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Title: Ranking on Network of Heterogeneous Information Networks
Ranking on networks plays an important role in many high-impact applications, including recommender systems, social network analysis, bioinformatics and many more. In the age of big data, a recent trend is to address the variety aspect of network ranking. Among others, two representative lines of research include (1) heterogeneous information network with different types of nodes and edges, and (2) network of networks with edges at different resolutions. In this paper, we propose a new network model named Network of Heterogeneous Information Networks (NeoHIN for short) that is capable of simultaneously modeling both different types of nodes/edges, and different edge resolutions. We further propose two new ranking algorithms on NeoHIN based on the cross-domain consistency principle. Experiments on synthetic and real-world networks show that our proposed algorithms are (1) effective, which outperform other existing methods, and (2) efficient, without additional time cost per iteration to their counterparts.  more » « less
Award ID(s):
1939725 1947135
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
IEEE BigData
Page Range / eLocation ID:
848 to 857
Medium: X
Sponsoring Org:
National Science Foundation
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