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.
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.
@cite_19 @cite_9 @cite_34
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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. Online social networks have gained great success in recent years and many of them involve multiple kinds of nodes and complex relationships. Among these relationships, social links among users are of great importance. Many existing link prediction methods focus on predicting social links that will appear in the future among all users based upon a snapshot of the social network. In real-world social networks, many new users are joining in the service every day. Predicting links for new users are more important. Different from conventional link prediction problems, link prediction for new users are more challenging due to the following reasons: (1) differences in information distributions between new users and the existing active users (i.e., old users); (2) lack of information from the new users in the network. We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts. We proposed a within-target-network personalized sampling method to process the existing active users' information in order to accommodate the differences in information distributions before the intra-network knowledge transfer. SCAN-PS can also exploit information in other source networks, where the user accounts are aligned with the target network. In this way, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Online social networks have gained great success in recent years. Some online social networks only involving users and social links among users can be represented as homogeneous networks. Meanwhile, some other social networks containing abundant information, which include multiple kinds of nodes and complex relationships, can be denoted as heterogeneous networks. Predicting the missing links or links that will be formed in the future based on a snapshot of social networks is formally defined as the link prediction problem. Link prediction problems have extensive applications in real-world social networks and many concrete social services can be cast as link prediction tasks, e.g., friend and location recommendations can all be solved as the problem of predicting social links among users and the location links between users and locations. Link prediction problems have been an important research topic for many years and a large number of different methods have been proposed so far. This article summaries the existing link prediction methods for both homogeneous and heterogeneous networks, which include various unsupervised link predicators, random walk based link prediction methods, methods based on matrix factorization techniques, supervised link prediction methods and meta paths based link prediction methods. Meanwhile, as proposed in recent works, people are usually involved in multiple social networks simultaneously nowadays and networks sharing common users are formally defined as the aligned networks. In this article, we will also introduce the latest progress of link prediction problems across multiple aligned heterogeneous networks. The link prediction problems across aligned networks can include anchor link prediction problem and link transfer across aligned heterogeneous networks. We will introduce the newly proposed methods to solve these problems in details and, finally, we will conclude this survey with a discussion about the future link prediction research works.