4.6 Article

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Automation & Control Systems

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

Rui Tang et al.

Summary: Researchers propose a framework based on multiple types of consistency to predict links between different layers in a multiplex social network. The framework leverages the consistency between embedding vectors and the positional relationships of nodes in latent spaces, modeling layers as weighted graphs. Experimental results demonstrate that the framework achieves high accuracy in link prediction.

IEEE TRANSACTIONS ON CYBERNETICS (2023)

Article Computer Science, Theory & Methods

Community Detection in Multiplex Networks

Matteo Magnani et al.

Summary: This article provides a taxonomy of community detection algorithms in multiplex networks and conducts an extensive experimental evaluation to answer three main questions, aiming to assist scholars and practitioners in choosing the appropriate methods for their data and tasks.

ACM COMPUTING SURVEYS (2022)

Article Multidisciplinary Sciences

Exploiting User Friendship Networks for User Identification across Social Networks

Yating Qu et al.

Summary: This study proposes a friendship networks-based user identification algorithm across social networks, which identifies users by comparing the similarity of their multi-hop neighbor nodes, and optimizes the process using gradient descent algorithm and Gale-Shapley matching algorithm. Experimental results show that the algorithm achieves higher precision, recall rate, and comprehensive evaluation index in user identification.

SYMMETRY-BASEL (2022)

Article Physics, Multidisciplinary

A Multiple Salient Features-Based User Identification across Social Media

Yating Qu et al.

Summary: This research proposes a multi-feature user identification method across social media, which can effectively improve the accuracy and recall rate of user identification.

ENTROPY (2022)

Article Computer Science, Information Systems

MAUIL: Multilevel attribute embedding for semisupervised user identity linkage

Baiyang Chen et al.

Summary: User identity linkage across social networks has practical value and research challenges. Existing methods have limitations in covering all attribute features and capturing higher-level semantic features. This paper proposes a novel semi-supervised model, MAUIL, to address these challenges using multi-level attribute embedding and regularized canonical correlation analysis.

INFORMATION SCIENCES (2022)

Article Computer Science, Artificial Intelligence

Network structural perturbation against interlayer link prediction

Rui Tang et al.

Summary: Interlayer link prediction aims to match the same entities across different layers of the multiplex network. Existing studies focus on predicting from aspects of network structure, attribute characteristics, etc., with few analyzing the effects of intralayer links. This research proposes two network structural perturbation methods and finds that the intralayer links connected with small degree nodes have the most significant impact on the prediction accuracy.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Mathematics

Network Alignment across Social Networks Using Multiple Embedding Techniques

Van-Vang Le et al.

Summary: Network alignment, also known as user identity linkage, is a significant research direction in social network analysis that predicts overlapping users between two different social networks. This research has proposed a method that combines different embedding techniques to address the network alignment problem.

MATHEMATICS (2022)

Article Computer Science, Artificial Intelligence

JORA: Weakly Supervised User Identity Linkage via Jointly Learning to Represent and Align

Conghui Zheng et al.

Summary: User identity linkage is a fundamental issue in various social network applications. Existing studies often face conflicts between objectives and difficulties in defining similarities. To address these challenges, we propose a model called JORA that jointly learns representations and alignment, inferring user correspondence and reducing error propagation through adaptive similarity learning.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Computer Science, Information Systems

Link Prediction Model for Opportunistic Networks Based on Feature Fusion

Jian Shu et al.

Summary: Link prediction is a hot issue in network evolution research. Existing methods using stacked structures introduce network noise and lower prediction accuracy. In this study, a link prediction model based on deep learning and attentional feature fusion is proposed, which automatically extracts network features and has been demonstrated to be accurate and stable in experiments.

IEEE ACCESS (2022)

Proceedings Paper Computer Science, Theory & Methods

A Real-Time Detection Algorithm for Abnormal Users in Multi Relationship Social Networks Based on Deep Neural Network

Ai-ping Zhang et al.

Summary: In order to address the imbalance of abnormal data in social networks, this paper proposes a real-time anomaly detection algorithm based on deep neural networks. By establishing a multi-relationship social gathering model and using random forest for tag data processing, a set of suspicious network abnormal nodes is successfully created. By utilizing the wavelet transform square integral for handling the abnormal data acquisition process, a real-time detection algorithm is constructed, which exhibits the largest bandwidth and best performance according to experimental results.

ADVANCED HYBRID INFORMATION PROCESSING, PT I (2022)

Article Computer Science, Artificial Intelligence

ASRNN: A recurrent neural network with an attention model for sequence labeling

Jerry Chun-Wei Lin et al.

Summary: The paper introduces an ASRNN model based on a hierarchical attention neural semi-CRF for sequence labeling tasks, which integrates information from different levels through a hierarchical structure and attention mechanism, leading to improved performance.

KNOWLEDGE-BASED SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

Building trust/distrust relationships on signed social service network through privacy-aware link prediction process

Huaizhen Kou et al.

Summary: This paper proposes a link prediction method using Simhash technology to protect user privacy, which has been proven through theoretical analysis and experimental validation to have advantages in overcoming the sparsity problem in social circle expansion.

APPLIED SOFT COMPUTING (2021)

Article Computer Science, Artificial Intelligence

Self-attention-based conditional random fields latent variables model for sequence labeling

Yinan Shao et al.

Summary: To process data like text and speech, Natural Language Processing (NLP) is a valuable tool. Sequence labeling is a vital part of NLP through techniques like text classification, machine translation, and sentiment analysis. Two novel frameworks, SA-CRFLV-I and SA-CRFLV-II, using latent variables within random fields show better performance in terms of well-known metrics compared to 4 well-known sequence prediction methodologies.

PATTERN RECOGNITION LETTERS (2021)

Article Computer Science, Artificial Intelligence

Uncertainty-aware network alignment

Fan Zhou et al.

Summary: This study proposes a novel framework UANA, which embeds nodes as Gaussian distributions instead of point vectors to capture uncertainty and address limitations in existing works. An adversarial learning paradigm is introduced to tackle the P2P matching constraint. Interpretability methods are included to explain aligning results and the effects of individual training samples on NA performance.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021)

Article Computer Science, Information Systems

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

Wenjing Zeng et al.

Summary: User identification is crucial for building comprehensive user information and has received significant attention from academia. Due to privacy settings and restrictions on data crawl in social networks, user data in real social networks may be incomplete or missing. Various algorithms and techniques are used in research to enhance the accuracy of user identification.

SECURITY AND COMMUNICATION NETWORKS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Socially-Aware Self-Supervised Tri-Training for Recommendation

Junliang Yu et al.

Summary: The paper introduces a general socially-aware SSL framework with tri-training that can extract self-supervision signals from user social information, improving the performance of recommendation systems.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Debiasing Learning based Cross-domain Recommendation

Siqing Li et al.

Summary: This paper addresses the issue of domain biases in cross-domain recommendation and proposes a debiasing learning based framework with causal embedding. By introducing a novel IPS estimator and restrictions for propensity score learning, it effectively mitigates the impact of domain biases on user preferences in cross-domain scenarios. Extensive experiments on public and industry datasets demonstrate the effectiveness of the proposed framework.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

DBUL: A User Identity Linkage Method across Social Networks Based on Spatiotemporal Data

Hui Xue et al.

Summary: This study proposes a method DBUL based on DBSCAN clustering to solve the user identity linkage problem based on spatiotemporal data in social networks, representing user identities as cluster centers and linking them by calculating similarities. Experimental results show that this method outperforms other state-of-the-art methods in terms of effectiveness and efficiency.

2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021) (2021)

Proceedings Paper Computer Science, Information Systems

Adversarial-Enhanced Hybrid Graph Network for User Identity Linkage

Xiaolin Chen et al.

Summary: This work investigates the user identity linkage task across different social media platforms using heterogeneous multi-modal posts and social connections. The proposed adversarial-enhanced hybrid graph network addresses the challenges of accurate user representation learning and mitigating the semantic gap issue. Extensive experiments validate the effectiveness of the network, with accompanying dataset, codes, and parameters released for research community use.

SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (2021)

Proceedings Paper Computer Science, Theory & Methods

User Naming Conventions Mapping Learning for Social Network Alignment

Zhao Yuan et al.

Summary: This study proposes a social network alignment method based on BP neural network, which compares the similarity of usernames through vector mapping. The proposed model improves alignment accuracy by 4% compared to the benchmark method, demonstrating higher precision and faster convergence with smaller training set ratio and less training time.

2021 THE 13TH INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2021) (2021)

Article Computer Science, Hardware & Architecture

A Two-Stagse Approach for Social Identity Linkage Based on an Enhanced Weighted Graph Model

Tao Qin et al.

MOBILE NETWORKS & APPLICATIONS (2020)

Article Computer Science, Information Systems

Learning social representations with deep autoencoder for recommender system

Yiteng Pan et al.

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Enhanced sequence labeling based on latent variable conditional random fields

Jerry Chun-Wei Lin et al.

NEUROCOMPUTING (2020)

Article Computer Science, Artificial Intelligence

A reliable cross-site user generated content modeling method based on topic model

Baoxi Liu et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Proceedings Paper Computer Science, Cybernetics

Display Name-Based Anchor User Identification across Chinese Social Networks

Yao Li et al.

2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) (2020)

Article Computer Science, Information Systems

Network Representation Learning: A Survey

Daokun Zhang et al.

IEEE TRANSACTIONS ON BIG DATA (2020)

Article Computer Science, Information Systems

An unsupervised user identification algorithm using network embedding and scalable nearest neighbour

Xiaoping Zhou et al.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2019)

Article Computer Science, Information Systems

BILU-NEMH: A BILU neural-encoded mention hypergraph for mention extraction

Jerry Chun-Wei Lin et al.

INFORMATION SCIENCES (2019)

Article Multidisciplinary Sciences

Behavioral Habits-Based User Identification Across Social Networks

Ling Xing et al.

SYMMETRY-BASEL (2019)

Article Automation & Control Systems

A Bi-LSTM mention hypergraph model with encoding schema for mention extraction

Jerry Chun-Wei Lin et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)

Proceedings Paper Computer Science, Information Systems

Capturing Deep Dynamic Information for Mapping Users across Social Networks

Chiyu Cai et al.

2019 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI) (2019)

Proceedings Paper Computer Science, Theory & Methods

Learning Binary Hash Codes for Fast Anchor Link Retrieval across Networks

Yongqing Wang et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Proceedings Paper Computer Science, Theory & Methods

DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data

Jie Feng et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Article Computer Science, Information Systems

A User Identification Algorithm Based on User Behavior Analysis in Social Networks

Kaikai Deng et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

Improved Flower Pollination Algorithm and Its Application in User Identification Across Social Networks

Wenjing Li et al.

IEEE ACCESS (2019)

Article Computer Science, Artificial Intelligence

Structure Based User Identification across Social Networks

Xiaoping Zhou et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2018)

Article Computer Science, Information Systems

Inferring Anchor Links Based on Social Network Structure

Shuo Feng et al.

IEEE ACCESS (2018)

Proceedings Paper Computer Science, Information Systems

LINKSOCIAL: Linking User Profiles Across Multiple Social Media Platforms

Vishal Sharma et al.

2018 9TH IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (ICBK) (2018)

Proceedings Paper Computer Science, Theory & Methods

A Fusion Information Embedding Method for User Identity Matching across Social Networks

Yizhuo Yang et al.

2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) (2018)

Proceedings Paper Computer Science, Information Systems

Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation

Junliang Yu et al.

CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT (2018)

Article Computer Science, Information Systems

User Identification Based on Display Names Across Online Social Networks

Yongjun Li et al.

IEEE ACCESS (2017)

Proceedings Paper Automation & Control Systems

Identifying i-bridge across online social networks

Amina Amara et al.

2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA) (2017)