Meta Diagram Based Active Social Networks Alignment

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 instances, 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(Active Iterative Alignment), is introduced in this paper. The model 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 were performed on a real-world aligned networks dataset, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.

Adaptive Network Alignment with Unsupervised and Multi-order Convolutional Networks

“…(R3) Unsupervised: Many existing alignment techniques require large training data. However, such ground-truth information is rarely available in real-world networks due to high labelling costs [33]. For instance, checking users' background and pairing their accounts manually in social networks is very time-consuming.…”

“…Another problem is network reconciliation, where two different networks are connected for data integration [25,30]. Our work solves the recent network alignment problem, which finds anchor links between an original network and its variants [2,33,36,38].…”

“…Networks, aka graphs, are structures that naturally capture relations between entities in data domains and information systems. Many applications, however, analyse not one, but multiple networks, e.g., for data discovery [12,26], social network analytics [33], knowledge graph reconciliation [50], and pattern matching in protein networks [29]. Network alignment, the task of pairing nodes between two isomorphic or nearisomorphic networks such that the paired nodes are similar w.r.t.…”

“…While different types of information may be useful for network alignment, the lack of a common modality imposes challenges [37]. Information integration may be guided by path-based constraints and proximity rules [33]. Yet, such approaches are domain-specific and require manual user efforts.…”

Scalable Heterogeneous Social Network Alignment through Synergistic Graph Partition

“…To effectively capture the diverse connections among users within and across networks with heterogeneous information, SHNA employs a group of meta diagrams in this paper. Meta diagram is a novel concept proposed in [16], which includes both meta path and more complex meta structures to outline the user correlations both within and across heterogeneous networks . As a scalable and general solution, SHNA addresses the social network alignment problem via two stages: network synergistic partition and parallel sub-network alignment.…”