Graph Regularized Transductive Classification on Heterogeneous Information Networks

Ji, Ming; Sun, Yizhou; Danilevsky, Marina; Han, Jiawei; Gao, Jing

doi:10.1007/978-3-642-15880-3_42

Cited by 212 publications

(213 citation statements)

References 12 publications

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“…And kinds of data mining tasks for HIN are realized recently. These research developments include similarity measure [12,24], clustering [25,26], classification [9,11], link prediction [2,22], ranking [14,34], recommendation [8,18], information fusion [10,19]. But these tasks just work on simple HINs with simple schema.…”

Section: Heterogeneous Information Networkmentioning

confidence: 99%

See 1 more Smart Citation

Meta-path-based link prediction in schema-rich heterogeneous information network

Cao

Zheng

Shi

et al. 2017

Int J Data Sci Anal

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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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Section: Heterogeneous Information Networkmentioning

confidence: 99%

“…In order to explain algorithm clearly, we would illustrate the process of AMPG through a toy example in Fig. 2, where the training pairs are { (1,8), (2,8), (3,9), (4,9)}.…”

Section: Automatic Meta-path Generationmentioning

confidence: 99%

Meta-path-based link prediction in schema-rich heterogeneous information network

Cao

Zheng

Shi

et al. 2017

Int J Data Sci Anal

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“…We used an heterogeneous version of LLGC, called GNetMine 7 [8], for transductive classification in bipartite networks. The classification procedure using bipartite networks was the same presented in [20].…”

Section: Experiments Configuration and Evaluation Criteriamentioning

confidence: 99%

Term Network Approach for Transductive Classification

Rossi¹,

Rezende²,

Lopes³

2015

Lecture Notes in Computer Science

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Abstract. Transductive classification is a useful way to classify texts when just few labeled examples are available. Transductive classification algorithms rely on term frequency to directly classify texts represented in vector space model or to build networks and perform label propagation. Related terms tend to belong to the same class and this information can be used to assign relevance scores of terms for classes and consequently the labels of documents. In this paper we propose the use of term networks to model term relations and perform transductive classification. In order to do so, we propose (i) different ways to generate term networks, (ii) how to assign initial relevance scores for terms, (iii) how to propagate the relevance scores among terms, and (iv) how to use the relevance scores of terms in order to classify documents. We demonstrate that transductive classification based on term networks can surpass the accuracies obtained by transductive classification considering texts represented in other types of networks or vector space model, or even the accuracies obtained by inductive classification. We also demonstrated that we can decrease the size of term networks through feature selection while keeping classification accuracy and decreasing computational complexity.

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“…Three clustering methods are used in our experiment for comparison: PathSelClus [9], GNetMine [20] and LP [7]. The first two algorithms are proposed for heterogeneous networks, and they are regarded as the state-of-the-art clustering algorithms.…”

Section: Case Study On Effectivenessmentioning

confidence: 99%

Semi-supervised Clustering on Heterogeneous Information Networks

Luo

Pang

Wang

2014

Advances in Knowledge Discovery and Data Mining

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Abstract. Semi-supervised clustering on information networks combines both the labeled and unlabeled data sets with an aim to improve the clustering performance. However, the existing semi-supervised clustering methods are all designed for homogeneous networks and do not deal with heterogeneous ones. In this work, we propose a semi-supervised clustering approach to analyze heterogeneous information networks, which include multi-typed objects and links and may contain more useful semantic information. The major challenge in the clustering task here is how to handle multi-relations and diverse semantic meanings in heterogeneous networks. In order to deal with this challenge, we introduce the concept of relation-path to measure the similarity between two data objects of the same type. Thereafter, we make use of the labeled information to extract different weights for all relation-paths. Finally, we propose SemiRPClus, a complete framework for semi-supervised learning in heterogeneous networks. Experimental results demonstrate the distinct advantages in effectiveness and efficiency of our framework in comparison with the baseline and some state-of-the-art approaches.

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Graph Regularized Transductive Classification on Heterogeneous Information Networks

Cited by 212 publications

References 12 publications

Meta-path-based link prediction in schema-rich heterogeneous information network

Meta-path-based link prediction in schema-rich heterogeneous information network

Term Network Approach for Transductive Classification

Semi-supervised Clustering on Heterogeneous Information Networks

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