Journal ArticleDOI
PathSim: meta path-based top-K similarity search in heterogeneous information networks
Yizhou Sun,Jiawei Han,Xifeng Yan,Philip S. Yu,Tianyi Wu +4 more
- Vol. 4, Iss: 11, pp 992-1003
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TLDR
Under the meta path framework, a novel similarity measure called PathSim is defined 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.Abstract:
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.read more
Citations
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Discovering Association Rules from Big Graphs
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Meta-Graph Based Attention-Aware Recommendation over Heterogeneous Information Networks
TL;DR: A Meta-Graph based Attention-aware Recommendation (MGAR) over HINs, which utilizes rich meta-graph based latent features to guide the heterogeneous information fusion recommendation and proposes an attention-based feature enhancement model which enables useful features and useless features contribute differently to the prediction, thus improves the performance of the recommendation.
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Recommendation in Context-Rich Environment: An Information Network Analysis Approach
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TL;DR: This tutorial will systematically introduce the methodologies of using heterogeneous information network mining approach to solve recommendation tasks, and demonstrate the effectiveness of such methods using different applications, ranging from collaboration recommendation in scientific research network to job recommendation in professional social network, and to drug discovery in biomedical networks.
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