The Experts below are selected from a list of 36726 Experts worldwide ranked by ideXlab platform
Céline Rouveirol - One of the best experts on this subject based on the ideXlab platform.
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a supervised machine learning Link Prediction approach for academic collaboration recommendation
Conference on Recommender Systems, 2010Co-Authors: Nesserine Benchettara, Rushed Kanawati, Céline RouveirolAbstract:In this work we tackle the problem of Link Prediction in co-authoring network. We apply a topological dyadic supervised machine learning approach for that purpose. A co-authoring network is actually obtained by the projection of a two-mode graph (an authoring graph Linking authors to publications they have signed) over the authors set. We show that Link Prediction performances can be substantially enhanced by analyzing not only the co-authoring network, but also the dual graph obtained by projecting the original two-mode network over the set of publications.
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supervised machine learning applied to Link Prediction in bipartite social networks
Advances in Social Networks Analysis and Mining, 2010Co-Authors: Nesserine Benchettara, Rushed Kanawati, Céline RouveirolAbstract:This work copes with the problem of Link Prediction in large-scale two-mode social networks. Two variations of the Link Prediction tasks are studied: predicting Links in a bipartite graph and predicting Links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of Prediction models we learn. This is achieved by introducing new variations of topological atttributes to measure the likelihood of two nodes to be connected. Our approach, for both tasks, consists in expressing the Link Prediction problem as a two class discrimination problem. Classical supervised machine learning approaches can then be applied in order to learn Prediction models. Experimental validation of the proposed approach is carried out on two real data sets: a co-authoring network extracted from the DBLP bibliographical database and bipartite graph history of transactions on an on-line music e-commerce site.
Chuan Shi - One of the best experts on this subject based on the ideXlab platform.
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meta path based Link Prediction in schema rich heterogeneous information network
Journal of data science, 2017Co-Authors: Xiaohuan Cao, Yuyan Zheng, Chuan ShiAbstract:Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Complex structure and rich semantics are unique features of HIN. Meta-path, the sequence of object types and relations connecting them, has been widely used to mine this semantic information in HIN. Link Prediction is an important data mining task to predict the potential Links among nodes, which are required in many applications, e.g., filling missing Links. The contemporary Link Prediction is usually based on simple HIN whose schema is bipartite or star schema. In these works, the meta-paths should be predefined or enumerated. However, in many real networked data, it is hard to describe their network structures with simple schema. For example, the RDF-formatted Knowledge Graph which includes tens of thousands types of objects and Links is a kind of schema-rich HIN. In this kind of schema-rich HIN, it is impossible to enumerate meta-paths so that the contemporary work is invalid. In this paper, we study Link Prediction in schema-rich HIN and propose a novel method named Link Prediction with automatic meta Path (LiPaP). The LiPaP designs an algorithm called automatic meta-path generation to automatically extract meta-paths from schema-rich HIN in the approximate order of relevance and adopt a supervised method with likelihood function to learn the weights of extracted meta-paths. Extensive experiments on real knowledge database, Yago, demonstrate that LiPaP is an effective, steady and efficient approach.
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Link Prediction in schema rich heterogeneous information network
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2016Co-Authors: Xiaohuan Cao, Yuyan Zheng, Chuan ShiAbstract:Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Many data mining tasks have been explored in this kind of network. Among them, Link Prediction is an important task to predict the potential Links among nodes, which are required in many applications. The contemporary Link Prediction usually are based on simple HIN whose schema are bipartite or star-schema. In these HINs, the meta paths are predefined or can be enumerated. However, in many real networked data, it is hard to describe their network structure with simple schema. For example, the knowledge base with RDF format include tens of thousands types of objects and Links. On this kind of schema-rich HIN, it is impossible to enumerate meta paths. In this paper, we study the Link Prediction in schema-rich HIN and propose a novel Link Prediction with automatic meta Paths method (LiPaP). The LiPaP designs an algorithm called Automatic Meta Path Generation (AMPG) to automatically extract meta paths from schema-rich HIN and a supervised method with likelihood function to learn weights of the extracted meta paths. Experiments on real knowledge database, Yago, validate that LiPaP is an effective, steady and efficient method.
Nesserine Benchettara - One of the best experts on this subject based on the ideXlab platform.
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a supervised machine learning Link Prediction approach for academic collaboration recommendation
Conference on Recommender Systems, 2010Co-Authors: Nesserine Benchettara, Rushed Kanawati, Céline RouveirolAbstract:In this work we tackle the problem of Link Prediction in co-authoring network. We apply a topological dyadic supervised machine learning approach for that purpose. A co-authoring network is actually obtained by the projection of a two-mode graph (an authoring graph Linking authors to publications they have signed) over the authors set. We show that Link Prediction performances can be substantially enhanced by analyzing not only the co-authoring network, but also the dual graph obtained by projecting the original two-mode network over the set of publications.
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supervised machine learning applied to Link Prediction in bipartite social networks
Advances in Social Networks Analysis and Mining, 2010Co-Authors: Nesserine Benchettara, Rushed Kanawati, Céline RouveirolAbstract:This work copes with the problem of Link Prediction in large-scale two-mode social networks. Two variations of the Link Prediction tasks are studied: predicting Links in a bipartite graph and predicting Links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of Prediction models we learn. This is achieved by introducing new variations of topological atttributes to measure the likelihood of two nodes to be connected. Our approach, for both tasks, consists in expressing the Link Prediction problem as a two class discrimination problem. Classical supervised machine learning approaches can then be applied in order to learn Prediction models. Experimental validation of the proposed approach is carried out on two real data sets: a co-authoring network extracted from the DBLP bibliographical database and bipartite graph history of transactions on an on-line music e-commerce site.
Reda Alhajj - One of the best experts on this subject based on the ideXlab platform.
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extension of neighbor based Link Prediction methods for directed weighted and temporal social networks
Information Sciences, 2018Co-Authors: Ertan Butun, Mehmet Kaya, Reda AlhajjAbstract:Abstract Link Prediction is one of the most interesting tasks in social network analysis. It has received considerable attention as evident by the number of studies described in the literature. Recently, heterogeneous, temporal or directed based network models have attracted considerable attention to deal with effectively real complex networks in terms of Link Prediction. Most of the Link Prediction measures in the literature don't consider the role of Link direction. In this study, we introduce a directional Link Prediction measure by extending neighbor based measures as directional pattern based to take into account the role of Link direction in directed networks. The introduced measure also considers weight and time information of Links, which are effective to improve accuracy of Link Prediction. In experiments, the introduced measure is compared to nine well-known Link Prediction measures in the literature by using supervised learning algorithms. Experimental results demonstrate that the proposed approach improves remarkably the accuracy of Link Prediction. This is mainly due to using structural information of networks effectively without requiring more information and computational time. 2018.
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a new topological metric for Link Prediction in directed weighted and temporal networks
Advances in Social Networks Analysis and Mining, 2016Co-Authors: Ertan Butun, Mehmet Kaya, Reda AlhajjAbstract:One of the most interesting tasks in social network analysis is Link Prediction. There are a lot of studies dealing with Link Prediction task in the literature. In recent years, there is an increasing on Link Prediction methods trying to model network as more close to real networks such as heterogeneous, temporal and directed network models to gain better Link Prediction performance. Many of the existing Link Prediction methods don't take into account Links directions in directed networks. In this paper we propose a new neighbor and graph pattern based topological metric considering direction of Links for Link Prediction. The proposed metric also takes into account temporal and weighted information, which are useful to increase Link Prediction performance. Accuracy of the proposed metric is evaluated by comparison with multiple baseline metrics from literature in supervised learning methods. Experimental results demonstrate that the proposed metric improves remarkably the accuracy of Link Prediction.
Xiaohuan Cao - One of the best experts on this subject based on the ideXlab platform.
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meta path based Link Prediction in schema rich heterogeneous information network
Journal of data science, 2017Co-Authors: Xiaohuan Cao, Yuyan Zheng, Chuan ShiAbstract:Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Complex structure and rich semantics are unique features of HIN. Meta-path, the sequence of object types and relations connecting them, has been widely used to mine this semantic information in HIN. Link Prediction is an important data mining task to predict the potential Links among nodes, which are required in many applications, e.g., filling missing Links. The contemporary Link Prediction is usually based on simple HIN whose schema is bipartite or star schema. In these works, the meta-paths should be predefined or enumerated. However, in many real networked data, it is hard to describe their network structures with simple schema. For example, the RDF-formatted Knowledge Graph which includes tens of thousands types of objects and Links is a kind of schema-rich HIN. In this kind of schema-rich HIN, it is impossible to enumerate meta-paths so that the contemporary work is invalid. In this paper, we study Link Prediction in schema-rich HIN and propose a novel method named Link Prediction with automatic meta Path (LiPaP). The LiPaP designs an algorithm called automatic meta-path generation to automatically extract meta-paths from schema-rich HIN in the approximate order of relevance and adopt a supervised method with likelihood function to learn the weights of extracted meta-paths. Extensive experiments on real knowledge database, Yago, demonstrate that LiPaP is an effective, steady and efficient approach.
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Link Prediction in schema rich heterogeneous information network
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2016Co-Authors: Xiaohuan Cao, Yuyan Zheng, Chuan ShiAbstract:Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Many data mining tasks have been explored in this kind of network. Among them, Link Prediction is an important task to predict the potential Links among nodes, which are required in many applications. The contemporary Link Prediction usually are based on simple HIN whose schema are bipartite or star-schema. In these HINs, the meta paths are predefined or can be enumerated. However, in many real networked data, it is hard to describe their network structure with simple schema. For example, the knowledge base with RDF format include tens of thousands types of objects and Links. On this kind of schema-rich HIN, it is impossible to enumerate meta paths. In this paper, we study the Link Prediction in schema-rich HIN and propose a novel Link Prediction with automatic meta Paths method (LiPaP). The LiPaP designs an algorithm called Automatic Meta Path Generation (AMPG) to automatically extract meta paths from schema-rich HIN and a supervised method with likelihood function to learn weights of the extracted meta paths. Experiments on real knowledge database, Yago, validate that LiPaP is an effective, steady and efficient method.
