Mining Heterogeneous Information Networks Principles And Methodologies Pdf


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mining heterogeneous information networks principles and methodologies pdf

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Mining heterogeneous information networks

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. View via Publisher. Save to Library. Create Alert. Launch Research Feed.

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Mining heterogeneous information networks: the next frontier. Research Feed. View 1 excerpt. View 1 excerpt, cites background. Feature-rich networks: going beyond complex network topologies. Highly Influenced. View 4 excerpts, cites background. Heterogeneous subgraph features for information networks. View 2 excerpts, cites background. Clustering on heterogeneous networks. Heterogeneous Network Mining and Analysis. On the power of big data: Mining structures from massive, unstructured text data.

Query-driven discovery of semantically similar substructures in heterogeneous networks. Ranking-based clustering of heterogeneous information networks with star network schema. Social Network Data Analytics. RankClus: integrating clustering with ranking for heterogeneous information network analysis. Community evolution detection in dynamic heterogeneous information networks.

Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.

KnowSim: A Document Similarity Measure on Structured Heterogeneous Information Networks.

JavaScript is disabled for your browser. Some features of this site may not work without it. This Collection. Login Register. Getting Started About Contact Us. Mining heterogeneous information networks Sun, Yizhou. Use this link to cite this item:.

Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Mining Heterogeneous Information Networks: Principles and Methodologies Abstract: Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge.


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Mining Heterogeneous Information Networks: Principles and Methodologies

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Mining heterogeneous information networks

Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload this page. Free to read. As a fundamental task, document similarity measure has broad impact to document-based classification, clustering and ranking. Traditional approaches represent documents as bag-of-words and compute document similarities using measures like cosine, Jaccard, and dice. However, entity phrases rather than single words in documents can be critical for evaluating document relatedness.

You can check for personal access by clicking on the DOI link. Advanced Search. Browse by Subject. ACM Books. Society for Experimental Mechanics Books. Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks.

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Mining Heterogeneous Information Networks: Principles and Methodologies. Abstract: Real-world physical and abstract data objects are interconnected, forming.


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as heterogeneous information networks, study how to lever- age the rich semantic meaning approach on mining semi-structured, multi-typed heteroge- neous information ferent types, and investigate the principles and methodolo- gies for.

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This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: .

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