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

TL;DR

This paper introduces PNA, a novel method for predicting anchor links between partially aligned social networks by leveraging anchor meta paths, tensor decomposition, and stable matching, effectively addressing key challenges in partial network alignment.

Contribution

The paper proposes PNA, a new approach combining explicit features, latent topological features, and stable matching to improve partial network alignment accuracy.

Findings

PNA outperforms existing methods significantly in experiments.

The method effectively handles the lack of general features and the one-to-one constraint.

Experiments on real-world networks validate the approach's effectiveness.

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-one$_\le$" (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 a set of explicit anchor adjacency features and latent topological features for anchor links based on the anchor meta path concept and tensor decomposition techniques, 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.