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Li Guo

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25 papers

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YNIMG Journal 2025 Journal Article

Accelerating multi-directional diffusion MRI through patch-based joint reconstruction

Zhongbiao Xu
Rongli Zhang
Wei Huang
Guanhua Deng
Xiaoyun Liang
Li Guo
Junying Cheng
Yaohui Wang

Diffusion magnetic resonance imaging (dMRI) is a valuable technique for studying tissue microstructure and connectivity in the brain. However, acquiring high-resolution dMRI data is time-consuming, limiting its clinical applicability. Traditional parallel imaging techniques can accelerate the acquisition of dMRI, but they are constrained by the geometry factor. In this study, we propose a novel patch-based multiple diffusion directions joint reconstruction method that simultaneously capitalizes on the intra- and inter-image correlation across multiple diffusion directions by grouping similar 3D image patches and then enforces the sparsity of these groups in sensitivity encoding (SENSE) reconstruction, termed PB-SENSE. The simulation and in vivo experiments demonstrated that the proposed method can achieve high-quality images comparable to those obtained from fully sampled data, even with an acceleration of 5. This suggests that the proposed method has the potential to enhance the practical application of high-resolution diffusion imaging.

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TMLR Journal 2025 Journal Article

Cross Entropy versus Label Smoothing: A Neural Collapse Perspective

Li Guo
George Andriopoulos
Zifan Zhao
Zixuan Dong
Shuyang Ling
Keith W. Ross

Label smoothing loss is a widely adopted technique to mitigate overfitting in deep neural networks. This paper studies label smoothing from the perspective of Neural Collapse (NC), a powerful empirical and theoretical framework which characterizes model behavior during the terminal phase of training. We first show empirically that models trained with label smoothing converge faster to neural collapse solutions and attain a stronger level of neural collapse compared to those trained with cross-entropy loss. Furthermore, we show that at the same level of NC1, models under label smoothing loss exhibit intensified NC2. These findings provide valuable insights into the impact of label smoothing on model performance and calibration. Then, leveraging the unconstrained feature model, we derive closed-form solutions for the global minimizers under both label smoothing and cross-entropy losses. We show that models trained with label smoothing have a lower conditioning number and, therefore, theoretically converge faster. Our study, combining empirical evidence and theoretical results, not only provides nuanced insights into the differences between label smoothing and cross-entropy losses, but also serves as an example of how the powerful neural collapse framework can be used to improve our understanding of DNNs.

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AAAI Conference 2025 Conference Paper

Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition

Changwei Wang
Shunpeng Chen
Yukun Song
Rongtao Xu
Zherui Zhang
Jiguang Zhang
Haoran Yang
Yu Zhang

Visual Place Recognition (VPR) is aimed at predicting the location of a query image by referencing a database of geotagged images. For VPR task, often fewer discriminative local regions in an image produce important effects while mundane background regions do not contribute or even cause perceptual aliasing because of easy overlap. However, existing methods lack precisely modeling and full exploitation of these discriminative regions. In addition, the lack of pixel-level correspondence supervision in the VPR dataset hinders further improvement of the local feature matching capability in the re-ranking stage. In this paper, we propose the Focus on Local (FoL) approach to stimulate the performance of image retrieval and re-ranking in VPR simultaneously by mining and exploiting reliable discriminative local regions in images and introducing pseudo-correlation supervision. First, we design two losses, Extraction-Aggregation Spatial Alignment Loss (SAL) and Foreground-Background Contrast Enhancement Loss (CEL), to explicitly model reliable discriminative local regions and use them to guide the generation of global representations and efficient re-ranking. Second, we introduce a weakly-supervised local feature training strategy based on pseudo-correspondences obtained from aggregating global features to alleviate the lack of local correspondences ground truth for the VPR task. Third, we suggest an efficient re-ranking pipeline that is efficiently and precisely based on discriminative region guidance. Finally, experimental results show that our FoL achieves the state-of-the-art on multiple VPR benchmarks in both image retrieval and re-ranking stages and also significantly outperforms existing two-stage VPR methods in terms of computational efficiency.

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EAAI Journal 2025 Journal Article

Peridynamic-driven feature-enhanced Vision Transformer for predicting defects and heterogeneous materials locations: Applications of deep learning in inverse problems

Yalin Li
Jinwei Guan
Li Guo

To achieve efficient and real-time evaluation of internal compositions in heterogeneous structures, this study proposes a peridynamic-driven feature-enhanced deep learning (DL) framework by integrating the Context Anchored Attention (CAA) mechanism and Kolmogorov-Arnold Networks (KANs) into a Vision Transformer (CK-ViT) architecture. This framework effectively captures deep structural features while leveraging a self-attention DropKey regularization technique to improve generalization. Three types of heterogeneous structure datasets were constructed based on peridynamic theory, enabling transfer learning with the CK-ViT model. Comprehensive evaluations, including epoch-wise and multi-metric assessments, demonstrate that CK-ViT achieves superior training efficiency and inference accuracy compared to conventional models. Specifically, the coefficient of determination (R 2) reaches 88. 23 %, 93. 19 %, and 90. 69 %, while the accuracy achieves 97. 99 %, 98. 72 %, and 98. 22 %, respectively, for the three problem types. These results indicate that CK-ViT can accurately predict the locations of defects and heterogeneous materials by leveraging the global strain distribution of structures. Ablation studies further confirm that the introduction of CAA and KANs enhances model performance by 7. 8 % and decreases the parameter storage overhead by 51. 0 %, underscoring their effectiveness. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was employed to conduct an in-depth analysis of the inference process and decision-making mechanisms, revealing the underlying principles that drive model predictions. Additionally, an error analysis was conducted to identify key sources of prediction uncertainty and potential optimization directions. This study establishes a robust and interpretable framework for real-time defect location prediction and material distribution estimation. It provides valuable references for structural health monitoring and material engineering.

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EAAI Journal 2024 Journal Article

MSE-Net: A novel master–slave encoding network for remote sensing scene classification

Hongguang Yue
Linbo Qing
Zhixuan Zhang
Zhengyong Wang
Li Guo
Yonghong Peng

Remote sensing scene (RSS) image classification plays a vital role in various fields such as urban planning and environmental protection. However, due to higher inter-class similarity and intra-class variability, achieving accurate classification for RSS images poses a considerable challenge for current convolutional neural networks (CNNs)-based and visual transformer (ViT)-based methods. To address these issues, this paper proposes a novel dual-encoding method named master–slave encoding network (MSE-Net) from two perspectives of feature extraction and fusion. The master encoder, based on ViT, extracts higher-level semantic features, while the slave encoder, based on CNN, captures relative lower-level spatial structure information. Secondly, to integrate feature information from the two encoders effectively, this paper further develop two fusion strategies. The first strategy involves the auxiliary enhancement units (AEUs), which eliminates semantic divergence between the two encoders, enhances spatial context awareness of the slave encoder and promotes effective feature learning. The interactive perception unit (IPU), as the second strategy, facilitates interaction and integration of the two encoders’ representations to extract more discriminative feature information. In addition, we conducted comparative experiments on four widely-used RSS datasets, including RSSCN7, SIRI-WHU, the aerial image dataset (AID) and NWPU-RESISC45 (NWPU45), to verify the effectiveness of MSE-Net. The experimental results demonstrate that MSE-Net achieved state-of-the-art (SOTA) performance across all the datasets.

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EAAI Journal 2024 Journal Article

Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network

Li Guo
Hongwei He
Yiran Ren
Runze Li
Bin Jiang
Jianye Gong

In the field of battery health state prognostics, the inaccurate lithium-ion battery's health status prediction is usually caused by the capacity regeneration (CR) phenomenon triggered by relaxation effects during the degradation process. To address this issue, we pay more attention to the main rapid degradation trend of battery capacity instead of many researches focusing on only CR phenomenon. This paper presents a prognostic framework called CEEMDAN-LSTM to decouple the normal capacity degradation process while eliminating local regenerative capacity, capturing degradation characteristics for battery state-of-health (SOH) prognostics. It introduces the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original battery capacity degradation curve into principal trend sequence and other high-frequency subsequences, and Pearson correlation coefficient (PCC) is calculated to remove irrelevant high-frequency subsequences. Predictions task is completed by bidirectional long short-term memory network (Bi-LSTM) and long short-term memory network (LSTM) groups. Experimental validations are conducted on two lithium-ion battery datasets from NASA Ames Research Center and the Advanced Life-Cycle Engineering Center of the University of Maryland. The results demonstrate that the proposed framework achieves more accurate SOH prediction than many previous mainstream methods.

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JBHI Journal 2024 Journal Article

PSTNet: Enhanced Polyp Segmentation With Multi-Scale Alignment and Frequency Domain Integration

Wenhao Xu
Rongtao Xu
Changwei Wang
Xiuli Li
Shibiao Xu
Li Guo

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scales, leading to limitations in accurately identifying polyps due to restricted RGB domain information and challenges in feature misalignment during multi-scale aggregation. To address these limitations, we propose the Polyp Segmentation Network with Shunted Transformer (PSTNet), a novel approach that integrates both RGB and frequency domain cues present in the images. PSTNet comprises three key modules: the Frequency Characterization Attention Module (FCAM) for extracting frequency cues and capturing polyp characteristics, the Feature Supplementary Alignment Module (FSAM) for aligning semantic information and reducing misalignment noise, and the Cross Perception localization Module (CPM) for synergizing frequency cues with high-level semantics to achieve efficient polyp segmentation. Extensive experiments on challenging datasets demonstrate PSTNet's significant improvement in polyp segmentation accuracy across various metrics, consistently outperforming state-of-the-art methods. The integration of frequency domain cues and the novel architectural design of PSTNet contribute to advancing computer-assisted polyp segmentation, facilitating more accurate diagnosis and management of CRC.

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AAAI Conference 2024 Conference Paper

Spectral Prompt Tuning: Unveiling Unseen Classes for Zero-Shot Semantic Segmentation

Wenhao Xu
Rongtao Xu
Changwei Wang
Shibiao Xu
Li Guo
Man Zhang
Xiaopeng Zhang

Recently, CLIP has found practical utility in the domain of pixel-level zero-shot segmentation tasks. The present landscape features two-stage methodologies beset by issues such as intricate pipelines and elevated computational costs. While current one-stage approaches alleviate these concerns and incorporate Visual Prompt Training (VPT) to uphold CLIP's generalization capacity, they still fall short in fully harnessing CLIP's potential for pixel-level unseen class demarcation and precise pixel predictions. To further stimulate CLIP's zero-shot dense prediction capability, we propose SPT-SEG, a one-stage approach that improves CLIP's adaptability from image to pixel. Specifically, we initially introduce Spectral Prompt Tuning (SPT), incorporating spectral prompts into the CLIP visual encoder's shallow layers to capture structural intricacies of images, thereby enhancing comprehension of unseen classes. Subsequently, we introduce the Spectral Guided Decoder (SGD), utilizing both high and low-frequency information to steer the network's spatial focus towards more prominent classification features, enabling precise pixel-level prediction outcomes. Through extensive experiments on two public datasets, we demonstrate the superiority of our method over state-of-the-art approaches, performing well across all classes and particularly excelling in handling unseen classes.

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NeurIPS Conference 2024 Conference Paper

The Prevalence of Neural Collapse in Neural Multivariate Regression

George Andriopoulos
Zixuan Dong
Li Guo
Zifan Zhao
Keith Ross

Recently it has been observed that neural networks exhibit Neural Collapse (NC) during the final stage of training for the classification problem. We empirically show that multivariate regression, as employed in imitation learning and other applications, exhibits Neural Regression Collapse (NRC), a new form of neural collapse: (NRC1) The last-layer feature vectors collapse to the subspace spanned by the $n$ principal components of the feature vectors, where $n$ is the dimension of the targets (for univariate regression, $n=1$); (NRC2) The last-layer feature vectors also collapse to the subspace spanned by the last-layer weight vectors; (NRC3) The Gram matrix for the weight vectors converges to a specific functional form that depends on the covariance matrix of the targets. After empirically establishing the prevalence of (NRC1)-(NRC3) for a variety of datasets and network architectures, we provide an explanation of these phenomena by modeling the regression task in the context of the Unconstrained Feature Model (UFM), in which the last layer feature vectors are treated as free variables when minimizing the loss function. We show that when the regularization parameters in the UFM model are strictly positive, then (NRC1)-(NRC3) also emerge as solutions in the UFM optimization problem. We also show that if the regularization parameters are equal to zero, then there is no collapse. To our knowledge, this is the first empirical and theoretical study of neural collapse in the context of regression. This extension is significant not only because it broadens the applicability of neural collapse to a new category of problems but also because it suggests that the phenomena of neural collapse could be a universal behavior in deep learning.

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EAAI Journal 2024 Journal Article

Unveiling group activity recognition: Leveraging Local–Global Context-Aware Graph Reasoning for enhanced actor–scene interactions

Xue Jiang
Linbo Qing
Jianglan Huang
Li Guo
Yonghong Peng

Group activity recognition aims to recognize holistic activity in multi-person scene, necessitating consideration of the interactions between actors and their surroundings. It has various applications, such as public surveillance, video analysis. Nonetheless, existing works merely extract scene features as a supplementary component of activity features, failing to adequately explore the interplay between the scene and actors. To address this limitation, this paper proposes a Local–Global Context-Aware Graph Reasoning Model (LG-CAGR), which leverages and reasons through local and global context features to gain deeper insights into group activity within the scene. In particular, we present an innovative feature extraction strategy to harness local location features and global scene attributes, effectively complementing actor features by capturing spatial group topology and determining relative positions between actors. Subsequently, we delve into these features by devising a local–global group reasoning module that deduces pair-wise interactions between actors and scenes within Graph Convolutional Network, comprehensively elucidating correlations between overall scene and local individuals to construct group-level features. Multi-graphs are constructed considering actor’s features, scene features as nodes, and interactions as edges. A self-attention graph pooling network is introduced to automatically integrate key actor features and form rich group-level features to recognize group activity. The results on Collective Activity Dataset, Collective Activity Extended Dataset, Volleyball Dataset and Public Life in Public Space dataset have reached 94. 0%, 97. 7%, 92. 7% and 56. 1%. Compared with existing methods using the same backbone, we exceeded 1%, 2. 1%, 0. 3%, and 14. 9% respectively, affirming the superiority of the proposed method compared with state-of-the-art methods.

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EAAI Journal 2023 Journal Article

A collaborative perception method of human-urban environment based on machine learning and its application to the case area

Jianlin Huang
Linbo Qing
Longmei Han
Jiajia Liao
Li Guo
Yonghong Peng

Human perception refers to people’s psychological feelings about a place. Understanding residents’ perception and their activities is important for promoting people-oriented urban construction. Recently, with the development of machine learning, researchers used this technology to study human perception from the open-source street view images. However, the perception measurement is limited, caused by the inadequate feature extraction. Besides, human perceptions and their activities are separately studied, which hinders the process of revealing the relationship between human and environment. Hence, a human perception model was firstly proposed, where a Transformer network was introduced to extract more discriminative semantic features and visual elements were integrated to enhance the feature representations. Experiments showed that the average deviation of perceptual scores was controlled within 1. 6 points, and its performance was improved by around 1%–18% in mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) compared with the existing best results. Secondly, the collaborative study of environmental perception and residents’ activities was carried out in the case area. Specifically, the perceptual measures of environment were implemented based on the street view video data. Meanwhile, the activities of residents were recognized by SlowFast network and quantified by a new informatics-based diversity indicator (Active Index). This study finally obtained their spatial distribution map, and showed that the perceptual dimensions lively, boring, safe, and depressing are correlated with information quantity of activities. The paper provides a novel method to understand better the urban environment and the distribution of residents’ activities to facilitate urban planning.

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IJCAI Conference 2023 Conference Paper

Accurate MRI Reconstruction via Multi-Domain Recurrent Networks

Jinbao Wei
Zhijie Wang
Kongqiao Wang
Li Guo
Xueyang Fu
Ji Liu
Xun Chen

In recent years, deep convolutional neural networks (CNNs) have become dominant in MRI reconstruction from undersampled k-space. However, most existing CNNs methods reconstruct the undersampled images either in the spatial domain or in the frequency domain, and neglecting the correlation between these two domains. This hinders the further reconstruction performance improvement. To tackle this issue, in this work, we propose a new multi-domain recurrent network (MDR-Net) with multi-domain learning (MDL) blocks as its basic units to reconstruct the undersampled MR image progressively. Specifically, the MDL block interactively processes the local spatial features and the global frequency information to facilitate complementary learning, leading to fine-grained features generation. Furthermore, we introduce an effective frequency-based loss to narrow the frequency spectrum gap, compensating for over-smoothness caused by the widely used spatial reconstruction loss. Extensive experiments on public fastMRI datasets demonstrate that our MDR-Net consistently outperforms other competitive methods and is able to provide more details.

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EAAI Journal 2023 Journal Article

Safe semi-supervised clustering based on Dempster–Shafer evidence theory

Haitao Gan
Zhi Yang
Ran Zhou
Li Guo
Zhiwei Ye
Rui Huang

In this paper, we propose a safe semi-supervised clustering algorithm based on Dempster–Shafer (D–S) evidence theory. The motivation is that D–S evidence theory can be used to fuse multiple base clustering results and obtain robust confidence estimations of mislabeled samples. Firstly, the proposed algorithm constructs multiple base clusters using fuzzy c -means and kernel fuzzy c -means. Base clusters with good performance are selected according to a clustering validity function. Then, D–S evidence theory is used to fuse the results of the selected base clusters, and the confidence of labeled samples is calculated based on the fused results. Finally, we construct a p -nearest neighbor graph to limit the outputs of labeled samples with low confidence to be those of the p nearest unlabeled samples. It is desired to reduce the negative influence of labeled samples with low confidence and achieve safe exploitation. To verify the effectiveness of the proposed algorithm, we compare it to several unsupervised and semi-supervised clustering algorithms. The results demonstrate that our algorithm yields higher accuracy and is more stable.

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AAAI Conference 2020 Conference Paper

A Knowledge-Aware Attentional Reasoning Network for Recommendation

Qiannan Zhu
Xiaofei Zhou
Jia Wu
Jianlong Tan
Li Guo

Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently. Many existing knowledge-aware recommendation methods have achieved better performance, which usually perform recommendation by reasoning on the paths between users and items in knowledge graphs. However, they ignore the users’ personal clicked history sequences that can better reﬂect users’ preferences within a period of time for recommendation. In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users’ clicked history sequences and path connectivity between users and items for recommendation. The proposed KARN not only develops an attention-based RNN to capture the user’s history interests from the user’s clicked history sequences, but also a hierarchical attentional neural network to reason on paths between users and items for inferring the potential user intents on items. Based on both user’s history interest and potential intent, KARN can predict the clicking probability of the user with respective to a candidate item. We conduct experiment on Amazon review dataset, and the experimental results demonstrate the superiority and effectiveness of our proposed KARN model.

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IJCAI Conference 2020 Conference Paper

A Relation-Specific Attention Network for Joint Entity and Relation Extraction

Yue Yuan
Xiaofei Zhou
Shirui Pan
Qiannan Zhu
Zeliang Song
Li Guo

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recognition followed by relation detection between every possible entity pairs, which usually suffers from numerous redundant operations. In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. Our RSAN utilizes relation-aware attention mechanism to construct specific sentence representations for each relation, and then performs sequence labeling to extract its corresponding head and tail entities. Experiments on two public datasets show that our model can effectively extract overlapping triplets and achieve state-of-the-art performance.

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AAAI Conference 2020 Conference Paper

Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction

Zhenyu Zhang
Xiaobo Shu
Bowen Yu
Tingwen Liu
Jiapeng Zhao
Quangang Li
Li Guo

Extracting relations from plain text is an important task with wide application. Most existing methods formulate it as a supervised problem and utilize one-hot hard labels as the sole target in training, neglecting the rich semantic information among relations. In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. Speciﬁcally, a bipartite graph is ﬁrst devised to discover type constraints between entities and relations based on the entire corpus. Then, we combine such type constraints with neural networks to achieve a knowledgeable model. Furthermore, this model is regarded as teacher to generate well-informed soft labels and guide the optimization of a student network via knowledge distillation. Besides, a multi-aspect attention mechanism is introduced to help student mine latent information from text. In this way, the enhanced student inherits the dark knowledge (e. g. , type constraints and relevance among relations) from teacher, and directly serves the testing scenarios without any extra constraints. We conduct extensive experiments on the TACRED and SemEval datasets, the experimental results justify the effectiveness of our approach.

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AAAI Conference 2019 Conference Paper

DAN: Deep Attention Neural Network for News Recommendation

Qiannan Zhu
Xiaofei Zhou
Zeliang Song
Jianlong Tan
Li Guo

With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing richer hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.

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IJCAI Conference 2019 Conference Paper

Deep Active Learning for Anchor User Prediction

Anfeng Cheng
Chuan Zhou
Hong Yang
Jia Wu
Lei Li
Jianlong Tan
Li Guo

Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction. To this end, we present a deep active learning model for anchor user prediction (DALAUP for short). However, active learning for anchor user sampling meets the challenges of non-i. i. d. user pair data caused by network structures and the correlation among anchor or non-anchor user pairs. To solve the challenges, DALAUP uses a couple of neural networks with shared-parameter to obtain the vector representations of user pairs, and ensembles three query strategies to select the most informative user pairs for labeling and model training. Experiments on real-world social network data demonstrate that DALAUP outperforms the state-of-the-art approaches.

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IJCAI Conference 2019 Conference Paper

Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs

Qiannan Zhu
Xiaofei Zhou
Jia Wu
Jianlong Tan
Li Guo

Multilingual knowledge graphs constructed by entity alignment are the indispensable resources for numerous AI-related applications. Most existing entity alignment methods only use the triplet-based knowledge to find the aligned entities across multilingual knowledge graphs, they usually ignore the neighborhood subgraph knowledge of entities that implies more richer alignment information for aligning entities. In this paper, we incorporate neighborhood subgraph-level information of entities, and propose a neighborhood-aware attentional representation method NAEA for multilingual knowledge graphs. NAEA devises an attention mechanism to learn neighbor-level representation by aggregating neighbors' representations with a weighted combination. The attention mechanism enables entities not only capture different impacts of their neighbors on themselves, but also attend over their neighbors' feature representations with different importance. We evaluate our model on two real-world datasets DBP15K and DWY100K, and the experimental results show that the proposed model NAEA significantly and consistently outperforms state-of-the-art entity alignment models.

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AAAI Conference 2018 Conference Paper

Knowledge Graph Embedding With Iterative Guidance From Soft Rules

Shu Guo
Quan Wang
Lihong Wang
Bin Wang
Li Guo

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various conﬁdence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves signiﬁcant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly bene- ﬁcial to KG embedding, even those with moderate conﬁdence levels. The code and data used for this paper can be obtained from https: //github. com/iieir-km/RUGE.

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AAAI Conference 2018 Conference Paper

Social Recommendation with an Essential Preference Space

Chun-Yi Liu
Chuan Zhou
Jia Wu
Yue Hu
Li Guo

Social recommendation, which aims to exploit social information to improve the quality of a recommender system, has attracted an increasing amount of attention in recent years. A large portion of existing social recommendation models are based on the tractable assumption that users consider the same factors to make decisions in both recommender systems and social networks. However, this assumption is not in concert with real-world situations, since users usually show different preferences in different scenarios. In this paper, we investigate how to exploit the differences between user preference in recommender systems and that in social networks, with the aim to further improve the social recommendation. In particular, we assume that the user preferences in different scenarios are results of different linear combinations from a more underlying user preference space. Based on this assumption, we propose a novel social recommendation framework, called social recommendation with an essential preferences space (SREPS), which simultaneously models the structural information in the social network, the rating and the consumption information in the recommender system under the capture of essential preference space. Experimental results on four real-world datasets demonstrate the superiority of the proposed SREPS model compared with seven stateof-the-art social recommendation methods.

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AAAI Conference 2016 Conference Paper

On the Minimum Differentially Resolving Set Problem for Diffusion Source Inference in Networks

Chuan Zhou
Wei-Xue Lu
Peng Zhang
Jia Wu
Yue Hu
Li Guo

In this paper we theoretically study the minimum Differentially Resolving Set (DRS) problem derived from the classical sensor placement optimization problem in network source locating. A DRS of a graph G = (V, E) is deﬁned as a subset S ⊆ V where any two elements in V can be distinguished by their different differential characteristic sets deﬁned on S. The minimum DRS problem aims to ﬁnd a DRS S in the graph G with minimum total weight v∈S w(v). In this paper we establish a group of Integer Linear Programming (ILP) models as the solution. By the weighted set cover theory, we propose an approximation algorithm with the Θ(ln n) approximability for the minimum DRS problem on general graphs, where n is the graph size.

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IJCAI Conference 2015 Conference Paper

Knowledge Base Completion Using Embeddings and Rules

Quan Wang
Bin Wang
Li Guo

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. A promising approach is to embed KBs into latent spaces and make inferences by learning and operating on latent representations. Such embedding models, however, do not make use of any rules during inference and hence have limited accuracy. This paper proposes a novel approach which incorporates rules seamlessly into embedding models for KB completion. It formulates inference as an integer linear programming (ILP) problem, with the objective function generated from embedding models and the constraints translated from rules. Solving the ILP problem results in a number of facts which 1) are the most preferred by the embedding models, and 2) comply with all the rules. By incorporating rules, our approach can greatly reduce the solution space and significantly improve the inference accuracy of embedding models. We further provide a slacking technique to handle noise in KBs, by explicitly modeling the noise with slack variables. Experimental results on two publicly available data sets show that our approach significantly and consistently outperforms state-of-the-art embedding models in KB completion. Moreover, the slacking technique is effective in identifying erroneous facts and ambiguous entities, with a precision higher than 90%.

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AAAI Conference 2014 Conference Paper

Combining Heterogenous Social and Geographical Information for Event Recommendation

Zhi Qiao
Peng Zhang
Yanan Cao
Chuan Zhou
Li Guo
Binxing Fang

With the rapid growth of event-based social networks (EBSNs) like Meetup, the demand for event recommendation becomes increasingly urgent. In EBSNs, event recommendation plays a central role in recommending the most relevant events to users who are likely to participate in. Different from traditional recommendation problems, event recommendation encounters three new types of information, i. e. , heterogenous online+offline social relationships, geographical features of events and implicit rating data from users. Yet combining the three types of data for offline event recommendation has not been considered. Therefore, we present a Bayesian latent factor model that can unify these data for event recommendation. Experimental results on real-world data sets show the performance of our method.

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AAAI Conference 2014 Conference Paper

Event Recommendation in Event-Based Social Networks

Zhi Qiao
Peng Zhang
Chuan Zhou
Yanan Cao
Li Guo
Yanchuan Zhang

With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. Different from classic recommendation problems, event recommendation generally faces the problems of heterogenous online and offline social relationships among users and implicit feedback data. In this paper, we present a baysian probability model that can fully unleash the power of heterogenous social relations and efficiently tackle with implicit feedback characteristic for event recommendation. Experimental results on several real-world datasets demonstrate the utility of our method.

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