RLINK: Deep reinforcement learning for user identity linkage

Li, Xiaoxue; Cao, Yanan; Li, Qian; Shang, Yanmin; Li, Yangxi; Liu, Yanbing; Xu, Guandong

doi:10.1007/s11280-020-00833-8

Cited by 23 publications

(3 citation statements)

References 37 publications

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“…The authors of [31] proposed an efficient two-stage algorithm called NR-GL, which was a unified framework for aggregating global and local features together in order to reconcile network alignment. The authors of [32] proposed a reinforcement learning model named RLINK to optimize the linkage strategy from a global perspective. This method transformed the network alignment problem into a sequence decision problem and made full use of both the social network structure and the pre-aligned identities to make predictions.…”

Section: Related Workmentioning

confidence: 99%

Network Alignment across Social Networks Using Multiple Embedding Techniques

Toai

Nguyen

et al. 2022

Mathematics

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Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.

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Section: Related Workmentioning

confidence: 99%

Network Alignment across Social Networks Using Multiple Embedding Techniques

Toai

Nguyen

et al. 2022

Mathematics

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“…Existing UA methods can be roughly classified into three categories: supervised, semi-supervised, and unsupervised approaches. Most research is supervised, where the main idea is training a ranking model or a binary classifier to find potential identical users by using pre-matched user pairs as guidance [3,6,[19][20][21][22][23][24][25][26][27][28][29]. For example, Man et al [22] proposed an anchor link prediction method PALE for preserving significant structural regularities of networks with an awareness of supervised pre-aligned users.…”

Section: Related Workmentioning

confidence: 99%

“…Fu et al [28] exploited the higherorder structural properties and alignment-oriented structural consistency to learn a unified graph embedding method (MGGE), which aimed to learn feature vectors of the graph. A recent study by Li et al [29] considered UA as a sequential decision problem and proposed a reinforcement learning model to align users from a global perspective.…”

Section: Related Workmentioning

confidence: 99%

JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks

Yang

Chen²

2022

Applied Sciences

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User alignment (UA), a central issue for social network analysis, aims to recognize the same natural persons across different social networks. Existing studies mainly focus on the positive effects of incorporating user attributes and network structure on UA. However, there have been few in-depth studies into the existing challenges for the joint integration of different types of text attributes, the imbalance between user attributes and network structure, and the utilization of massive unidentified users. To this end, this paper presents a high-accuracy embedding model named Joint embedding of Attributes and Relations for User Alignment (JARUA), to tackle the UA problem. First, a mechanism that can automatically identify the granularity of user attributes is introduced for handling multi-type user attributes. Second, a graph attention network is employed to extract the structural features and is integrated with user attributes features. Finally, an iterative training algorithm with quality filters is introduced to bootstrap the model performances. We evaluate JARUA on two real-world data sets. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art approaches.

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DualLink: Dual Domain Adaptation for User Identity Linkage Across Social Networks

Kou

Wang

et al. 2021

Web Information Systems and Applications

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RLINK: Deep reinforcement learning for user identity linkage

Cited by 23 publications

References 37 publications

Network Alignment across Social Networks Using Multiple Embedding Techniques

Network Alignment across Social Networks Using Multiple Embedding Techniques

JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks

DualLink: Dual Domain Adaptation for User Identity Linkage Across Social Networks

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