Predicting anchor links between heterogeneous social networks

Sajadmanesh, Sina; Rabiee, Hamid R.; Khodadadi, Ali

doi:10.1109/asonam.2016.7752228

Cited by 8 publications

(4 citation statements)

References 22 publications

(37 reference statements)

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“…In recent years, newer studies have shifted from traditional link prediction on static and homogeneous networks toward newer domains, considering heterogeneity and dynamicity of networks [10,12,14,25,29]. However, most of these works merely focus on one of these aspects, disregarding the other.…”

Section: Sajadmanesh Et Almentioning

confidence: 99%

“…For example, the co-authorship meta-path A → P ← A in a bibliographic network creates a sense of similarity between two Author nodes. These type of meta-paths, called similarity meta-paths, are widely used to define topological features for link prediction problem in heterogeneous networks [29,33,46]. Table 1 presents a number of similarity meta-paths that can be defined on DBLP, Delicious, and MovieLens networks to capture the heterogeneous similarity between different node types.…”

Section: Dynamic Feature Extractionmentioning

confidence: 99%

“…For example, in online social networks, such as Facebook, new users are joining in the network every day, and new friendship links are being added to the network gradually. This dynamic characteristic causes the structure of the network to change and evolve over time, and taking these changes into account can significantly boost the quality of link prediction task [28].In recent years, newer studies have shifted from traditional link prediction on static and homogeneous networks toward newer domains, considering heterogeneity and dynamicity of networks [10,12,14,25,29]. However, most of these works merely focus on one of these aspects, disregarding the other.…”

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confidence: 99%

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Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Sajadmanesh

Bazargani

Zhang

et al. 2019

ACM Trans. Knowl. Discov. Data

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Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this paper, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (Np-Glm), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train Np-Glm and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-à-vis competitive baselines. Sajadmanesh et al.The problem of link prediction has a long literature and is studied extensively in the last decade. Initial works on link prediction problem mostly concentrated on homogeneous networks, which are composed of single type of nodes connected by links of the same type [21,22,40]. However, many of today's networks, such as online social networks or bibliographic networks, are inherently heterogeneous, in which multiple types of nodes are interconnected using multiple types of links [31,37]. For example, a bibliographic network may contain author, paper, venue, etc. as different node types; and write, publish, cite, and so on as diverse link types that bind nodes with different types to each other. In these heterogeneous networks, the concept of a link can be generalized to a relationship, which can be constructed by combining different links with different types. For instance, the author-cite-paper relationship can be defined in a bibliographic network as a combination of author-write-paper and paper-cite-paper links. Analogously, one can generalize the link prediction to relationship prediction in heterogeneous networks which tries to predict complex relationships instead of links [34].While most of the studies on the link/relationship prediction in heterogeneous networks utilize a static snapshot of the und...

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Section: Sajadmanesh Et Almentioning

confidence: 99%

Section: Dynamic Feature Extractionmentioning

confidence: 99%

mentioning

confidence: 99%

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Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Sajadmanesh

Bazargani

Zhang

et al. 2019

ACM Trans. Knowl. Discov. Data

Self Cite

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“…is will result in sparse and incomplete relationships. And, a number of existing works also use UGCs to user identification [4,20]. ese methods are usually based on posting time, location, writing style, or similarity of content [4].…”

Section: Introductionmentioning

confidence: 99%

User Identification Based on Integrating Multiple User Information across Online Social Networks

Zeng

Tang

Wang

et al. 2021

Security and Communication Networks

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User identification can help us build more comprehensive user information. It has been attracting much attention from academia. Most of the existing works are profile-based user identification and relationship-based user identification. Due to user privacy settings and social network restrictions on user data crawl, user data may be missing or incomplete in real social networks. User data include profiles, user-generated contents (UGCs), and relationships. The features extracted in previous research may be sparse. In order to reduce the impact of the above problems on user identification, we propose a multiple user information user identification framework (MUIUI). Firstly, we develop multiprocess crawlers to obtain the user data from two popular social networks, Twitter and Facebook. Secondly, we use named entity recognition and entity linking to obtain and integrate locations and organizations from profiles and UGCs. We also extract URLs from profiles and UGCs. We apply the locations jointly with the relationships and develop several algorithms to measure the similarity of the display name, all locations, all organizations, location in profile, all URLs, following organizations, and user ID, respectively. Afterward, we propose a fusion classifier machine learning-based user identification method. The results show that the F1 score of MUIUI reaches 86.46% on the dataset. It proves that MUIUI can reduce the impact of user data that are missing or incomplete.

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A Solution to Tweet-Based User Identification Across Online Social Networks

Zhang

Peng

2017

Advanced Data Mining and Applications

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Predicting anchor links between heterogeneous social networks

Cited by 8 publications

References 22 publications

Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

User Identification Based on Integrating Multiple User Information across Online Social Networks

A Solution to Tweet-Based User Identification Across Online Social Networks

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