Paper Info
Title
PNA: Partial Network Alignment with Generic Stable Matching
Abstract
To enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. The shared users between different networks are called anchor users, while the remaining unshared users are named as non-anchor users. Connections between accounts of anchor users in different networks are defined as anchor links and networks partially aligned by anchor links can be represented as partially aligned networks. In this paper, we want to predict anchor links between partially aligned social networks, which is formally defined as the partial network alignment problem. The partial network alignment problem is very difficult to solve because of the following two challenges: (1) the lack of general features for anchor links, and (2) the one to at most one constraint on anchor links. To address these two challenges, a new method PNA (Partial Network Aligner) is proposed in this paper. PNA (1) extracts various adjacency scores among users across networks based on a set of inter-network anchor meta paths, and (2) utilizes the generic stable matching to identify the non-anchor users to prune the redundant anchor links attached to them. Extensive experiments conducted on two real-world partially aligned social networks demonstrate that PNA can solve the partial network alignment problem very well and outperform all the other comparison methods with significant advantages.
Year
DOI
Venue
2015
10.1109/IRI.2015.34
Information Reuse and Integration
Keywords
Field
DocType
Partial Network Alignment, Multiple Heterogeneous Social Networks, Data Mining
Adjacency list,Data mining,Social network,Computer science,Network alignment,Feature extraction,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
10
0.48
24
Authors
5
Name
Order
Citations
PageRank
Jiawei Zhang180672.17
Weixiang Shao21367.28
senzhang wang3504.88
Xiangnan Kong4105957.66
Philip S. Yu5306703474.16