ACM Transactions on Knowledge Discovery from Data (TKDD)最新文献_第6页

Unsupervised Adversarial Network Alignment with Reinforcement Learning 与强化学习的无监督对抗网络对齐

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-10-22 DOI: 10.1145/3477050

Yang Zhou, Jiaxiang Ren, R. Jin, Zijie Zhang, Jingyi Zheng, Zhe Jiang, Da Yan, D. Dou

{"title":"Unsupervised Adversarial Network Alignment with Reinforcement Learning","authors":"Yang Zhou, Jiaxiang Ren, R. Jin, Zijie Zhang, Jingyi Zheng, Zhe Jiang, Da Yan, D. Dou","doi":"10.1145/3477050","DOIUrl":"https://doi.org/10.1145/3477050","url":null,"abstract":"Network alignment, which aims at learning a matching between the same entities across multiple information networks, often suffers challenges from feature inconsistency, high-dimensional features, to unstable alignment results. This article presents a novel network alignment framework, Unsupervised Adversarial learning based Network Alignment(UANA), that combines generative adversarial network (GAN) and reinforcement learning (RL) techniques to tackle the above critical challenges. First, we propose a bidirectional adversarial network distribution matching model to perform the bidirectional cross-network alignment translations between two networks, such that the distributions of real and translated networks completely overlap together. In addition, two cross-network alignment translation cycles are constructed for training the unsupervised alignment without the need of prior alignment knowledge. Second, in order to address the feature inconsistency issue, we integrate a dual adversarial autoencoder module with an adversarial binary classification model together to project two copies of the same vertices with high-dimensional inconsistent features into the same low-dimensional embedding space. This facilitates the translations of the distributions of two networks in the adversarial network distribution matching model. Finally, we develop an RL based optimization approach to solve the vertex matching problem in the discrete space of the GAN model, i.e., directly select the vertices in target networks most relevant to the vertices in source networks, without unstable similarity computation that is sensitive to discriminative features and similarity metrics. Extensive evaluation on real-world graph datasets demonstrates the outstanding capability of UANA to address the unsupervised network alignment problem, in terms of both effectiveness and scalability.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125432513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Context-Aware Semantic Annotation of Mobility Records 移动记录的上下文感知语义注释

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-10-22 DOI: 10.1145/3477048

Huandong Wang, Yong Li, Junjie Lin, Hancheng Cao, Depeng Jin

引用次数: 0

Network Embedding via Motifs 基于motif的网络嵌入

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-10-22 DOI: 10.1145/3473911

Ping Shao, Yang Yang, Shengyao Xu, Chunping Wang

引用次数: 9

Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks 关注卷积网络深度迁移学习的知识蒸馏

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-10-22 DOI: 10.1145/3473912

Xingjian Li, H. Xiong, Zeyu Chen, Jun Huan, Ji Liu, Chengzhong Xu, D. Dou

{"title":"Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks","authors":"Xingjian Li, H. Xiong, Zeyu Chen, Jun Huan, Ji Liu, Chengzhong Xu, D. Dou","doi":"10.1145/3473912","DOIUrl":"https://doi.org/10.1145/3473912","url":null,"abstract":"Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly improve and accelerate training while the accuracy is frequently bottlenecked by the limited dataset size of the new target task. To solve the problem, some regularization methods, constraining the outer layer weights of the target network using the starting point as references (SPAR), have been studied. In this article, we propose a novel regularized transfer learning framework operatorname{DELTA} , namely DEep Learning Transfer using Feature Map with Attention. Instead of constraining the weights of neural network, operatorname{DELTA} aims at preserving the outer layer outputs of the source network. Specifically, in addition to minimizing the empirical loss, operatorname{DELTA} aligns the outer layer outputs of two networks, through constraining a subset of feature maps that are precisely selected by attention that has been learned in a supervised learning manner. We evaluate operatorname{DELTA} with the state-of-the-art algorithms, including L^2 and emph {L}^2text{-}SP . The experiment results show that our method outperforms these baselines with higher accuracy for new tasks. Code has been made publicly available.1","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131426975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning 基于增量多源特征学习的时空事件预测

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-09-13 DOI: 10.1145/3464976

Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Yanfang Ye, Chang-Tien Lu, Naren Ramakrishnan

{"title":"Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning","authors":"Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Yanfang Ye, Chang-Tien Lu, Naren Ramakrishnan","doi":"10.1145/3464976","DOIUrl":"https://doi.org/10.1145/3464976","url":null,"abstract":"The forecasting of significant societal events such as civil unrest and economic crisis is an interesting and challenging problem which requires both timeliness, precision, and comprehensiveness. Significant societal events are influenced and indicated jointly by multiple aspects of a society, including its economics, politics, and culture. Traditional forecasting methods based on a single data source find it hard to cover all these aspects comprehensively, thus limiting model performance. Multi-source event forecasting has proven promising but still suffers from several challenges, including (1) geographical hierarchies in multi-source data features, (2) hierarchical missing values, (3) characterization of structured feature sparsity, and (4) difficulty in model’s online update with incomplete multiple sources. This article proposes a novel feature learning model that concurrently addresses all the above challenges. Specifically, given multi-source data from different geographical levels, we design a new forecasting model by characterizing the lower-level features’ dependence on higher-level features. To handle the correlations amidst structured feature sets and deal with missing values among the coupled features, we propose a novel feature learning model based on an Nth-order strong hierarchy and fused-overlapping group Lasso. An efficient algorithm is developed to optimize model parameters and ensure global optima. More importantly, to enable the model update in real time, the online learning algorithm is formulated and active set techniques are leveraged to resolve the crucial challenge when new patterns of missing features appear in real time. Extensive experiments on 10 datasets in different domains demonstrate the effectiveness and efficiency of the proposed models.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128476583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs 基于微博话题标签推荐的句子到话题标签语义映射学习

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-09-04 DOI: 10.1145/3466876

Riccardo Cantini, F. Marozzo, Giovanni Bruno, Paolo Trunfio

{"title":"Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs","authors":"Riccardo Cantini, F. Marozzo, Giovanni Bruno, Paolo Trunfio","doi":"10.1145/3466876","DOIUrl":"https://doi.org/10.1145/3466876","url":null,"abstract":"The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and searched. In Twitter and other social media platforms, hashtags are exploited by users to facilitate the search, categorization, and spread of posts. Choosing the appropriate hashtags for a post is not always easy for users, and therefore posts are often published without hashtags or with hashtags not well defined. To deal with this issue, we propose a new model, called HASHET (HAshtag recommendation using Sentence-to-Hashtag Embedding Translation), aimed at suggesting a relevant set of hashtags for a given post. HASHET is based on two independent latent spaces for embedding the text of a post and the hashtags it contains. A mapping process based on a multi-layer perceptron is then used for learning a translation from the semantic features of the text to the latent representation of its hashtags. We evaluated the effectiveness of two language representation models for sentence embedding and tested different search strategies for semantic expansion, finding out that the combined use of BERT (Bidirectional Encoder Representation from Transformer) and a global expansion strategy leads to the best recommendation results. HASHET has been evaluated on two real-world case studies related to the 2016 United States presidential election and COVID-19 pandemic. The results reveal the effectiveness of HASHET in predicting one or more correct hashtags, with an average F-score up to 0.82 and a recommendation hit-rate up to 0.92. Our approach has been compared to the most relevant techniques used in the literature (generative models, unsupervised models, and attention-based supervised models) by achieving up to 15% improvement in F-score for the hashtag recommendation task and 9% for the topic discovery task.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121736247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Embedding Heterogeneous Information Network in Hyperbolic Spaces 在双曲空间中嵌入异构信息网络

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-09-04 DOI: 10.1145/3468674

Yiding Zhang, Xiao Wang, Nian Liu, C. Shi

{"title":"Embedding Heterogeneous Information Network in Hyperbolic Spaces","authors":"Yiding Zhang, Xiao Wang, Nian Liu, C. Shi","doi":"10.1145/3468674","DOIUrl":"https://doi.org/10.1145/3468674","url":null,"abstract":"Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional space, has attracted considerable research attention. Most of the existing HIN embedding methods focus on preserving the inherent network structure and semantic correlations in Euclidean spaces. However, one fundamental problem is whether the Euclidean spaces are the intrinsic spaces of HIN? Recent researches find the complex network with hyperbolic geometry can naturally reflect some properties, e.g., hierarchical and power-law structure. In this article, we make an effort toward embedding HIN in hyperbolic spaces. We analyze the structures of three HINs and discover some properties, e.g., the power-law distribution, also exist in HINs. Therefore, we propose a novel HIN embedding model HHNE. Specifically, to capture the structure and semantic relations between nodes, HHNE employs the meta-path guided random walk to sample the sequences for each node. Then HHNE exploits the hyperbolic distance as the proximity measurement. We also derive an effective optimization strategy to update the hyperbolic embeddings iteratively. Since HHNE optimizes different relations in a single space, we further propose the extended model HHNE++. HHNE++ models different relations in different spaces, which enables it to learn complex interactions in HINs. The optimization strategy of HHNE++ is also derived to update the parameters of HHNE++ in a principle manner. The experimental results demonstrate the effectiveness of our proposed models.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133826485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Con&Net: A Cross-Network Anchor Link Discovery Method Based on Embedding Representation Con&Net:基于嵌入表示的跨网络锚链接发现方法

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-09-04 DOI: 10.1145/3469083

Xueyuan Wang, Hongpo Zhang, Zongmin Wang, Yaqiong Qiao, Jiangtao Ma, Honghua Dai

{"title":"Con&Net: A Cross-Network Anchor Link Discovery Method Based on Embedding Representation","authors":"Xueyuan Wang, Hongpo Zhang, Zongmin Wang, Yaqiong Qiao, Jiangtao Ma, Honghua Dai","doi":"10.1145/3469083","DOIUrl":"https://doi.org/10.1145/3469083","url":null,"abstract":"Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133239394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

KRAN: Knowledge Refining Attention Network for Recommendation KRAN:面向推荐的知识精炼注意力网络

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-09-04 DOI: 10.1145/3470783

Zhenyu Zhang, Lei Zhang, Dingqi Yang, Liu Yang

{"title":"KRAN: Knowledge Refining Attention Network for Recommendation","authors":"Zhenyu Zhang, Lei Zhang, Dingqi Yang, Liu Yang","doi":"10.1145/3470783","DOIUrl":"https://doi.org/10.1145/3470783","url":null,"abstract":"Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to Pareto’s principle, the important neighbors only account for a small proportion. These traditional algorithms can not fully mine the useful information in the KG. To fully release the power of KGs for building recommender systems, we propose in this article KRAN, a Knowledge Refining Attention Network, which can subtly capture the characteristics of the KG and thus boost recommendation performance. We first introduce a traditional attention mechanism into the KG processing, making the knowledge extraction more targeted, and then propose a refining mechanism to improve the traditional attention mechanism to extract the knowledge in the KG more effectively. More precisely, KRAN is designed to use our proposed knowledge-refining attention mechanism to aggregate and obtain the representations of the entities (both attributes and items) in the KG. Our knowledge-refining attention mechanism first measures the relevance between an entity and it’s neighbors in the KG by attention coefficients, and then further refines the attention coefficients using a “richer-get-richer” principle, in order to focus on highly relevant neighbors while eliminating less relevant neighbors for noise reduction. In addition, for the item cold start problem, we propose KRAN-CD, a variant of KRAN, which further incorporates pre-trained KG embeddings to handle cold start items. Experiments show that KRAN and KRAN-CD consistently outperform state-of-the-art baselines across different settings.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122825776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Wealth Flow Model: Online Portfolio Selection Based on Learning Wealth Flow Matrices 财富流动模型:基于学习财富流动矩阵的在线投资组合选择

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2021-09-04 DOI: 10.1145/3464308

Jianfei Yin, Ruili Wang, Yeqing Guo, Yizhe Bai, Shunda Ju, Weili Liu, J. Huang

{"title":"Wealth Flow Model: Online Portfolio Selection Based on Learning Wealth Flow Matrices","authors":"Jianfei Yin, Ruili Wang, Yeqing Guo, Yizhe Bai, Shunda Ju, Weili Liu, J. Huang","doi":"10.1145/3464308","DOIUrl":"https://doi.org/10.1145/3464308","url":null,"abstract":"This article proposes a deep learning solution to the online portfolio selection problem based on learning a latent structure directly from a price time series. It introduces a novel wealth flow matrix for representing a latent structure that has special regular conditions to encode the knowledge about the relative strengths of assets in portfolios. Therefore, a wealth flow model (WFM) is proposed to learn wealth flow matrices and maximize portfolio wealth simultaneously. Compared with existing approaches, our work has several distinctive benefits: (1) the learning of wealth flow matrices makes our model more generalizable than models that only predict wealth proportion vectors, and (2) the exploitation of wealth flow matrices and the exploration of wealth growth are integrated into our deep reinforcement algorithm for the WFM. These benefits, in combination, lead to a highly-effective approach for generating reasonable investment behavior, including short-term trend following, the following of a few losers, no self-investment, and sparse portfolios. Extensive experiments on five benchmark datasets from real-world stock markets confirm the theoretical advantage of the WFM, which achieves the Pareto improvements in terms of multiple performance indicators and the steady growth of wealth over the state-of-the-art algorithms.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115736463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1