PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks

Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, Tianyi Wu

Research output: Contribution to journalConference articlepeer-review


Similarity search is a primitive operation in database and Web search engines. With the advent of large-scale heterogeneous information networks that consist of multi-typed, interconnected objects, such as the bibliographic networks and social media networks, it is important to study similarity search in such networks. Intuitively, two objects are similar if they are linked by many paths in the network. However, most existing similarity measures are defined for homogeneous networks. Different semantic meanings behind paths are not taken into consideration. Thus they cannot be directly applied to heterogeneous networks. In this paper, we study similarity search that is defined among the same type of objects in heterogeneous networks. Moreover, by considering different linkage paths in a network, one could derive various similarity semantics. Therefore, we introduce the concept of meta path-based similarity, where a meta path is a path consisting of a sequence of relations defined between different object types (i.e., structural paths at the meta level). No matter whether a user would like to explicitly specify a path combination given sufficient domain knowledge, or choose the best path by experimental trials, or simply provide training examples to learn it, meta path forms a common base for a network-based similarity search engine. In particular, under the meta path framework we define a novel similarity measure called PathSim that is able to find peer objects in the network (e.g., find authors in the similar field and with similar reputation), which turns out to be more meaningful in many scenarios compared with random-walk based similarity measures. In order to support fast online query processing for PathSim queries, we develop an efficient solution that partially materializes short meta paths and then concatenates them online to compute top-k results. Experiments on real data sets demonstrate the effectiveness and efficiency of our proposed paradigm.

Original languageEnglish (US)
Pages (from-to)992-1003
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number11
StatePublished - Aug 2011
Event37th International Conference on Very Large Data Bases, VLDB 2011 - Seattle, United States
Duration: Aug 29 2011Sep 3 2011

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science


Dive into the research topics of 'PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks'. Together they form a unique fingerprint.

Cite this