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Jiawei Han - One of the best experts on this subject based on the ideXlab platform.

  • active learning on heterogeneous Information Networks a multi armed bandit approach
    International Conference on Data Mining, 2018
    Co-Authors: Doris Xin, Brandon Norick, Ahmed Elkishky, De Liao, Jiawei Han
    Abstract:

    Active learning exploits inherent structures in the unlabeled data to minimize the number of labels required to train an accurate model. It enables effective machine learning in applications with high labeling cost, such as document classification and drug response prediction. We investigate active learning on heterogeneous Information Networks, with the objective of obtaining accurate node classifications while minimizing the number of labeled nodes. Our proposed algorithm harnesses a multi-armed bandit (MAB) algorithm to determine network structures that identify the most important nodes to the classification task, accounting for node types and without assuming label assortativity. Evaluations on real-world network classification tasks demonstrate that our algorithm outperforms existing methods independent of the underlying classification model.

  • easing embedding learning by comprehensive transcription of heterogeneous Information Networks
    Knowledge Discovery and Data Mining, 2018
    Co-Authors: Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han
    Abstract:

    Heterogeneous Information Networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich Information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible Information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous Information Networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.

  • easing embedding learning by comprehensive transcription of heterogeneous Information Networks
    arXiv: Social and Information Networks, 2018
    Co-Authors: Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han
    Abstract:

    Heterogeneous Information Networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich Information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible Information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous Information Networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at this https URL.

  • distant meta path similarities for text based heterogeneous Information Networks
    Conference on Information and Knowledge Management, 2017
    Co-Authors: Chenguang Wang, Yizhou Sun, Yangqiu Song, Ming Zhang, Jiawei Han
    Abstract:

    Measuring network similarity is a fundamental data mining problem. The mainstream similarity measures mainly leverage the structural Information regarding to the entities in the network without considering the network semantics. In the real world, the heterogeneous Information Networks (HINs) with rich semantics are ubiquitous. However, the existing network similarity doesn't generalize well in HINs because they fail to capture the HIN semantics. The meta-path has been proposed and demonstrated as a right way to represent semantics in HINs. Therefore, original meta-path based similarities (e.g., PathSim and KnowSim) have been successful in computing the entity proximity in HINs. The intuition is that the more instances of meta-path(s) between entities, the more similar the entities are. Thus the original meta-path similarity only applies to computing the proximity of two neighborhood (connected) entities. In this paper, we propose the distant meta-path similarity that is able to capture HIN semantics between two distant (isolated) entities to provide more meaningful entity proximity. The main idea is that even there is no shared neighborhood entities of (i.e., no meta-path instances connecting) the two entities, but if the more similar neighborhood entities of the entities are, the more similar the two entities should be. We then find out the optimum distant meta-path similarity by exploring the similarity hypothesis space based on different theoretical foundations. We show the state-of-the-art similarity performance of distant meta-path similarity on two text-based HINs and make the datasets public available.

  • meta path guided embedding for similarity search in large scale heterogeneous Information Networks
    arXiv: Social and Information Networks, 2016
    Co-Authors: Jingbo Shang, Jiawei Han, Jialu Liu, Lance Kaplan, Jian Peng
    Abstract:

    Most real-world data can be modeled as heterogeneous Information Networks (HINs) consisting of vertices of multiple types and their relationships. Search for similar vertices of the same type in large HINs, such as bibliographic Networks and business-review Networks, is a fundamental problem with broad applications. Although similarity search in HINs has been studied previously, most existing approaches neither explore rich semantic Information embedded in the network structures nor take user's preference as a guidance. In this paper, we re-examine similarity search in HINs and propose a novel embedding-based framework. It models vertices as low-dimensional vectors to explore network structure-embedded similarity. To accommodate user preferences at defining similarity semantics, our proposed framework, ESim, accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space. Moreover, an efficient and parallel sampling-based optimization algorithm has been developed to learn embeddings in large-scale HINs. Extensive experiments on real-world large-scale HINs demonstrate a significant improvement on the effectiveness of ESim over several state-of-the-art algorithms as well as its scalability.

Yizhou Sun - One of the best experts on this subject based on the ideXlab platform.

  • distant meta path similarities for text based heterogeneous Information Networks
    Conference on Information and Knowledge Management, 2017
    Co-Authors: Chenguang Wang, Yizhou Sun, Yangqiu Song, Ming Zhang, Jiawei Han
    Abstract:

    Measuring network similarity is a fundamental data mining problem. The mainstream similarity measures mainly leverage the structural Information regarding to the entities in the network without considering the network semantics. In the real world, the heterogeneous Information Networks (HINs) with rich semantics are ubiquitous. However, the existing network similarity doesn't generalize well in HINs because they fail to capture the HIN semantics. The meta-path has been proposed and demonstrated as a right way to represent semantics in HINs. Therefore, original meta-path based similarities (e.g., PathSim and KnowSim) have been successful in computing the entity proximity in HINs. The intuition is that the more instances of meta-path(s) between entities, the more similar the entities are. Thus the original meta-path similarity only applies to computing the proximity of two neighborhood (connected) entities. In this paper, we propose the distant meta-path similarity that is able to capture HIN semantics between two distant (isolated) entities to provide more meaningful entity proximity. The main idea is that even there is no shared neighborhood entities of (i.e., no meta-path instances connecting) the two entities, but if the more similar neighborhood entities of the entities are, the more similar the two entities should be. We then find out the optimum distant meta-path similarity by exploring the similarity hypothesis space based on different theoretical foundations. We show the state-of-the-art similarity performance of distant meta-path similarity on two text-based HINs and make the datasets public available.

  • meta structure computing relevance in large heterogeneous Information Networks
    Knowledge Discovery and Data Mining, 2016
    Co-Authors: Zhipeng Huang, Yizhou Sun, Reynold Cheng, Yudian Zheng, Nikos Mamoulis
    Abstract:

    A heterogeneous Information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures can be used in various applications, including entity resolution, recommendation, and Information retrieval. Several studies have investigated the use of HIN Information for relevance computation, however, most of them only utilize simple structure, such as path, to measure the similarity between objects. In this paper, we propose to use meta structure, which is a directed acyclic graph of object types with edge types connecting in between, to measure the proximity between objects. The strength of meta structure is that it can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we further design an algorithm with data structures proposed to support their evaluation. Our extensive experiments on YAGO and DBLP show that meta structure-based relevance is more effective than state-of-the-art approaches, and can be efficiently computed.

  • modeling topic diffusion in multi relational bibliographic Information Networks
    Conference on Information and Knowledge Management, 2014
    Co-Authors: Huan Gui, Jiawei Han, Yizhou Sun, George Brova
    Abstract:

    Information diffusion has been widely studied in Networks, aiming to model the spread of Information among objects when they are connected with each other. Most of the current research assumes the underlying network is homogeneous, i.e., objects are of the same type and they are connected by links with the same semantic meanings. However, in the real word, objects are connected via different types of relationships, forming multi-relational heterogeneous Information Networks. In this paper, we propose to model Information diffusion in such multi-relational Networks, by distinguishing the power in passing Information around for different types of relationships. We propose two variations of the linear threshold model for multi-relational Networks, by considering the aggregation of Information at either the model level or the relation level. In addition, we use real diffusion action logs to learn the parameters in these models, which will benefit diffusion prediction in real Networks. We apply our diffusion models in two real bibliographic Information Networks, DBLP network and APS network, and experimentally demonstrate the effectiveness of our models compared with single-relational diffusion models. Moreover, our models can determine the diffusion power of each relation type, which helps us understand the diffusion process better in the multi-relational bibliographic network scenario.

  • recommendation in heterogeneous Information Networks with implicit user feedback
    Conference on Recommender Systems, 2013
    Co-Authors: Xiang Ren, Yizhou Sun, Brandon Norick, Bradley Sturt, Urvashi Khandelwal, Jiawei Han
    Abstract:

    Recent studies suggest that by using additional user or item relationship Information when building hybrid recommender systems, the recommendation quality can be largely improved. However, most such studies only consider a single type of relationship, e.g., social network. Notice that in many applications, the recommendation problem exists in an attribute-rich heterogeneous Information network environment. In this paper, we study the entity recommendation problem in heterogeneous Information Networks. We propose to combine various relationship Information from the network with user feedback to provide high quality recommendation results. The major challenge of building recommender systems in heterogeneous Information Networks is to systematically define features to represent the different types of relationships between entities, and learn the importance of each relationship type. In the proposed framework, we first use meta-path-based latent features to represent the connectivity between users and items along different paths in the related Information network. We then define a recommendation model with such latent features and use Bayesian ranking optimization techniques to estimate the model. Empirical studies show that our approach outperforms several widely employed implicit feedback entity recommendation techniques.

  • pathselclus integrating meta path selection with user guided object clustering in heterogeneous Information Networks
    ACM Transactions on Knowledge Discovery From Data, 2013
    Co-Authors: Yizhou Sun, Jiawei Han, Brandon Norick, Xifeng Yan
    Abstract:

    Real-world, multiple-typed objects are often interconnected, forming heterogeneous Information Networks. A major challenge for link-based clustering in such Networks is their potential to generate many different results, carrying rather diverse semantic meanings. In order to generate desired clustering, we propose to use meta-path, a path that connects object types via a sequence of relations, to control clustering with distinct semantics. Nevertheless, it is easier for a user to provide a few examples (seeds) than a weighted combination of sophisticated meta-paths to specify her clustering preference. Thus, we propose to integrate meta-path selection with user-guided clustering to cluster objects in Networks, where a user first provides a small set of object seeds for each cluster as guidance. Then the system learns the weight for each meta-path that is consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths. A probabilistic approach is proposed to solve the problem, and an effective and efficient iterative algorithm, PathSelClus, is proposed to learn the model, where the clustering quality and the meta-path weights mutually enhance each other. Our experiments with several clustering tasks in two real Networks and one synthetic network demonstrate the power of the algorithm in comparison with the baselines.

Philip S Yu - One of the best experts on this subject based on the ideXlab platform.

  • hitfraud a broad learning approach for collective fraud detection in heterogeneous Information Networks
    International Conference on Data Mining, 2017
    Co-Authors: Siim Viidu, Philip S Yu
    Abstract:

    On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on product and its popularity, the offer type (packaged game, virtual currency to a game or subscription service), storefront and geography. Existing fraud policies and models make decisions independently for each transaction based on transaction attributes, payment velocities, user characteristics, and other relevant Information. However, suspicious transactions may still evade detection and hence we propose a broad learning approach leveraging a graph based perspective to uncover relationships among suspicious transactions, i.e., inter-transaction dependency. Our focus is to detect suspicious transactions by capturing common fraudulent behaviors that would not be considered suspicious when being considered in isolation. In this paper, we present HitFraud that leverages heterogeneous Information Networks for collective fraud detection by exploring correlated and fast evolving fraudulent behaviors. First, a heterogeneous Information network is designed to link entities of interest in the transaction database via different semantics. Then, graph based features are efficiently discovered from the network exploiting the concept of meta-paths, and decisions on frauds are made collectively on test instances. Experiments on real-world payment transaction data from Electronic Arts demonstrate that the prediction performance is effectively boosted by HitFraud with fast convergence.

  • hitfraud a broad learning approach for collective fraud detection in heterogeneous Information Networks
    arXiv: Learning, 2017
    Co-Authors: Siim Viidu, Philip S Yu
    Abstract:

    On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on product and its popularity, the offer type (packaged game, virtual currency to a game or subscription service), storefront and geography. Existing fraud policies and models make decisions independently for each transaction based on transaction attributes, payment velocities, user characteristics, and other relevant Information. However, suspicious transactions may still evade detection and hence we propose a broad learning approach leveraging a graph based perspective to uncover relationships among suspicious transactions, i.e., inter-transaction dependency. Our focus is to detect suspicious transactions by capturing common fraudulent behaviors that would not be considered suspicious when being considered in isolation. In this paper, we present HitFraud that leverages heterogeneous Information Networks for collective fraud detection by exploring correlated and fast evolving fraudulent behaviors. First, a heterogeneous Information network is designed to link entities of interest in the transaction database via different semantics. Then, graph based features are efficiently discovered from the network exploiting the concept of meta-paths, and decisions on frauds are made collectively on test instances. Experiments on real-world payment transaction data from Electronic Arts demonstrate that the prediction performance is effectively boosted by HitFraud with fast convergence where the computation of meta-path based features is largely optimized. Notably, recall can be improved up to 7.93% and F-score 4.62% compared to baselines.

  • mining knowledge from interconnected data a heterogeneous Information network analysis approach
    Very Large Data Bases, 2012
    Co-Authors: Philip S Yu
    Abstract:

    Most objects and data in the real world are interconnected, forming complex, heterogeneous but often semi-structured Information Networks. However, most people consider a database merely as a data repository that supports data storage and retrieval rather than one or a set of heterogeneous Information Networks that contain rich, inter-related, multi-typed data and Information. Most network science researchers only study homogeneous Networks, without distinguishing the different types of objects and links in the Networks. In this tutorial, we view database and other interconnected data as heterogeneous Information Networks, and study how to leverage the rich semantic meaning of types of objects and links in the Networks. We systematically introduce the technologies that can effectively and efficiently mine useful knowledge from such Information Networks.

  • mining knowledge from databases an Information network analysis approach
    International Conference on Management of Data, 2010
    Co-Authors: Philip S Yu
    Abstract:

    Most people consider a database is merely a data repository that supports data storage and retrieval. Actually, a database contains rich, inter-related, multi-typed data and Information, forming one or a set of gigantic, interconnected, heterogeneous Information Networks. Much knowledge can be derived from such Information Networks if we systematically develop an effective and scalable database-oriented Information network analysis technology. In this tutorial, we introduce database-oriented Information network analysis methods and demonstrate how Information Networks can be used to improve data quality and consistency, facilitate data integration, and generate interesting knowledge. This tutorial presents an organized picture on how to turn a database into one or a set of organized heterogeneous Information Networks, how Information Networks can be used for data cleaning, data consolidation, and data qualify improvement, how to discover various kinds of knowledge from Information Networks, how to perform OLAP in Information Networks, and how to transform database data into knowledge by Information network analysis. Moreover, we present interesting case studies on real datasets, including DBLP and Flickr, and show how interesting and organized knowledge can be generated from database-oriented Information Networks.

Jing Gao - One of the best experts on this subject based on the ideXlab platform.

  • graph regularized transductive classification on heterogeneous Information Networks
    European conference on Machine Learning, 2010
    Co-Authors: Yizhou Sun, Jiawei Han, Marina Danilevsky, Jing Gao
    Abstract:

    A heterogeneous Information network is a network composed of multiple types of objects and links. Recently, it has been recognized that strongly-typed heterogeneous Information Networks are prevalent in the real world. Sometimes, label Information is available for some objects. Learning from such labeled and unlabeled data via transductive classification can lead to good knowledge extraction of the hidden network structure. However, although classification on homogeneous Networks has been studied for decades, classification on heterogeneous Networks has not been explored until recently. In this paper, we consider the transductive classification problem on heterogeneous networked data which share a common topic. Only some objects in the given network are labeled, and we aim to predict labels for all types of the remaining objects. A novel graph-based regularization framework, GNetMine, is proposed to model the link structure in Information Networks with arbitrary network schema and arbitrary number of object/link types. Specifically, we explicitly respect the type differences by preserving consistency over each relation graph corresponding to each type of links separately. Efficient computational schemes are then introduced to solve the corresponding optimization problem. Experiments on the DBLP data set show that our algorithm significantly improves the classification accuracy over existing state-of-the-art methods.

  • on community outliers and their efficient detection in Information Networks
    Knowledge Discovery and Data Mining, 2010
    Co-Authors: Jing Gao, Yizhou Sun, Feng Liang, Wei Fan, Chi Wang, Jiawei Han
    Abstract:

    Linked or networked data are ubiquitous in many applications. Examples include web data or hypertext documents connected via hyperlinks, social Networks or user profiles connected via friend links, co-authorship and citation Information, blog data, movie reviews and so on. In these datasets (called "Information Networks"), closely related objects that share the same properties or interests form a community. For example, a community in blogsphere could be users mostly interested in cell phone reviews and news. Outlier detection in Information Networks can reveal important anomalous and interesting behaviors that are not obvious if community Information is ignored. An example could be a low-income person being friends with many rich people even though his income is not anomalously low when considered over the entire population. This paper first introduces the concept of community outliers (interesting points or rising stars for a more positive sense), and then shows that well-known baseline approaches without considering links or community Information cannot find these community outliers. We propose an efficient solution by modeling networked data as a mixture model composed of multiple normal communities and a set of randomly generated outliers. The probabilistic model characterizes both data and links simultaneously by defining their joint distribution based on hidden Markov random fields (HMRF). Maximizing the data likelihood and the posterior of the model gives the solution to the outlier inference problem. We apply the model on both synthetic data and DBLP data sets, and the results demonstrate importance of this concept, as well as the effectiveness and efficiency of the proposed approach.

Yu, Philip S. - One of the best experts on this subject based on the ideXlab platform.

  • Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Wang Yue, Xu Zhuo, Lu Bai, Wan Yao, Cui Lixin, Zhao Qian, Hancock, Edwin R., Yu, Philip S.
    Abstract:

    Joint-event-extraction, which extracts structural Information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. Most existing works do not fully address the sparse co-occurrence relationships between entities and triggers, which loses this important Information and thus deteriorates the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. Then, to incorporate the missing Information in the aforementioned co-occurrence relationships, we propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of either triggers or entities based on the type distribution of each other. Moreover, since the connected entities and triggers naturally form a heterogeneous Information network (HIN), we leverage the latent pattern along meta-paths for a given corpus to further improve the performance of our proposed method. To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods. Empirical results and analysis show that our approach outperforms the state-of-the-art methods in both entity and trigger extraction

  • Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
    2018
    Co-Authors: Sun Lichao, Cao Bokai, He Lifang, Huang Zhipeng, Xia Congying, Wei Xiaokai, Yu, Philip S.
    Abstract:

    Meta-graph is currently the most powerful tool for similarity search on heterogeneous Information Networks,where a meta-graph is a composition of meta-paths that captures the complex structural Information. However, current relevance computing based on meta-graph only considers the complex structural Information, but ignores its embedded meta-paths Information. To address this problem, we proposeMEta-GrAph-based network embedding models, called MEGA and MEGA++, respectively. The MEGA model uses normalized relevance or similarity measures that are derived from a meta-graph and its embedded meta-paths between nodes simultaneously, and then leverages tensor decomposition method to perform node embedding. The MEGA++ further facilitates the use of coupled tensor-matrix decomposition method to obtain a joint embedding for nodes, which simultaneously considers the hidden relations of all meta Information of a meta-graph.Extensive experiments on two real datasets demonstrate thatMEGA and MEGA++ are more effective than state-of-the-art approaches.Comment: accepted by ICBK 1

  • HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
    2017
    Co-Authors: Cao Bokai, Mao Mia, Viidu Siim, Yu, Philip S.
    Abstract:

    On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on product and its popularity, the offer type (packaged game, virtual currency to a game or subscription service), storefront and geography. Existing fraud policies and models make decisions independently for each transaction based on transaction attributes, payment velocities, user characteristics, and other relevant Information. However, suspicious transactions may still evade detection and hence we propose a broad learning approach leveraging a graph based perspective to uncover relationships among suspicious transactions, i.e., inter-transaction dependency. Our focus is to detect suspicious transactions by capturing common fraudulent behaviors that would not be considered suspicious when being considered in isolation. In this paper, we present HitFraud that leverages heterogeneous Information Networks for collective fraud detection by exploring correlated and fast evolving fraudulent behaviors. First, a heterogeneous Information network is designed to link entities of interest in the transaction database via different semantics. Then, graph based features are efficiently discovered from the network exploiting the concept of meta-paths, and decisions on frauds are made collectively on test instances. Experiments on real-world payment transaction data from Electronic Arts demonstrate that the prediction performance is effectively boosted by HitFraud with fast convergence where the computation of meta-path based features is largely optimized. Notably, recall can be improved up to 7.93% and F-score 4.62% compared to baselines.Comment: ICDM 201