Back to Search
Start Over
Heterogeneous Network Crawling: Reaching Target Nodes by Motif-Guided Navigation.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Sep2022, Vol. 34 Issue 9, p4285-4297. 13p. - Publication Year :
- 2022
-
Abstract
- With numerous nodes on online heterogeneous networks, how to reach and extract target nodes of our specific interests is a pressing problem. In this paper, we propose a novel heterogeneous network crawler, MCrawl. It addresses the problem via iterative online heterogeneous network crawling by navigating its available APIs, starting from a set of target nodes, i.e., seed nodes. We are facing two challenges towards addressing the problem. First, to navigate within a vast network, how do we start from a small set of target nodes? In other words, which nodes in the “current frontier” and which direction shall we expand, to reach promising target nodes quickly? We propose motif-based crawling to exploit the complex structures and rich semantics of heterogeneous networks. Second, in many scenarios, we do not have a classifier to assess the quality of the harvested nodes and thus the motifs to expand. We develop a probabilistic inference framework to estimate the yield and harvest rates of motifs, achieving principled bootstrapping for crawling. Our experiment on real networks of MCrawl achieves significant margins over baselines. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NAVIGATION
*INFERENCE (Logic)
*SOCIAL networks
*TASK analysis
*SEMANTICS
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 34
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 158405969
- Full Text :
- https://doi.org/10.1109/TKDE.2020.3038458