# Meta Diagram based Active Social Networks Alignment

## TL;DR

This paper introduces ActiveIter, a novel model for aligning online social networks by leveraging meta diagrams, active learning, and greedy link selection to address data scarcity, heterogeneity, and one-to-one constraints, demonstrating superior performance.

## Contribution

The paper proposes ActiveIter, a new network alignment model that effectively handles heterogeneity, limited training data, and one-to-one constraints in social network alignment.

## Key findings

- ActiveIter outperforms baseline methods in real-world datasets.
- Meta diagrams improve feature extraction for heterogeneous networks.
- ActiveIter effectively reduces manual labeling effort.

## Abstract

Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training data, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter, is introduced in this paper. ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments are conducted on real-world aligned networks datasets, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.