Discovering Meta-Paths in Large Heterogeneous Information Networks

Meng, Changping; Cheng, Reynold; Maniu, Silviu; Senellart, Pierre; Zhang, Wangda

doi:10.1145/2736277.2741123

Cited by 101 publications

(106 citation statements)

References 27 publications

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“…To find the best set of informative meta-paths, an exhaustive search is required in a search space with size of θ(K L ) which is a NP-Complete problem1012. Thus, as the network schema grows in size, generating all informative meta-paths as well as selecting the best set among them become a nontrivial issue.…”

Section: Resultsmentioning

confidence: 99%

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Mutual information model for link prediction in heterogeneous complex networks

Shakibian

Charkari

2017

Sci Rep

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Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.

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Section: Resultsmentioning

confidence: 99%

“…Automatic discovery of meta-paths in large scale complex networks has been studied in ref. 10. The users are asked to provide some examples of node pairs that exhibit high proximity.…”

mentioning

confidence: 99%

Mutual information model for link prediction in heterogeneous complex networks

Shakibian

Charkari

2017

Sci Rep

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“…More broadly, many networked data are difficult to be modeled with heterogeneous network with a simple network schema. For example, in RDF data, there are so many types of objects and relations, which cannot be described with network schema [181], [78]. Many research problems arise with this kind of schema-rich HINs, for example, management of objects and relations with so many types and automatic generation of meta paths.…”

Section: ) Network Structurementioning

confidence: 99%

“…It is a critical task to automatically extract meta paths in this condition. Recently, Meng et al [181] study how to discover meta paths automatically which can best explain the relationship between node pairs. Another important issue is to automatically determine the weights of meta paths.…”

Section: ) Network Structurementioning

confidence: 99%

“…Along the X-axis, the network structure becomes more complex, and semantics information becomes richer along the Y-axis. For example, RankClus [17] NetClus [20] RankClus [17] CPIH [103] HeteRecom [34] MPBR [125] PIPL [37] GNetMine [91] UGES [59] PathSim [14] HeteSim [13] ComClus [38] HeProjI [80] PathSelClus [15] Evo−NetClus [180] HeteRec [133] DYNALINK [115] TRAIL [111] MNA [40] FSPG [181] Hete−CF [138] SemRec [39] HRank [21] Network structure Semantics exploration PCRW [50] classification similarity measure clustering link prediction recommendation ranking MCD [156] Fig. 12.…”

Section: ) Network Structurementioning

confidence: 99%

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A survey of heterogeneous information network analysis

Shi

Zhang

et al. 2017

IEEE Trans. Knowl. Data Eng.

872

457

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Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions. Index Termsheterogeneous information network, data mining, semi-structural data, meta path

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Formatting biological big data for modern machine learning in drug discovery

Duran‐Frigola

Fernández‐Torras

Bertoni

et al. 2018

WIREs Comput Mol Sci

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Biological data is accumulating at an unprecedented rate, escalating the role of data‐driven methods in computational drug discovery. This scenario is favored by recent advances in machine learning algorithms, which are optimized for huge datasets and consistently beat the predictive performance of previous art, rapidly approaching human expert reasoning. The urge to couple biological data to cutting‐edge machine learning has spurred developments in data integration and knowledge representation, especially in the form of heterogeneous, multiplex and semantically‐rich biological networks. Today, thanks to the propitious rise in knowledge embedding techniques, these large and complex biological networks can be converted to a vector format that suits the majority of machine learning implementations. Here, we explain why this can be particularly transformative for drug discovery where, for decades, customary chemoinformatics methods have employed vector descriptors of compound structures as the standard input of their prediction tasks. A common vector format to represent biology and chemistry may push biological information into most of the existing steps of the drug discovery pipeline, boosting the accuracy of predictions and uncovering connections between small molecules and other biological entities such as targets or diseases. This article is categorized under: Computer and Information Science > Databases and Expert Systems Computer and Information Science > Chemoinformatics

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Discovering Meta-Paths in Large Heterogeneous Information Networks

Cited by 101 publications

References 27 publications

Mutual information model for link prediction in heterogeneous complex networks

Mutual information model for link prediction in heterogeneous complex networks

A survey of heterogeneous information network analysis

Formatting biological big data for modern machine learning in drug discovery

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