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To enjoy more social network services, users nowadays are usually involved inmultiple online social networks simultaneously. The shared users betweendifferent networks are called anchor users, while the remaining unshared usersare named as non-anchor users. Connections between accounts of anchor users indifferent networks are defined as anchor links and networks partially alignedby anchor links can be represented as partially aligned networks. In thispaper, we want to predict anchor links between partially aligned socialnetworks, which is formally defined as the partial network alignment problem.The partial network alignment problem is very difficult to solve because of thefollowing two challenges: (1) the lack of general features for anchor links,and (2) the "one-to-one$_\le$" (one to at most one) constraint on anchor links.To address these two challenges, a new method PNA (Partial Network Aligner) isproposed in this paper. PNA (1) extracts a set of explicit anchor adjacencyfeatures and latent topological features for anchor links based on the anchormeta path concept and tensor decomposition techniques, and (2) utilizes thegeneric stable matching to identify the non-anchor users to prune the redundantanchor links attached to them. Extensive experiments conducted on tworeal-world partially aligned social networks demonstrate that PNA can solve thepartial network alignment problem very well and outperform all the othercomparison methods with significant advantages.

Partial Network Alignment with Anchor Meta Path and Truncated Generic Stable Matching