Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

Tang, Rui; Jiang, Shuyu; Chen, Xingshu; Wang, Haizhou; Wang, Wenxian; Wang, Wei

doi:10.1016/j.knosys.2020.105598

Cited by 45 publications

(13 citation statements)

References 59 publications

(92 reference statements)

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“…Zhou et al [28] proposed a friend-relationship-based user identification (FRUI) algorithm that counts the number of shared friends to calculate the degree of match for all candidate-matched node pairs and chooses pairs that have the maximum value as the final set of matched pairs. Tang et al [15] further investigated the importance of the scale-free property of real-world SMNs for accomplishing interlayer link prediction and proposed a degree penalty principle to calculate the degree of match of all unmatched node pairs. Ren et al [55] defined a set of meta-diagrams for feature extraction and used greedy link selection for the interlayer link prediction.…”

Section: Non-embedding-based Methodsmentioning

confidence: 99%

“…The goal of interlayer link prediction is to leverage feature or structure information to determine whether accounts across different SMNs belong to the same user [15]; this is a challenging task in multiplex network analysis. It is also known as anchor link prediction [16,17], network alignment [18,19,20,21,22], user identification [23], and user identity linkage [21].…”

Section: Introductionmentioning

confidence: 99%

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Interlayer Link Prediction in Multiplex Social Networks Based on Multiple Types of Consistency between Embedding Vectors

Tang¹,

Miao²,

Jiang³

et al. 2020

Preprint

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Online users are typically active on multiple social media networks (SMNs), which constitute a multiplex social network. With improvements in cybersecurity awareness, users increasingly choose different usernames and provide different profiles on different SMNs. Thus, it is becoming increasingly challenging to determine whether given accounts on different SMNs belong to the same user; this can be expressed as an interlayer link prediction problem in a multiplex network. To address the challenge of predicting interlayer links , feature or structure information is leveraged. Existing methods that use network embedding techniques to address this problem focus on learning a mapping function to unify all nodes into a common latent representation space for prediction; positional relationships between unmatched nodes and their common matched neighbors (CMNs) are not utilized. Furthermore, the layers are often modeled as unweighted graphs, ignoring the strengths of the relationships between nodes. To address these limitations, we propose a framework based on multiple types of consistency between embedding vectors (MulCEV). In MulCEV, the traditional embedding-based method is applied to obtain the degree of consistency between the vectors representing the unmatched nodes, and a proposed distance consistency index based on the positions of nodes in each latent space provides additional clues for

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Section: Non-embedding-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interlayer Link Prediction in Multiplex Social Networks Based on Multiple Types of Consistency between Embedding Vectors

Tang¹,

Miao²,

Jiang³

et al. 2020

Preprint

Self Cite

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show abstract

“…Different evaluation criteria such as Precision, Recall and F-measure are used to confirm the performance of the proposed method [29]. Precision is defined as the ratio of the number of correct users suggested (𝑇𝑜𝑝𝐾) to the total number of users suggested.…”

Section: Evaluation Criteriamentioning

confidence: 99%

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Yang

Peng

et al. 2022

Concurrency and Computation

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Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user' on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

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“…The online social network is characterized by a complex network in which users are represented as nodes and the follower-followee information as edges [45,46,47]. We construct a directed social graph.…”

Section: Social Graph Buildingmentioning

confidence: 99%

Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks

Miao¹,

Chen²,

Wang³

et al. 2022

Preprint

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The pervasiveness of offensive language on the social network has caused adverse effects on society, such as abusive behavior online. It is urgent to detect offensive language and curb its spread. Existing research shows that methods with community structure features effectively improve the performance of offensive language detection. However, the existing models deal with community structure independently, which seriously affects the effectiveness of detection models. In this paper, we propose an end-to-end method based on community structure and text features for offensive language detection (CT-OLD). Specifically, the community structure features are directly captured by the graph attention network layer, and the text embeddings are taken from the last hidden layer of BERT. Attention mechanisms and position encoding are used to fuse these features. Meanwhile, we add user opinion to the community structure for representing user features. The user opinion is represented by user historical behavior information, which outperforms that represented by text information. Besides the above point, the distribution of users and tweets is unbalanced in the popular datasets, which limits the generalization ability of the model. To address this issue, we construct and release a dataset with reasonable user distribution. Our method outperforms baselines with the F1 score of 89.94%. The results show that the end-toend model effectively learns the potential information of community structure and text, and user historical behavior information is more suitable for user opinion representation.

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Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

Abstract: structure-based methods when the multiplex network average degree and node overlapping rate are low.

Cited by 45 publications

References 59 publications

Interlayer Link Prediction in Multiplex Social Networks Based on Multiple Types of Consistency between Embedding Vectors

Interlayer Link Prediction in Multiplex Social Networks Based on Multiple Types of Consistency between Embedding Vectors

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks

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