scholarly journals Exploiting User Friendship Networks for User Identification across Social Networks

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 110
Author(s):  
Yating Qu ◽  
Ling Xing ◽  
Huahong Ma ◽  
Honghai Wu ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Recommendation System ◽  
User Behavior ◽  
Comprehensive Evaluation ◽  
Behavior Pattern ◽  
Recall Rate ◽  
Friendship Networks ◽  
User Identification ◽  
Identification Process ◽  
Gradient Descent Algorithm

Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user’s multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale–Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.

scholarly journals Exploiting Two-Level Information Entropy across Social Networks for User Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ling Xing ◽  
Kaikai Deng ◽  
Honghai Wu ◽  
Ping Xie ◽  
Mingchuan Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Information Entropy ◽  
Online Social Networks ◽  
User Behavior ◽  
User Profile ◽  
Recall Rate ◽  
Entropy Method ◽  
Stable Marriage ◽  
User Identification ◽  
Level Information

As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, F -measure ( F 1 ), and area under curve (AUC).

scholarly journals A heuristics for HTTP traffic identification in measuring user dissimilarity

2020 ◽  
Vol 2 (1-4) ◽  
pp. 17-28
Author(s):  
Adeyemi R. Ikuesan ◽  
Mazleena Salleh ◽  
Hein S. Venter ◽  
Shukor Abd Razak ◽  
Steven M. Furnell
Keyword(s):  
User Behavior ◽  
Recall Rate ◽  
Learning System ◽  
The Internet ◽  
Complex Nature ◽  
Web Traffic ◽  
User Identification ◽  
E Learning ◽  
Research Findings ◽  
Unit Of Measurement

AbstractThe prevalence of HTTP web traffic on the Internet has long transcended the layer 7 classification, to layers such as layer 5 of the OSI model stack. This coupled with the integration-diversity of other layers and application layer protocols has made identification of user-initiated HTTP web traffic complex, thus increasing user anonymity on the Internet. This study reveals that, with the current complex nature of Internet and HTTP traffic, browser complexity, dynamic web programming structure, the surge in network delay, and unstable user behavior in network interaction, user-initiated requests can be accurately determined. The study utilizes HTTP request method of GET filtering, to develop a heuristic algorithm to identify user-initiated requests. The algorithm was experimentally tested on a group of users, to ascertain the certainty of identifying user-initiated requests. The result demonstrates that user-initiated HTTP requests can be reliably identified with a recall rate at 0.94 and F-measure at 0.969. Additionally, this study extends the paradigm of user identification based on the intrinsic characteristics of users, exhibited in network traffic. The application of these research findings finds relevance in user identification for insider investigation, e-commerce, and e-learning system as well as in network planning and management. Further, the findings from the study are relevant in web usage mining, where user-initiated action comprises the fundamental unit of measurement.

Exploiting similarities of user friendship networks across social networks for user identification

2020 ◽  
Vol 506 ◽  
pp. 78-98 ◽  
Author(s):  
Yongjun Li ◽  
Zhaoting Su ◽  
Jiaqi Yang ◽  
Congjie Gao
Keyword(s):  
Social Networks ◽  
Friendship Networks ◽  
User Identification

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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47114-47123 ◽  
Author(s):  
Kaikai Deng ◽  
Ling Xing ◽  
Longshui Zheng ◽  
Honghai Wu ◽  
Ping Xie ◽  
...  
Keyword(s):  
Social Networks ◽  
Behavior Analysis ◽  
User Behavior ◽  
Identification Algorithm ◽  
User Identification ◽  
User Behavior Analysis

scholarly journals Behavioral Habits-Based User Identification Across Social Networks

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1134 ◽  
Author(s):  
Xing ◽  
Deng ◽  
Wu ◽  
Xie ◽  
Gao
Keyword(s):  
Social Networks ◽  
User Behavior ◽  
Practical Significance ◽  
User Identification ◽  
Online Data ◽  
Matching Problems ◽  
Network Applications ◽  
Matching Process ◽  
User Data ◽  
The One

Social networking is an interactive Internet of Things. The symmetry of the network can reflect the similar friendships of users on different social networks. A user’s behavior habits are not easy to change, and users usually have the same or similar display names and published contents among multiple social networks. Therefore, the symmetry concept can be used to analyze the information generated by the user for user identification. User identification plays a key role in building better information about social network user profiles. As a consequence, it has very important practical significance in many network applications and has attracted a great deal of attention from researchers. However, existing works are primarily focused on rich network data and ignore the difficulty involved in data acquisition. Display names and user-published content are very easy to obtain compared to other types of user data across different social networks. Therefore, this paper proposes an across social networks user identification method based on user behavior habits (ANIUBH). We analyzed the user’s personalized naming habits in terms of display names, then utilized different similarity calculation methods to measure the similarity of the features contained in the display names. The variant entropy value was adopted to assign weights to the features mentioned above. In addition, we also measured and analyzed the user’s interest graph to further improve user identification performance. Finally, we combined one-to-one constraint with the Gale–Shapley algorithm to eliminate the one-to-many and many-to-many account-matching problems that often occur during the results-matching process. Experimental results demonstrated that our proposed method enables the possibility of user identification using only a small amount of online data.

A Situational Analytic Method for User Behavior Pattern in Multimedia Social Networks

2019 ◽  
Vol 5 (4) ◽  
pp. 520-528 ◽  
Author(s):  
Zhiyong Zhang ◽  
Ranran Sun ◽  
Xiaoxue Wang ◽  
Changwei Zhao
Keyword(s):  
Social Networks ◽  
User Behavior ◽  
Behavior Pattern ◽  
Analytic Method ◽  
Multimedia Social Networks

scholarly journals A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rongmei Zhao ◽  
Xi Xiong ◽  
Xia Zu ◽  
Shenggen Ju ◽  
Zhongzhi Li ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation System ◽  
Information Overload ◽  
User Behavior ◽  
Recommendation Systems ◽  
Social Domain ◽  
Network Information ◽  
Cross Domain ◽  
Real World Datasets

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

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