GenAI4ELab/papercli-papers huggingface dataset:
Make Bricks with a Little Straw: Large-Scale Spatio-Temporal Graph Learning with Restricted GPU-Memory Capacity
Authors: Binwu Wang; Pengkun Wang; Zhengyang Zhou; Zhe Zhao; Wei Xu; Yang Wang
Venue: IJCAI 2024 (main)

Abstract: Traffic prediction plays a key role in various smart city applications, which can help traffic managers make traffic plans in advance, assist online ride-hailing companies in deploying vehicles reasonably, and provide early warning of congestion for safety authorities. While increasingly complex models achieve impressive prediction performance, there are concerns about the effectiveness of these models in handling large-scale road networks. Especially for researchers who don't have access to powerful GPU devices, the expensive memory burden limits the usefulness of these models. In this paper, we take the first step of learning on the large-scale spatio-temporal graph and propose a divide-and-conquer training strategy for Large Spatio-Temporal Graph Learning, namely LarSTL. The core idea behind this strategy is to divide the large graph into multiple subgraphs, which are treated as task streams to sequentially train the model to conquer each subgraph one by one. We introduce a novel perspective based on the continuous learning paradigm to achieve this goal. In order to overcome forgetting the knowledge learned from previous subgraphs, an experience-replay strategy consolidates the learned knowledge by replaying nodes sampled from previous subgraphs. Moreover, we configure specific feature adaptors for each subgraph to extract personalized features, and it is also beneficial to consolidate the learned knowledge from the perspective of parameters. We conduct experiments using multiple large-scale traffic network datasets on a V100 GPU with only 16GB memory, and the results demonstrate that our LarSTL can achieve competitive performance and high efficiency.
HeterGCL: Graph Contrastive Learning Framework on Heterophilic Graph
Authors: Chenhao Wang; Yong Liu; Yan Yang; Wei Li
Venue: IJCAI 2024 (main)

Abstract: Graph Contrastive Learning (GCL) has attracted significant research attention due to its self-supervised ability to learn robust node representations. Unfortunately, most methods primarily focus on homophilic graphs, rendering them less effective for heterophilic graphs. In addition, the complexity of node interactions in heterophilic graphs poses considerable challenges to augmentation schemes, coding architectures, and contrastive designs for traditional GCL. In this work, we propose HeterGCL, a novel graph contrastive learning framework with structural and semantic learning to explore the true potential of GCL on heterophilic graphs. Specifically, We abandon the random augmentation scheme that leads to the destruction of the graph structure, instead introduce an adaptive neighbor aggregation strategy (ANA) to extract topology-supervised signals from neighboring nodes at different distances and explore the structural information with an adaptive local-to-global contrastive loss. In the semantic learning module, we jointly consider the original nodes' features and the similarity between nodes in the latent feature space to explore hidden associations between nodes. Experimental results on homophilic and heterophilic graphs demonstrate that HeterGCL outperforms existing self-supervised and semi-supervised baselines across various downstream tasks.
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Authors: Tianfu Wang; Qilin Fan; Chao Wang; Long Yang; Leilei Ding; Nicholas Jing Yuan; Hui Xiong
Venue: IJCAI 2024 (main)

Abstract: Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a flexible and generalizable RL framework for VNE, named FlagVNE. Specifically, we design a bidirectional action-based Markov decision process model that enables the joint selection of virtual and physical nodes, thus improving the exploration flexibility of solution space. To tackle the expansive and dynamic action space, we design a hierarchical decoder to generate adaptive action probability distributions and ensure high training efficiency. Furthermore, to overcome the generalization issue for varying VNR sizes, we propose a meta-RL-based training method with a curriculum scheduling strategy, facilitating specialized policy training for each VNR size. Finally, extensive experimental results show the effectiveness of FlagVNE across multiple key metrics. Our code is available at https://github.com/GeminiLight/flag-vne.
Spatial-Temporal Perceiving: Deciphering User Hierarchical Intent in Session-Based Recommendation
Authors: Xiao Wang; Tingting Dai; Qiao Liu; Shuang Liang
Venue: IJCAI 2024 (main)

Abstract: Session-based recommendation (SBR) aims to predict the next-interacted item based on anonymous users' behavior sequences. The main challenge is how to recognize the user intent with limited interactions to achieve a more accurate inference of user behavior. Existing works usually regard several consecutive items in the current session as intent. However, we argue such intent generation based on temporal transition ignores the fact that each item also has its semantically connected items in the feature space, which can be regarded as spatial intent. The limited consideration of intent fails to capture complex behavioral patterns in real-world scenarios, leading to sub-optimal solutions. To address this issue, we propose the Hierarchical Intent Perceiving Contrastive Learning Framework (HearInt) for SBR, which proposes a hierarchical consideration of intents from both temporal and spatial perspective. Specifically, we first propose that the user's temporal intents are mutually exclusive while the spatial intents are mutually compatible. Following these analyses, we design a Temporal Intent Decoupling module to mitigate the mutual influence of long-term and short-term intents, and a Cross-scale Contrastive Learning task to enhance the consistency of intents across different spatial scales. Experimental results on three real-world datasets exhibit that HearInt achieves state-of-the-art performance.
Joint Source Localization in Different Platforms via Implicit Propagation Characteristics of Similar Topics
Authors: Zhen Wang; Dongpeng Hou; Shu Yin; Chao Gao; Xianghua Li
Venue: IJCAI 2024 (main)

Abstract: Different social media are widely used in our daily lives. Inspired by the fact that similar topics have similar propagation characteristics, we mine the implicit knowledge of cascades with similar topics from different platforms to enhance the localization performance for scenarios where limited propagation data leads to the weak learning ability of existing localization models. In this work, we first construct a multiple platform propagation cascade dataset, aligning similar topics from both Twitter and Weibo, and enriching it with user profiles. Leveraging this dataset, we propose a Dual-channel Source Localization Framework (DSLF) for the joint cascades with similar topics. Specifically, a self-loop attention based graph convolutional network is designed to adaptively adjust the neighborhood aggregation scheme of different users with heterogeneous features in the message-passing process. Additionally, a dual-structure based Kullback-Leibler (KL) regularization module is proposed to constrain the latent distribution space of the source probabilities of similar characteristic-level users for a similar topic, enhancing the robustness of the model. Extensive experiments across Twitter and Weibo platforms demonstrate the superiority of the proposed DSLF over the SOTA methods. The code is available at https://github.com/cgao-comp/DSLF.
WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction
Authors: Binqing Wu; Weiqi Chen; Wengwei Wang; Bingqing Peng; Liang Sun; Ling Chen
Venue: IJCAI 2024 (main)

Abstract: Due to insufficient local area information, numerical weather prediction (NWP) may yield biases for specific areas. Previous studies correct biases mainly by employing handcrafted features or applying data-driven methods intuitively, overlooking the complicated dependencies between weather factors and between areas. To address this issue, we propose WeatherGNN, a local NWP bias-correction method that utilizes Graph Neural Networks (GNNs) to exploit meteorological dependencies and spatial dependencies under the guidance of domain knowledge. Specifically, we introduce a factor GNN to capture area-specific meteorological dependencies adaptively based on spatial heterogeneity and a fast hierarchical GNN to capture dynamic spatial dependencies efficiently guided by Tobler's first and second laws of geography. Our experimental results on two real-world datasets demonstrate that WeatherGNN achieves the state-of-the-art performance, outperforming the best baseline with an average of 4.75 % on RMSE.
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling
Authors: Di Wu; Shicai Fan; Xue Zhou; Li Yu; Yuzhong Deng; Jianxiao Zou; Baihong Lin
Venue: IJCAI 2024 (main)

Abstract: Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect and locate anomalies. Recently, diffusion models have shown promising applications for anomaly detection due to their powerful generative ability. However, these models lack strict mathematical support for normal image reconstruction and unexpectedly suffer from low reconstruction quality. To address these issues, this paper proposes a novel and highly-interpretable method named Masked Diffusion Posterior Sampling (MDPS). In MDPS, the problem of normal image reconstruction is mathematically modeled as multiple diffusion posterior sampling for normal images based on the devised masked noisy observation model and the diffusion-based normal image prior under Bayesian framework. Using a metric designed from pixel-level and perceptual-level perspectives, MDPS can effectively compute the difference map between each normal posterior sample and the given test image. Anomaly scores are obtained by averaging all difference maps for multiple posterior samples. Exhaustive experiments on MVTec and BTAD datasets demonstrate that MDPS can achieve state-of-the-art performance in normal image reconstruction quality as well as anomaly detection and localization.
Robust Heterophilic Graph Learning against Label Noise for Anomaly Detection
Authors: Junhang Wu; Ruimin Hu; Dengshi Li; Zijun Huang; Lingfei Ren; Yilong Zang
Venue: IJCAI 2024 (main)

Abstract: Given clean labels, Graph Neural Networks (GNNs) have shown promising abilities for graph anomaly detection. However, real-world graphs are inevitably noisy labeled, which drastically degrades the performance of GNNs. To alleviate it, some studies follow the local consistency (a.k.a homophily) assumption to conduct neighborhood-based label noise correction, and to dense raw graphs using raw features or representations learned by poisoned labels. But for the anomaly detection task, the graph is not always homophilic but more likely to be heterophilic, which would corrupt the above assumption due to complicating connection patterns and impairing the effects of message passing. To this end, we propose a novel label noise-resistant graph learning (NRGL) framework, which facilitates robust graph learning from the perspectives of structure augmentation and fine-grained label governance. Specifically, we first present an investigation to verify that increasing graph homophily could help resist label noise. Based on the observation, an unsupervised contrastive learning paradigm is then introduced so well that it cannot only adaptively extract the dual views from the raw graph as structure augmentation, but also enhance the robustness of node representations. Next, given robust node representations, the noisy labels are divided into three candidate sets based on the small-loss criterion for fine-grained noise governance. Furthermore, a node sampler is designed to take structure importance, class frequency, and confidence score into consideration, which helps select reliable and important nodes for training. Extensive experiments on real-world datasets demonstrate the effectiveness of our method.
Hierarchical Reinforcement Learning for Point of Interest Recommendation
Authors: Yanan Xiao; Lu Jiang; Kunpeng Liu; Yuanbo Xu; Pengyang Wang; Minghao Yin
Venue: IJCAI 2024 (main)

Abstract: With the increasing popularity of location-based services, accurately recommending points of interest (POIs) has become a critical task. Although existing technologies are proficient in processing time-series data, they fall short when it comes to accommodating the diversity and dynamism in users' POI selections, particularly in extracting key signals from complex historical behaviors. To address this challenge, we introduced the Hierarchical Reinforcement Learning Preprocessing Framework (HRL-PRP), a framework that can be integrated into existing recommendation models to effectively optimize user profiles. The HRL-PRP framework employs a two-tiered decision-making process, where the high-level process determines the necessity of modifying profiles, and the low-level process focuses on selecting POIs within the profiles. Through evaluations on multiple real-world datasets, we have demonstrated that HRL-PRP surpasses existing state-of-the-art methods in various recommendation performance metrics.
Learning Fair Representations for Recommendation via Information Bottleneck Principle
Authors: Junsong Xie; Yonghui Yang; Zihan Wang; Le Wu
Venue: IJCAI 2024 (main)

Abstract: User-oriented recommender systems (RS) characterize users' preferences based on observed behaviors and are widely deployed in personalized services. However, RS may unintentionally capture biases related to sensitive attributes (e.g., gender) from behavioral data, leading to unfair issues and discrimination against particular groups (e.g., females). Adversarial training is a popular technique for fairness-aware RS, when filtering sensitive information in user modeling. Despite advancements in fairness, achieving a good accuracy-fairness trade-off remains a challenge in adversarial training. In this paper, we investigate fair representation learning from a novel information theory perspective. Specifically, we propose a model-agnostic Fair recommendation method via the Information Bottleneck principle FairIB. The learning objective of FairIB is to maximize the mutual information between user representations and observed interactions, while simultaneously minimizing it between user representations and sensitive attributes. This approach facilitates the capturing of essential collaborative signals in user representations while mitigating the inclusion of unnecessary sensitive information. Empirical studies on two real-world datasets demonstrate the effectiveness of the proposed FairIB, which significantly improves fairness while maintaining competitive recommendation accuracy, either in single or multiple sensitive scenarios. The code is available at https://github.com/jsxie9/IJCAI_FairIB.
Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation
Authors: Fei Xiong; Haoran Sun; Guixun Luo; Shirui Pan; Meikang Qiu; Liang Wang
Venue: IJCAI 2024 (main)

Abstract: In recommender systems, graph neural networks (GNN) can integrate interactions between users and items with their attributes, which makes GNN-based methods more powerful. However, directly stacking multiple layers in a graph neural network can easily lead to over-smoothing, hence recommendation systems based on graph neural networks typically underutilize higher-order neighborhoods in their learning. Although some heterogeneous graph random walk methods based on meta-paths can achieve higher-order aggregation, the focus is predominantly on the nodes at the ends of the paths. Moreover, these methods require manually defined meta-paths, which limits the model’s expressiveness and flexibility. Furthermore, path encoding in graph neural networks usually focuses only on the sequence leading to the target node. However, real-world interactions often do not follow this strict sequence, limiting the predictive performance of sequence-based network models. These problems prevent GNN-based methods from being fully effective. We propose a Graph Attention network with Information Propagation path aggregation for Social Recommendation (GAIPSRec). Firstly, we propose a universal heterogeneous graph sampling framework that does not require manually defining meta-paths for path sampling, thereby offering greater flexibility. Moreover, our method takes into account all nodes on the aggregation path and is capable of learning information from higher-order neighbors without succumbing to over-smoothing. Finally, our method utilizes a gate mechanism to fuse sequential and non-sequential dependence in encoding path instances, allowing a more holistic view of the data. Extensive experiments on real-world datasets show that our proposed GAIPSRec improves the performance significantly and outperforms state-of-the-art methods.
Decoupled Invariant Attention Network for Multivariate Time-series Forecasting
Authors: Haihua Xu; Wei Fan; Kun Yi; Pengyang Wang
Venue: IJCAI 2024 (main)

Abstract: To achieve more accurate prediction results in Time Series Forecasting (TSF), it is essential to distinguish between the valuable patterns (invariant patterns) of the spatial-temporal relationship and the patterns that are prone to generate distribution shift (variant patterns), then combine them for forecasting.The existing works, such as transformer-based models and GNN-based models, focus on capturing main forecasting dependencies whether it is stable or not, and they tend to overlook patterns that carry both useful information and distribution shift. In this paper, we propose a model for better forecasting time series: Decoupled Invariant Attention Network (DIAN), which contains two modules to learn spatial and temporal relationships respectively: 1) Spatial Decoupled Invariant-Variant Learning (SDIVL) to decouple the spatial invariant and variant attention scores, and then leverage convolutional networks to effectively integrate them for subsequent layers; 2) Temporal Augmented Invariant-Variant Learning (TAIVL) to decouple temporal invariant and variant patterns and combine them for further forecasting.In this module, we also design Temporal Intervention Mechanism to create multiple intervened samples by reassembling variant patterns across time stamps to eliminate the spurious impacts of variant patterns.In addition, we propose Joint Optimization to minimize the loss function considering all invariant patterns, variant patterns and intervened patterns so that our model can gain a more stable predictive ability.Extensive experiments on five datasets have demonstrated our superior performance with higher efficiency compared with state-of-the-art methods.
Joint Domain Adaptive Graph Convolutional Network
Authors: Niya Yang; Ye Wang; Zhizhi Yu; Dongxiao He; Xin Huang; Di Jin
Venue: IJCAI 2024 (main)

Abstract: In the realm of cross-network tasks, graph domain adaptation is an effective tool due to its ability to transfer abundant labels from nodes in the source domain to those in the target domain. Existing adversarial domain adaptation methods mainly focus on domain-wise alignment. These approaches, while effective in mitigating the marginal distribution shift between the two domains, often ignore the integral aspect of structural alignment, potentially leading to negative transfer. To address this issue, we propose a joint adversarial domain adaptive graph convolutional network (JDA-GCN) that is uniquely augmented with structural graph alignment, so as to enhance the efficacy of knowledge transfer. Specifically, we construct a structural graph to delineate the interconnections among nodes within identical categories across the source and target domains. To further refine node representation, we integrate the local consistency matrix with the global consistency matrix, thereby leveraging the learning of the sub-structure similarity of nodes to enable more robust and effective representation of nodes. Empirical evaluation on diverse real-world datasets substantiates the superiority of our proposed method, marking a significant advancement over existing state-of-the-art graph domain adaptation algorithms.
Kernel Readout for Graph Neural Networks
Authors: Jiajun Yu; Zhihao Wu; Jinyu Cai; Adele Lu Jia; Jicong Fan
Venue: IJCAI 2024 (main)

Abstract: Graph neural networks (GNNs) for graph classification or representation learning require a pooling operation to convert the nodes' embeddings of each graph to a vector as the graph-level representation and the operation has a significant impact on model accuracy. The paper presents a novel graph pooling method called Kernel Readout (KerRead). KerRead maps the node embeddings from the sample space with limited nodes to an augmented sample space with infinite nodes, and then calculates the inner product between some learnable adaptive centers and the augmented node embeddings, which forms a final graph-level feature vector. We apply the proposed strategy to six supervised and two unsupervised graph neural networks such as GCN, GIN, GUNet, InfoGraph, and GraphCL, and the experiments on eight benchmark datasets show that the proposed readout outperforms classical pooling methods such as Sum and seven state-of-the-art pooling methods such as SRead and Janossy GRU. Code and Appendix are both available at https://github.com/jiajunCAU/KerRead.
LG-GNN: Local-Global Adaptive Graph Neural Network for Modeling Both Homophily and Heterophily
Authors: Zhizhi Yu; Bin Feng; Dongxiao He; Zizhen Wang; Yuxiao Huang; Zhiyong Feng
Venue: IJCAI 2024 (main)

Abstract: Most Graph Neural Networks (GNNs) are based on the homophily assumption, where nodes with the same labels or similar features tend to be connected to each other. However, real-world graphs often do not adhere to this homophily assumption. Currently, most researches aggregate multi-hop neighbor information to discover more potentially relevant nodes. However, in the aggregation process of GNNs, the difference in modeling global and local information is not considered, inevitably leading to information loss. Motivated by this limitation, we propose LG-GNN, a local-global adaptive graph neural network for modeling both homophily and heterophily. Specifically, we model the long-range structural similarity and local feature similarity between nodes from global and local perspectives, in order to capture distant dependencies in highly heterophilic networks while reducing the mixing of locally dissimilar feature nodes, thereby increasing the effectiveness of information aggregation in highly heterophilic graphs. Extensive experiments on a wide range of real-world datasets demonstrate that our proposed approach performs well in both heterophilic and homophilic graphs.