More Accurate Inference of User Profiles in Online Social Networks

2015 ◽  
pp. 533-546
Author(s):  
Raïssa Yapan Dougnon ◽  
Philippe Fournier-Viger ◽  
Jerry Chun-Wei Lin ◽  
Roger Nkambou
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Profiles

scholarly journals Enhanced Techniques for Filtering of Wall Messages over Online Social Networks (OSN) User Profiles

2015 ◽  
Vol 5 (4) ◽  
pp. 47-61 ◽  
Author(s):  
Nikhil Sanyog Choudhary ◽  
◽  
Himanshu Yadav ◽  
Anurag Jain
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Profiles

scholarly journals Adaptive method of similarity detection of user profiles on online social networks

2019 ◽  
Vol 68 (2) ◽  
pp. 43-57
Author(s):  
Michał Zabielski ◽  
Zbigniew Tarapata ◽  
Rafał Kasprzyk
Keyword(s):  
Social Networks ◽  
Network Theory ◽  
Online Social Networks ◽  
User Profile ◽  
Adaptive Method ◽  
User Profiles ◽  
Simulation Environment ◽  
Similarity Detection ◽  
The Web

The paper presents a method, based on graph and network theory, which allows to detect cloned user profiles on Online Social Networks. Moreover, an idea of similarity containers, which gives an opportunity to incorporate importance and context of data into a model, was introduced. The presented solutions were adapted to the idea of simulation environment, which will allow to detect a profile cloning process before that activity will be completely performed by an attacker. Keywords: Online Social Networks, user profile cloning, violation of privacy on the web.

Identification of Influential Online Social Network Users Based on Multi-Features

2016 ◽  
Vol 30 (06) ◽  
pp. 1659015 ◽  
Author(s):  
Qindong Sun ◽  
Nan Wang ◽  
Yadong Zhou ◽  
Zuomin Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Online Social Network ◽  
Real Data ◽  
User Profiles ◽  
Sina Weibo ◽  
Pagerank Algorithm ◽  
Model Based ◽  
User Influence ◽  
Influential Users

The problem of discovering influential users is important to understand and analyze online social networks. The user profiles and interactions between users are significant features to evaluate the user influence. As these features are heterogeneous, it is challengeable to take all of them into a proper model for influence evaluation. In this paper, we propose a model based on personal user features and the adjacent factor to discover influential users in online social networks. Through taking the advantages of Bayesian network and chain principle of PageRank algorithm, the features of the user profiles and interactions are integratedly considered in our model. Based on real data from Sina Weibo data and multiple evaluation metrics of retweet count, tweet count, follower count, etc., the experimental results show that influential users identified by our model are more powerful than the ones identified by single indicator methods and PageRank-based methods.

Cross-Platform User Profile Matching in Online Social Networks

2013 ◽  
Vol 380-384 ◽  
pp. 1955-1958 ◽  
Author(s):  
Dong Liu ◽  
Quan Yuan Wu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Similarity Measures ◽  
User Profile ◽  
Future Research ◽  
User Profiles ◽  
String Similarity ◽  
Profile Matching ◽  
Cross Platform ◽  
String Similarity Measures

Nowadays, it is common that people have several identities in different online social networks where their identities information is stored as user profiles. Matching cross-platform user profiles becomes a spotlight in the future research. In the paper, we propose a profile matching framework. Depending on the format of each field, different string similarity measures are adopted. Meanwhile, each fields importance is considered. At last, we evaluate the effectiveness of our proposed methods by experiments.

scholarly journals Clustering in Aggregated User Profiles Across Multiple Social Networks

2017 ◽  
Vol 7 (6) ◽  
pp. 3692
Author(s):  
Charu Virmani ◽  
Anuradha Pillai ◽  
Dimple Juneja
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Similarity Measure ◽  
Online Social Networks ◽  
Vital Role ◽  
User Profiles ◽  
Ensemble Clustering ◽  
Clustering Method ◽  
A Value

A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble K-means clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.

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