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Mining heterogeneous information networks : principles and methodologies

Author: Yizhou Sun; Jiawei Han
Publisher: [San Rafael, Calif.] : Morgan & Claypool Publishers, ©2012.
Series: Synthesis lectures on data mining and knowledge discovery, #5.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Sun, Yizhou.
Mining on heterogeneous information networks.
San Rafael : Morgan & Claypool, 2012
(OCoLC)785081661
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Yizhou Sun; Jiawei Han
ISBN: 9781608458813 1608458814
OCLC Number: 801595907
Description: 1 online resource (xi, 147 pages) : illustrations (some color)
Contents: 1. Introduction --
1.1 What are heterogeneous information networks? --
1.2 Why is mining heterogeneous networks a new game? --
1.3 Organization of the book. Part I. Ranking-based clustering and classification --
2. Ranking-based clustering --
2.1 Overview --
2.2 RankClus --
2.2.1 Ranking functions --
2.2.2 From conditional rank distributions to new clustering measures --
2.2.3 Cluster centers and distance measure --
2.2.4 RankClus: algorithm summarization --
2.2.5 Experimental results --
2.3 NetClus --
2.3.1 Ranking functions --
2.3.2 Framework of NetClus algorithm --
2.3.3 Generative model for target objects in a net-cluster --
2.3.4 Posterior probability for target objects and attribute objects --
2.3.5 Experimental results. 3. Classification of heterogeneous information networks / Ming Ji --
3.1 Overview --
3.2 GNetMine --
3.2.1 The classification problem definition --
3.2.2 Graph-based regularization framework --
3.3 RankClass --
3.3.1 The framework of RankClass --
3.3.2 Graph-based ranking --
3.3.3 Adjusting the network --
3.3.4 Posterior probability calculation --
3.4 Experimental results --
3.4.1 Dataset --
3.4.2 Accuracy study --
3.4.3 Case study --
Part II. Meta-path-based similarity search and mining. Part II. Meta-path-based similarity search and mining --
4. Meta-path-based similarity search --
4.1 Overview --
4.2 PathSim: a meta-path-based similarity measure --
4.2.1 Network schema and meta-path --
4.2.2 Meta-path-based similarity framework --
4.2.3 PathSim: a novel similarity measure --
4.3 Online query processing for single meta-path --
4.3.1 Single meta-path concatenation --
4.3.2 Baseline --
4.3.3 Co-clustering-based pruning --
4.4 Multiple meta-paths combination --
4.5 Experimental results --
4.5.1 Effectiveness --
4.5.2 Efficiency comparison --
4.5.3 Case-study on Flickr network. 5. Meta-path-based relationship prediction --
5.1 Overview --
5.2 Meta-path-based relationship prediction framework --
5.2.1 Meta-path-based topological feature space --
5.2.2 Supervised relationship prediction framework --
5.3 Co-authorship prediction --
5.3.1 The co-authorship prediction model --
5.3.2 Experimental results --
5.4 Relationship prediction with time --
5.4.1 Meta-path-based topological features for author citation relationship prediction --
5.4.2 The relationship building time prediction model --
5.4.3 Experimental results. Part III. Relation strength-aware mining --
6. Relation strength-aware clustering with incomplete attributes --
6.1 Overview --
6.2 The relation strength-aware clustering problem definition --
6.2.1 The clustering problem --
6.3 The clustering framework --
6.3.1 Model overview --
6.3.2 Modeling attribute generation --
6.3.3 Modeling structural consistency --
6.3.4 The unified model --
6.4 The clustering algorithm --
6.4.1 Cluster optimization --
6.4.2 Link type strength learning --
6.4.3 Putting together: the GenClus algorithm --
6.5 Experimental results --
6.5.1 Datasets --
6.5.2 Effectiveness study. 7. User-guided clustering via meta-path selection --
7.1 Overview --
7.2 The meta-path selection problem for user-guided clustering --
7.2.1 The meta-path selection problem --
7.2.2 User-guided clustering --
7.2.3 The problem definition --
7.3 The probabilistic model --
7.3.1 Modeling the relationship generation --
7.3.2 Modeling the guidance from users --
7.3.3 Modeling the quality weights for meta-path selection --
7.3.4 The unified model --
7.4 The learning algorithm --
7.4.1 Optimize clustering result given meta-path weights --
7.4.2 Optimize meta-path weights given clustering result --
7.4.3 The PathSelClus algorithm --
7.5 Experimental results --
7.5.1 Datasets --
7.5.2 Effectiveness study --
7.5.3 Case study on meta-path weights --
7.6 Discussions. 8. Research frontiers --
Bibliography --
Authors' biographies.
Series Title: Synthesis lectures on data mining and knowledge discovery, #5.
Responsibility: Yizhou Sun and Jiawei Han.
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Abstract:

Investigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or  Read more...

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