GenAI4ELab/papercli-papers huggingface dataset:
Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
Authors: Kang Luo; Yuanshao Zhu; Wei Chen; Kun Wang; Zhengyang Zhou; Sijie Ruan; Yuxuan Liang
Venue: IJCAI 2024 (main)

Abstract: Trajectory modeling refers to characterizing human movement behavior, serving as a pivotal step in understanding mobility patterns. Nevertheless, existing studies typically ignore the confounding effects of geospatial context, leading to the acquisition of spurious correlations and limited generalization capabilities. To bridge this gap, we initially formulate a Structural Causal Model (SCM) to decipher the trajectory representation learning process from a causal perspective. Building upon the SCM, we further present a Trajectory modeling framework (TrajCL) based on Causal Learning, which leverages the backdoor adjustment theory as an intervention tool to eliminate the spurious correlations between geospatial context and trajectories. Extensive experiments on two real-world datasets verify that TrajCL markedly enhances performance in trajectory classification tasks while showcasing superior generalization and interpretability.
Massively Parallel Single-Source SimRanks in O(log N) Rounds
Authors: Siqiang Luo; Zulun Zhu
Venue: IJCAI 2024 (main)

Abstract: SimRank is one of the most fundamental measures that evaluate the structural similarity between two nodes in a graph and has been applied in a plethora of data mining and machine learning tasks. These tasks often involve single-source SimRank computation that evaluates the SimRank values between a source node u and all other nodes. Due to its high computation complexity, single-source SimRank computation for large graphs is notoriously challenging, and hence recent studies resort to distributed processing. To our surprise, although SimRank has been widely adopted for two decades, theoretical aspects of distributed SimRanks with provable results have rarely been studied. In this paper, we conduct a theoretical study on single-source SimRank computation in the Massive Parallel Computation (MPC) model, which is the standard theoretical framework modeling distributed systems. Existing distributed SimRank algorithms enforce either Ω(log n) communication round complexity or Ω(n) machine space for a graph of n nodes. We overcome this barrier. Particularly, given a graph of n nodes, for any query node v and constant error ϵ>3/n, we show that using O(log² log n) rounds of communication among machines is enough to compute single-source SimRank values with at most ϵ absolute errors, while each machine only needs a space sub-linear to n. To the best of our knowledge, this is the first single-source SimRank algorithm in MPC that can overcome the Θ(log n) round complexity barrier with provable result accuracy.
DGCD: An Adaptive Denoising GNN for Group-level Cognitive Diagnosis
Authors: Haiping Ma; Siyu Song; Chuan Qin; Xiaoshan Yu; Limiao Zhang; Xingyi Zhang; Hengshu Zhu
Venue: IJCAI 2024 (main)

Abstract: Group-level cognitive diagnosis, pivotal in intelligent education, aims to effectively assess group-level knowledge proficiency by modeling the learning behaviors of individuals within the group. Existing methods typically conceptualize the group as an abstract entity or aggregate the knowledge levels of all members to represent the group’s overall ability. However, these methods neglect the high-order connectivity among groups, students, and exercises within the context of group learning activities, along with the noise present in their interactions, resulting in less robust and suboptimal diagnosis performance. To this end, in this paper, we propose DGCD, an adaptive Denoising graph neural network for realizing effective Group-level Cognitive Diagnosis. Specifically, we first construct a group-student-exercise (GSE) graph to explicitly model higher-order connectivity among groups, students, and exercises, contributing to the acquisition of informative representations. Then, we carefully design an adaptive denoising module, integrated into the graph neural network, to model the reliability distribution of student-exercise edges for mining purer interaction features. In particular, edges of lower reliability are more prone to exclusion, thereby reducing the impact of noisy interactions. Furthermore, recognizing the relational imbalance in the GSE graph, which could potentially introduce bias during message passing, we propose an entropy-weighted balance module to mitigate such bias. Finally, extensive experiments conducted on four real-world educational datasets clearly demonstrate the effectiveness of our proposed DGCD model. The code is available at https://github.com/BIMK/Intelligent-Education/tree/main/DGCD.
SeeDRec: Sememe-based Diffusion for Sequential Recommendation
Authors: Haokai Ma; Ruobing Xie; Lei Meng; Yimeng Yang; Xingwu Sun; Zhanhui Kang
Venue: IJCAI 2024 (main)

Abstract: Inspired by the power of Diffusion Models (DM) verified in various fields, some pioneering works have started to explore DM in recommendation. However, these prevailing endeavors commonly implement diffusion on item indices, leading to the increasing time complexity, the lack of transferability, and the inability to fully harness item semantic information. To tackle these challenges, we propose SeeDRec, a sememe-based diffusion framework for sequential recommendation (SR). Specifically, inspired by the notion of sememe in NLP, SeeDRec first defines a similar concept of recommendation sememe to represent the minimal interest unit and upgrades the specific diffusion objective from the item level to the sememe level. With the Sememe-to-Interest Diffusion Model (S2IDM), SeeDRec can accurately capture the user's diffused interest distribution learned from both local interest evolution and global interest generalization while maintaining low computational costs. Subsequently, an Interest-aware Prompt-enhanced (IPE) strategy is proposed to better guide each user's sequential behavior modeling via the learned user interest distribution. Extensive experiments on nine SR datasets and four cross-domain SR datasets verify its effectiveness and universality. The code is available in https://github.com/hulkima/SeeDRec.
Multi-Modal Sarcasm Detection Based on Dual Generative Processes
Authors: Huiying Ma; Dongxiao He; Xiaobao Wang; Di Jin; Meng Ge; Longbiao Wang
Venue: IJCAI 2024 (main)

Abstract: With the advancement of the internet, sarcastic sentiment expression on social media has grown increasingly diverse. Consequently, multimodal sarcasm detection has emerged as a valuable tool for users to comprehend and interpret sarcastic expressions. Previous research suggests that effectively integrating three modalities (namely image, text, and their inconsistencies) enhances sarcasm detection. However, in some instances, sarcasm detection can be achieved using a single modality, while others necessitate multiple modalities for accurate recognition. This variability suggests that each modality contributes differently to sarcasm detection, and employing a traditional fusion method may introduce bias in the information, unable to explicitly demonstrate the prediction ability of each modality. Therefore, we propose a multimodal sarcasm detection method based on dual generative processes. The dual generative processes map features into the same semantic space to deeply explore emotional inconsistencies between modalities. Concurrently, by incorporating the concept of strong and weak modalities, we explicitly model the modalities' contributions based on prediction performance and autonomously adjust the weight distribution. Experimental results on publicly available multi-modal sarcasm detection datasets validate the superiority of our proposed model.
Graph Collaborative Expert Finding with Contrastive Learning
Authors: Qiyao Peng; Wenjun Wang; Hongtao Liu; Cuiying Huo; Minglai Shao
Venue: IJCAI 2024 (main)

Abstract: In Community Question Answering (CQA) websites, most current expert finding methods often model expert embeddings from textual features and optimize them with expert-question first-order interactions, i.e., this expert has answered this question. In this paper, we try to address the limitation of current models that typically neglect the intrinsic high-order connectivity within expert-question interactions, which is pivotal for collaborative effects. We introduce an innovative and simple approach: by conceptualizing expert-question interactions as a bipartite graph, and then we propose a novel graph-based expert finding method based on contrastive learning to effectively capture both first-order and intricate high-order connectivity, named CGEF. Specifically, we employ a question encoder to model questions from titles and employ the graph attention network to recursively propagate embeddings. Besides, to alleviate the problem of sparse interactions, we devise two auxiliary tasks to enhance expert modeling. First, we generate multiple views of one expert, including: 1) behavior-level augmentation drops interaction edges randomly in the graph; 2) interest-level augmentation randomly replaces question titles with tags in the graph. Then we maximize the agreement between one expert and the corresponding augmented expert on a specific view. In this way, the model can effectively inject collaborative signals into expert modeling. Extensive experiments on six CQA datasets demonstrate significant improvements compared with recent methods.
Seed Selection in the Heterogeneous Moran Process
Authors: Petros Petsinis; Andreas Pavlogiannis; Josef Tkadlec; Panagiotis Karras
Venue: IJCAI 2024 (main)

Abstract: The Moran process is a classic stochastic process that models the rise and takeover of novel traits in network-structured populations. In biological terms, a set of mutants, each with fitness m ∈ (0, ∞) invade a population of residents with fitness 1. Each agent reproduces at a rate proportional to its fitness and each offspring replaces a random network neighbor. The process ends when the mutants either fixate (take over the whole population) or go extinct. The fixation probability measures the success of the invasion. To account for environmental heterogeneity, we study a generalization of the Standard process, called the Heterogeneous Moran process. Here, the fitness of each agent is determined both by its type (resident/mutant) and the node it occupies. We study the natural optimization problem of seed selection: given a budget k, which k agents should initiate the mutant invasion to maximize the fixation probability? We show that the problem is strongly inapproximable: it is NP-hard to distinguish between maximum fixation probability 0 and 1. We then focus on mutant-biased networks, where each node exhibits at least as large mutant fitness as resident fitness. We show that the problem remains NP-hard, but the fixation probability becomes submodular, and thus the optimization problem admits a greedy (1 − 1/e)-approximation. An experimental evaluation of the greedy algorithm along with various heuristics on real-world data sets corroborates our results.
Counterfactual User Sequence Synthesis Augmented with Continuous Time Dynamic Preference Modeling for Sequential POI Recommendation
Authors: Lianyong Qi; Yuwen Liu; Weiming Liu; Shichao Pei; Xiaolong Xu; Xuyun Zhang; Yingjie Wang; Wanchun Dou
Venue: IJCAI 2024 (main)

Abstract: With the proliferation of Location-based Social Networks (LBSNs), user check-in data at Points-of-Interest (POIs) has surged, offering rich insights into user preferences. However, sequential POI recommendation systems always face two pivotal challenges. A challenge lies in the difficulty of modeling time in a discrete space, which fails to accurately capture the dynamic nature of user preferences. Another challenge is the inherent sparsity and noise in continuous POI recommendation, which hinder the recommendation process. To address these challenges, we propose counterfactual user sequence synthesis with continuous time dynamic preference modeling (CussCtpm). CussCtpm innovatively combines Gated Recurrent Unit (GRU) with neural Ordinary Differential Equations (ODEs) to model user preferences in a continuous time framework. CussCtpm captures user preferences at both the POI-level and interest-level, identifying deterministic and non-deterministic preference concepts. Particularly at the interest-level, we employ GRU and neural ODEs to model users' dynamic preferences in continuous space, aiming to capture finer-grained shifts in user preferences over time. Furthermore, CussCtpm utilizes counterfactual data augmentation to generate counterfactual positive and negative user sequences. Our extensive experiments on two widely-used public datasets demonstrate that CussCtpm outperforms several advanced baseline models.
Multi-Relational Graph Attention Network for Social Relationship Inference from Human Mobility Data
Authors: Guangming Qin; Jianpeng Qi; Bin Wang; Guiyuan Jiang; Yanwei Yu; Junyu Dong
Venue: IJCAI 2024 (main)

Abstract: Inferring social relationships from human mobility data holds significant value in real-life spatio-temporal applications, which inspires the development of a series of graph-based methods for inferring social relationships. Despite their effectiveness, we argue that previous methods either rely solely on direct relations between users, neglecting valuable user mobility patterns, or have not fully harnessed the indirect interactions, thereby struggling to capture users' mobility preferences. To address these issues, in this work, we propose the Multi-Relational Graph Attention Network (MRGAN), a novel graph attention network, which is able to explicitly model indirect relations and effectively capture their different impact. Specifically, we first extract a multi-relational graph from heterogeneous mobility graph to explicitly model the direct and indirect relations,and then utilize influence attention and cross-relation attention to further capture the different influence between users, and different importance of relations for each user. Comprehensive experiments on three real-world mobile datasets demonstrate that the proposed model significantly outperforms state-of-the-art models in predicting social relationships between users. The source code of our model is available at https://github.com/qinguangming1999/MRGAN_IJCAI.
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning
Authors: Jihyeon Seong; Sekwang Oh; Jaesik Choi
Venue: IJCAI 2024 (main)

Abstract: Trend filtering simplifies complex time series data by applying smoothness to filter out noise while emphasizing proximity to the original data. However, existing trend filtering methods fail to reflect abrupt changes in the trend due to `approximateness,' resulting in constant smoothness. This approximateness uniformly filters out the tail distribution of time series data, characterized by extreme values, including both abrupt changes and noise. In this paper, we propose Trend Point Detection formulated as a Markov Decision Process (MDP), a novel approach to identifying essential points that should be reflected in the trend, departing from approximations. We term these essential points as Dynamic Trend Points (DTPs) and extract trends by interpolating them. To identify DTPs, we utilize Reinforcement Learning (RL) within a discrete action space and a forecasting sum-of-squares loss function as a reward, referred to as the Dynamic Trend Filtering network (DTF-net). DTF-net integrates flexible noise filtering, preserving critical original subsequences while removing noise as required for other subsequences. We demonstrate that DTF-net excels at capturing abrupt changes compared to other trend filtering algorithms and enhances forecasting performance, as abrupt changes are predicted rather than smoothed out.
Learning Multi-Granularity and Adaptive Representation for Knowledge Graph Reasoning
Authors: Ziyu Shang; Peng Wang; Wenjun Ke; Jiajun Liu; Hailang Huang; Guozheng Li; Chenxiao Wu; Jianghan Liu; Xiye Chen; Yining Li
Venue: IJCAI 2024 (main)

Abstract: Knowledge graph reasoning (KGR) aims to infer new factual triples from existing knowledge graphs (KGs). Recently, a new category of methods, possessing both transductive and inductive reasoning capabilities, has been proposed to tackle this task via learning entity-independent representations from local neighboring structures. However, these methods are plagued by inefficiency issues and they exclusively capture evidence from well-designed local structures, ignoring the correlation between the query and different structures within KGs. In this work, we first propose a novel multi-granularity and adaptive representation framework, MulGA, exploiting the connectivity subgraph to uniformly and hierarchically model query-related triples, relation paths, and subgraphs without explicitly extracting any graph structure, hence mitigating inefficiency issues. Second, we introduce a message-passing mechanism across connectivity subgraphs, facilitating all entities to attain query-related structural representations of diverse granularity levels, i.e., triple and relation paths of different lengths. Third, we design a self-attention-based merging mechanism that allocates weights to different granularities and then consolidates them into subgraph granularity representations for reasoning. The systematic experiments have been conducted on 15 benchmarks and MulGA achieves a significant improvement in MRR by an average of 1.5% on transductive and 2.7% on inductive tasks than existing state-of-the-art methods. Moreover, MulGA boasts faster convergence speed, competitive inference time, and alleviates the over-smoothing prevalent in graph neural networks.
Unsupervised Deep Graph Structure and Embedding Learning
Authors: Xiaobo Shen; Lei Shi; Xiuwen Gong; Shirui Pan
Venue: IJCAI 2024 (main)

Abstract: Graph Neural Network (GNN) is powerful in graph embedding learning, but its performance has been shown to be heavily degraded under adversarial attacks. Deep graph structure learning (GSL) is proposed to defend attack by jointly learning graph structure and graph embedding, typically in node classification task. Label supervision is expensive in real-world applications, and thus unsupervised GSL is more challenging and still remains less studied. To fulfill this gap, this paper proposes a new unsupervised GSL method, i.e., unsupervised property GNN (UPGNN). UPGNN first refines graph structure by exploring properties of low rank, sparsity, feature smoothness. UPGNN employs graph mutual information loss to learn graph embedding by maximizing its correlation with refined graph. The proposed UPGNN learns graph structure and embedding without label supervision, and thus can be applied various downstream tasks. We further propose Accelerated UPGNN (AUPGNN) to reduce computational complexity, providing a efficient alternative to UPGNN. Our extensive experiments on node classification and clustering demonstrate the effectiveness of the proposed method over the state-of-the-arts especially under heavy perturbation.
Efficient Correlated Subgraph Searches for AI-powered Drug Discovery
Authors: Hiroaki Shiokawa; Yuma Naoi; Shohei Matsugu
Venue: IJCAI 2024 (main)

Abstract: Correlated subgraph searches (CSSs) are essential building blocks for AI-powered drug discovery. Given a query molecule modeled as a graph, CSS finds top-k molecules correlated to the query in a database. However, the cost increases exponentially with the molecule size. Herein we present Corgi, a framework to accelerate CSS methods while ensuring top-k search accuracy. Corgi dynamically excludes unnecessary subgraphs to overcome the expensive cost without sacrificing search accuracy. Our experimental analysis confirms that Corgi has a shorter running time and improved accuracy compared to existing state-of-the-art methods, while a case study demonstrates that Corgi is suitable for practical AI-powered drug discovery.
Anomaly Subgraph Detection through High-Order Sampling Contrastive Learning
Authors: Ying Sun; Wenjun Wang; Nannan Wu; Chunlong Bao
Venue: IJCAI 2024 (main)

Abstract: Anomaly subgraph detection is a crucial task in various real-world applications, including identifying high-risk areas, detecting river pollution, and monitoring disease outbreaks. Early traditional graph-based methods can obtain high-precision detection results in scenes with small-scale graphs and obvious anomaly features. Most existing anomaly detection methods based on deep learning primarily concentrate on identifying anomalies at the node level, while neglecting to detect anomaly groups in the internal structure. In this paper, we propose a novel end-to-end Graph Neural Network (GNN) based anomaly subgraph detection approach(ASD-HC) in graph-structured data. 1)We propose a high-order neighborhood sampling strategy to construct our node and k-order neighbor-subgraph instance pairs. 2)Anomaly features of nodes are captured through a self-supervised contrastive learning model. 3) Detecting the maximum connected anomaly subgraph is performed by integrating the Non-parameter Graph Scan statistics and a Random Walk module. We evaluate ASD-HC against five state-of-the-art baselines using five benchmark datasets. ASD-HC outperforms the baselines by over 13.01% in AUC score. Various experiments demonstrate that our approach effectively detects anomaly subgraphs within large-scale graphs.
SemanticMask: A Contrastive View Design for Anomaly Detection in Tabular Data
Authors: Shuting Tao; Tongtian Zhu; Hongwei Wang; Xiangming Meng
Venue: IJCAI 2024 (main)

Abstract: Contrastive learning based on data augmentation techniques has recently achieved substantial advancement in learning a representation well-suited for anomaly detection in image domain. However, due to the lack of spatial structure, designing effective data augmentation methods for tabular data remains challenging. Conventional techniques, such as random mask, disregard the inter-feature correlations and fail to accurately represent the data. To address this issue, we propose a novel augmentation technique called SemanticMask which leverages the semantic information from column names to generate better augmented views. SemanticMask aims to ensure that the shared information between views contains sufficient information for anomaly detection without redundancy. We analyze the relationship between shared information and anomaly detection performance and empirically demonstrate that good views for tabular anomaly detection tasks are feature-dependent. Our experiment results validate the superiority of SemanticMask over the state-of-the-art anomaly detection methods and existing augmentation techniques for tabular data. In further evaluations of the multi-class novelty detection task, SemanticMask also significantly outperforms the baseline.
Instance-Level Metalearning for Outlier Detection
Authors: Long Vu; Peter Kirchner; Charu C. Aggarwal; Horst Samulowitz
Venue: IJCAI 2024 (main)

Abstract: A machine learning task can be viewed as a sequential pipeline of different algorithmic choices, including data preprocessing, model selection, and hyper-parameter tuning. Automated machine learning selects this sequence in an automated manner. While such approaches are natural in supervised settings, they remain challenging for unsupervised tasks such as outlier detection because of the lack of availability of label-centric feedback. In this paper, we present an instance-level metalearning approach for outlier detection. This approach learns how outlier instances are related to normal points in many labeled data sets to create a supervised meta-model. This meta-model is then used on a new (unlabeled) data set to predict outliers. We show the robustness of our approach on several benchmarks from the OpenML repository.