection with consideration of the varying privacy requirements across different clients, a privacy-enhanced line of work on personalized differentially private federated learning (PDP-FL) has been proposed. However, the existing solution for PDP-FL [20] assumes the raw privacy budgets of all clients should be collected by the server. These values are then directly utilized to improve the model utility via facilitating the privacy preferences partitioning (i.e., partitioning all clients into multiple privacy groups). It is however non-realistic because the raw privacy budgets can be quite informative and sensitive. In this work, our goal is to achieve PDP-FL without exposing clients' raw privacy budgets by indirectly partitioning the privacy preferences solely based on clients' noisy model updates. The crux lies in the fact that the noisy updates could be influenced by two entangled factors of DP noises and non-IID clients' data, leaving it unknown whether it is possible to uncover privacy preferences by disentangling the two affecting factors. To overcome the hurdle, we systematically investigate the unexplored question of under what conditions can the model updates of clients be primarily influenced by noise levels rather than data distribution. Then, we propose a simple yet effective strategy based on clustering the L2 norm of the noisy updates, which can be integrated into the vanilla PDP-FL to maintain the same performance. Experimental results demonstrate the effectiveness and feasibility of our privacy-budget-agnostic PDP-FL method.
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method
Authors: Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng
Venue: CIKM
Year: 2023

Neural ranking models (NRMs) and dense retrieval (DR) models have given rise to substantial improvements in overall retrieval performance. In addition to their effectiveness, and motivated by the proven lack of robustness of deep learning-based approaches in other areas, there is growing interest in the robustness of deep learning-based approaches to the core retrieval problem. Adversarial attack methods that have so far been developed mainly focus on attacking NRMs, with very little attention being paid to the robustness of DR models. In this paper, we introduce the adversarial retrieval attack (AREA) task. The AREA task is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model in response to a query. We consider the decision-based black-box adversarial setting, which is realistic in real-world search engines. To address the AREA task, we first employ existing adversarial attack methods designed for NRMs. We find that the promising results that have previously been reported on attacking NRMs, do not generalize to DR models: these methods underperform a simple term spamming method. We attribute the observed lack of generalizability to the interaction-focused architecture of NRMs, which emphasizes fine-grained relevance matching. DR models follow a different representation-focused architecture that prioritizes coarse-grained representations. We propose to formalize attacks on DR models as a contrastive learning problem in a multi-view representation space. The core idea is to encourage the consistency between each view representation of the target document and its corresponding viewer via view-wise supervision signals. Experimental results demonstrate that the proposed method can significantly outperform existing attack strategies in misleading the DR model with small indiscernible text perturbations.