scholarly journals Extraction of Multilayered Social Networks from Activity Data

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Katarzyna Musial ◽  
Piotr Bródka ◽  
Przemysław Kazienko ◽  
Jarosław Gaworecki
Keyword(s):  
Social Networks ◽  
Social Network ◽  
New Method ◽  
Activity Data ◽  
Communication Medium ◽  
Web Based ◽  
Characteristic Element ◽  
Additional Challenge ◽  
Different Types ◽  
E Mail

The data gathered in all kinds of web-based systems, which enable users to interact with each other, provides an opportunity to extract social networks that consist of people and relationships between them. The emerging structures are very complex due to the number and type of discovered connections. In web-based systems, the characteristic element of each interaction between users is that there is always an object that serves as a communication medium. This can be, for example, an e-mail sent from one user to another or post at the forum authored by one user and commented on by others. Based on these objects and activities that users perform towards them, different kinds of relationships can be identified and extracted. Additional challenge arises from the fact that hierarchies can exist between objects; for example, a forum consists of one or more groups of topics, and each of them contains topics that finally include posts. In this paper, we propose a new method for creation of multilayered social network based on the data about users activities towards different types of objects between which the hierarchy exists. Due to the flattening, preprocessing procedure of new layers and new relationships in the multilayered social network can be identified and analysed.

Analyzing Social Networks to Mine Important Friends

2013 ◽  
pp. 90-104 ◽  
Author(s):  
Carson K.-S. Leung ◽  
Irish J. M. Medina ◽  
Syed K. Tanbeer
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Social Networking Sites ◽  
Web Based ◽  
The Social ◽  
Social Entity ◽  
Social Entities ◽  
Media Data ◽  
Media Mining

The emergence of Web-based communities and social networking sites has led to a vast volume of social media data, embedded in which are rich sets of meaningful knowledge about the social networks. Social media mining and social network analysis help to find a systematic method or process for examining social networks and for identifying, extracting, representing, and exploiting meaningful knowledge—such as interdependency relationships among social entities in the networks—from the social media. This chapter presents a system for analyzing the social networks to mine important groups of friends in the networks. Such a system uses a tree-based mining approach to discover important friend groups of each social entity and to discover friend groups that are important to social entities in the entire social network.

Building Recommendation Service with Social Networks and Semantic Databases

2014 ◽  
pp. 83-92
Author(s):  
Sašo Karakatič ◽  
Vili Podgorelec ◽  
Marjan Heričko
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Semantic Information ◽  
Additional Data ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Web Based ◽  
Semantic Database

In this chapter, it is shown how useful user services can be created through the integration of social networks and semantic databases. The authors developed a recommendation service in a form of a Web-based application, where a user's interests are imported from social network Facebook and linked with additional data from open semantic database Freebase. Based on a custom implementation of k-nearest neighbors algorithm, the developed method is able to find recommendations based on users’ interests enriched with semantic information. The resulting list of found recommendations is then shown to the user in some basic categories like movies, music, games, books, and others.

Influential Nodes in Social Networks

2020 ◽  
pp. 371-385
Author(s):  
Kousik Das ◽  
Rupkumar Mahapatra ◽  
Sovan Samanta ◽  
Anita Pal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Structure ◽  
The Social ◽  
Different Types ◽  
Influential Nodes

Social network is the perfect place for connecting people. The social network is a social structure formed by a set of nodes (persons, organizations, etc.) and a set of links (connection between nodes). People feel very comfortable to share news and information through a social network. This chapter measures the influential persons in different types of online and offline social networks.

A Usability Evaluation of Facebook's Privacy Features Based on the Perspectives of Experts and Users

2019 ◽  
pp. 1544-1568
Author(s):  
Márcio J. Mantau ◽  
Marcos H. Kimura ◽  
Isabela Gasparini ◽  
Carla D. M. Berkenbrock ◽  
Avanilde Kemczinski
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
User Interaction ◽  
Usability Evaluation ◽  
Second Step ◽  
Web Based ◽  
The Third ◽  
Privacy Settings ◽  
Three Stages

The issue of privacy in social networks is a hot topic today, because of the growing amount of information shared among users, who are connected to social media every moment and by different devices and displays. This chapter presents a usability evaluation of the privacy features of Facebook's social network. The authors carry out an evaluation composed by three approaches, executed in three stages: first by the analysis and inspection of system's features related to privacy, available for both systems (Web-based systems and mobile-based systems, e.g. app). The second step is a heuristic evaluation led by three experts, and finally, the third step is a questionnaire with 605 users to compare the results between specialists and real users. This chapter aims to present the problems associated with these privacy settings, and it also wants to contribute for improving the user interaction with this social network.

Motif Analysis and the Periodic Structural Changes in an Organizational Email-Based Social Network

2011 ◽  
pp. 292-302
Author(s):  
Krzysztof Juszczyszyn ◽  
Katarzyna Musial
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Topology ◽  
Structural Changes ◽  
Structural Parameters ◽  
Local Network ◽  
Network Motifs ◽  
Motif Analysis ◽  
University Of Technology ◽  
E Mail

Network motifs are small subgraphs that reflect local network topology and were shown to be useful for creating profiles that reveal several properties of the network. In this work the motif analysis of the e-mail network of the Wroclaw University of Technology, consisting of over 4000 nodes was conducted. Temporal changes in the network structure during the period of 20 months were analysed and the correlations between global structural parameters of the network and motif distribution were found. These results are to be used in the development of methods dedicated for fast estimating of the properties of complex internet-based social networks.

Keyword Selection Methodology for Identification of Major Events using Social Networks

2015 ◽  
Vol 7 (1) ◽  
pp. 42-60
Author(s):  
Eitan Bahir ◽  
Ammatzia. Peled
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Early Identification ◽  
Real Time Systems ◽  
Threshold Values ◽  
Terror Attacks ◽  
Different Types ◽  
Processing Effort ◽  
Selection Methodology ◽  
Time Systems

The understanding of information communicated over social networks enables quick tracking of real events as they occur. In other cases, where the “crowd” factor is on high note, it is possible to identify events and to evaluate their magnitude, even before they occur. A full assessment of the content generated by social network users is very complex. This, due to the gigantic volume of data communicated over the net at any given time. Using few, well defined, keywords for the detection of relevant data reduces, considerably, the processing effort and expedites the identification of events, such as wildfire, floods or terror attacks. The preliminary results here has shown that by using keywords, specially tailored for different types of major events, one may detect ‘abnormal' surges of social network activities. Also, presented are threshold values, in terms of magnitude and frequency designed for early detection of these events. This approach is the basis for the development of algorithms for early identification real time systems and for geographical tracking of major events.

Interest-oriented versus relationship-oriented social network sites in China

First Monday ◽  
2010 ◽  
Author(s):  
Weiyu Zhang ◽  
Rong Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Structural Analysis ◽  
Collective Action ◽  
Social Ties ◽  
Social Network Sites ◽  
Design Features ◽  
The Social ◽  
Different Types ◽  
Common Interests

This paper examines interest-oriented vs. relationship-oriented social network sites in China and their different implications for collective action. By utilizing a structural analysis of the design features and a survey of members of the social networks, this paper shows that the way a social network site is designed strongly suggests the formation and maintenance of different types of social ties. The social networks formed among strangers who share common interests imply different types of collective action, compared to the social networks that aim at the replication and strengthening of off-line relationships.

scholarly journals Image Based Classification of Rumor Information from the Social Network Platform

2021 ◽  
Vol 38 (5) ◽  
pp. 1413-1421
Author(s):  
Vallamchetty Sreenivasulu ◽  
Mohammed Abdul Wajeed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
End Users ◽  
Web Based ◽  
Suggested Approach ◽  
Text Input ◽  
Social Network Data ◽  
Proposed Model ◽  
Spam Filters ◽  
Network Platform

Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.

scholarly journals Location prediction in location-based social networks

2017 ◽  
Vol 7 (3) ◽  
pp. 149-156
Author(s):  
Mucahit Baydar ◽  
Songul Albayrak
Keyword(s):  
Social Networks ◽  
Wireless Networks ◽  
Social Network ◽  
Mobile Devices ◽  
New Method ◽  
Time Range ◽  
Location Prediction ◽  
The Social ◽  
Location Based Social Network ◽  
Location Based Social Networks

AbstractDevelopments in mobile devices and wireless networks have led to the increasing popularity of location-based social networks. These networks allow users to explore new places, share their location, videos and photos and make friends. They give information about the mobility of users, which can be used to improve the networks. This paper studies the problem of predicting the next check-in of users of location-based social networks. For an accurate prediction, we first analyse the datasets that are obtained from the social networks, Foursquare and Gowalla. Then we obtain some features like place popularity, place popular time range, place distance to user’s home, user’s past visits, category preferences and friendships ,which are used for prediction and deeper understanding of the user behaviours. We use each feature individually, and then in combination, using the new method. Finally, we compare the acquired results and observe the improvement with the new method.Keywords: Location prediction, location-based social network, check-in data.

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