Citation Prediction in Heterogeneous Bibliographic Networks

Yu, Xiao; Gu, Quanquan; Zhou, Mianwei; Han, Jiawei

doi:10.1137/1.9781611972825.96

Cited by 101 publications

(65 citation statements)

References 20 publications

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“…Our dataset [20,25]is a subset containing scientific papers in four areas: databases, data mining, artificial intelligence, and information retrieval. The dataset has four classes of nodes: Paper, Author, Topic, and Venue.…”

Section: Methodsmentioning

confidence: 99%

Discovering Meta-Paths in Large Heterogeneous Information Networks

Meng

Cheng

Maniu

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

107

105

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The Heterogeneous Information Network (HIN) is a graph data model in which nodes and edges are annotated with class and relationship labels. Large and complex datasets, such as Yago or DBLP, can be modeled as HINs. Recent work has studied how to make use of these rich information sources. In particular, meta-paths, which represent sequences of node classes and edge types between two nodes in a HIN, have been proposed for such tasks as information retrieval, decision making, and product recommendation. Current methods assume meta-paths are found by domain experts. However, in a large and complex HIN, retrieving meta-paths manually can be tedious and difficult. We thus study how to discover meta-paths automatically. Specifically, users are asked to provide example pairs of nodes that exhibit high proximity. We then investigate how to generate meta-paths that can best explain the relationship between these node pairs. Since this problem is computationally intractable, we propose a greedy algorithm to select the most relevant meta-paths. We also present a data structure to enable efficient execution of this algorithm. We further incorporate hierarchical relationships among node classes in our solutions. Extensive experiments on real-world HIN show that our approach captures important meta-paths in an efficient and scalable manner.

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Section: Methodsmentioning

confidence: 99%

Discovering Meta-Paths in Large Heterogeneous Information Networks

Meng

Cheng

Maniu

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

107

105

View full text Add to dashboard Cite

show abstract

“…Using the meta-path feature, some works have been done [2,22,23,30] in simple HIN. Sun et al [22] proposed PathPredict to solve the problem of co-author relationship prediction by extracting meta-pathbased feature and building logistic regression-based model.…”

Section: Link Prediction In Hinmentioning

confidence: 99%

“…Utilizing meta-path, a lot of works usually employ a two-step process to solve the link prediction problem in HIN. The first step is to extract meta-path-based feature vectors, and the second step is to train a regression or classification model to compute the existence probability of a link [2,22,23,30]. For example, Sun et al [22] proposed PathPredict to solve the problem of co-author relationship prediction.…”

Section: Introductionmentioning

confidence: 99%

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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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“…We use same definitions and notations as in our previous works [9] and [10]. Network schema for the DBLP and IMDb networks are shown in Figure 2.…”

Section: Problem Definition and Fea-ture Spacementioning

confidence: 99%

User guided entity similarity search using meta-path selection in heterogeneous information networks

Sun

Norick

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

Self Cite

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With the emergence of web-based social and information applications, entity similarity search in information networks, aiming to find entities with high similarity to a given query entity, has gained wide attention. However, due to the diverse semantic meanings in heterogeneous information networks, which contain multi-typed entities and relationships, similarity measurement can be ambiguous without context. In this paper, we investigate entity similarity search and the resulting ambiguity problems in heterogeneous information networks. We propose to use a meta-path-based ranking model ensemble to represent semantic meanings for similarity queries, exploit the possibility of using using user-guidance to understand users query. Experiments on real-world datasets show that our framework significantly outperforms competitor methods.

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Citation Prediction in Heterogeneous Bibliographic Networks

Cited by 101 publications

References 20 publications

Discovering Meta-Paths in Large Heterogeneous Information Networks

Discovering Meta-Paths in Large Heterogeneous Information Networks

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

User guided entity similarity search using meta-path selection in heterogeneous information networks

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