text: 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.
  @cite_19: Given two graphs, the aim of graph matching is to find the “best” matching between nodes of one graph and nodes of the other graph. Due to distortions of the data and the complexity of the problem, in some applications, an active and interactive graph algorithm is needed. The active module queries one of the nodes of the graphs and the interactive module receives from an oracle (human or artificial) the node of the other graph that has to be mapped with and considers the new knowledge. We present different active strategies that decide the node to be queried and adapt these strategies to the graph-matching problem.
  @cite_9: Network alignment is the problem of matching the nodes of two graphs, maximizing the similarity of the matched nodes and the edges between them. This problem is encountered in a wide array of applications - from biological networks to social networks to ontologies - where multiple networked data sources need to be integrated. Due to the difficulty of the task, an accurate alignment can rarely be found without human assistance. Thus, it is of great practical importance to develop network alignment algorithms that can optimally leverage experts who are able to provide the correct alignment for a small number of nodes. Yet, only a handful of existing works address this active network alignment setting. The majority of the existing active methods focus on absolute queries ("are nodes @math and @math the same or not?"), whereas we argue that it is generally easier for a human expert to answer relative queries ("which node in the set @math is the most similar to node @math ?"). This paper introduces a novel relative-query strategy, TopMatchings, which can be applied on top of any network alignment method that constructs and solves a bipartite matching problem. Our method identifies the most informative nodes to query based on the top- @math matchings of a bipartite graph. We compare the proposed approach to several commonly-used query strategies and perform experiments on both synthetic and real-world datasets. Our matching-based strategy yields the highest overall performance, outperforming all the baseline methods by more than 15 percentage points in some cases.
  @cite_34: 
summary: Active learning is an effective method for network alignment in the face of lacking labeled links which has been previous studied by @cite_19 @cite_34 . The query strategies proposed by Cort ' e s and Serratosa @cite_19 return a probability matrix for different alignment choices which makes the quantification of network alignment straightforward. However, this kind of strategies totally ignore the existing in online social networks. Therefore, we provide an innovative query strategy considering in . Malmi @cite_9 proposes two relative-query strategies and instead of focusing on absolute-query. However, it may not be less challenging for experts to make comparative judgements in online social networks, because the quantity of cantidates corresponding to one node will be huge.