Mining Knowledge from Interconnected Data: A Heterogeneous Information Network Analysis Approach
Summary: Reframes databases as heterogeneous information networks (HINs) with multi-typed objects and links; leverages semantic types for richer mining beyond homogeneous graphs. Provides a systematic tutorial and techniques for effective and scalable knowledge mining in HINs for data management research. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yizhou Sun
- 2. Jiawei Han
- 3. Xifeng Yan
- 4. Philip S. Yu
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 371 | A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration | 2012 | VLDB | 0.00025389696 |
| 435 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00023260172 |
| 768 | PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks | 2011 | VLDB | 0.00016919065 |
| 2,048 | Graph Cube: On Warehousing and OLAP Multidimensional Networks | 2011 | SIGMOD | 9.6914395e-05 |
| 3,484 | Relation Strength-Aware Clustering of Heterogeneous Information Networks with Incomplete Attributes | 2012 | VLDB | 7.0524417e-05 |
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