scholarly journals Structural Attack to Anonymous Graph of Social Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Tieying Zhu ◽  
Shanshan Wang ◽  
Xiangtao Li ◽  
Zhiguo Zhou ◽  
Riming Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Graph Matching ◽  
Rapid Development ◽  
Sensitive Information ◽  
Matching Method ◽  
Graph Data ◽  
Social Network Data ◽  
Regional Feature ◽  
Social Graphs

With the rapid development of social networks and its applications, the demand of publishing and sharing social network data for the purpose of commercial or research is increasing. However, the disclosure risks of sensitive information of social network users are also arising. The paper proposes an effective structural attack to deanonymize social graph data. The attack uses the cumulative degree ofn-hop neighbors of a node as the regional feature and combines it with the simulated annealing-based graph matching method to explore the nodes reidentification in anonymous social graphs. The simulation results on two social network datasets show that the attack is feasible in the nodes reidentification in anonymous graphs including the simply anonymous graph, randomized graph andk-isomorphism graph.

Genetic Algorithm Applied in the Social Network Data Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 2188-2191
Author(s):  
Lei Liu ◽  
Quan Bao Gao
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Data Analysis ◽  
Rapid Development ◽  
Virtual Community ◽  
Social Network Data ◽  
Starting Point ◽  
The Social ◽  
Optimal Data Analysis

The rapid development of network and information technology makes the network become the indispensable part in people's life. Network design uses email as a starting point, instead of actual letters. Then Happy Nets, BBS etc. are evolved from it, with virtual as their major feature. In the process of social networks evolution, the personal image transformed from the actual into the virtual one. All this has contributed to the birth of the social network, which then makes the contacts among people presenting the feature of network expansion and cost reduction. The popular social network nowadays is considered to be social plus network, namely, through the network, as a carrier, people are connected to form a virtual community with certain characteristics. Based on the genetic algorithm and genetic coding technology, the article is designed to make the optimal data analysis and create a optimistic cyber environment in the process of the social networks explosive development.

Anonymization Techniques for Privacy Preservation in Social Networks: A Review

2021 ◽  
pp. 14-21
Author(s):  
Kalpana Chavhan ◽  
Dr. Praveen S. Challagidad
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Privacy Preservation ◽  
Information Science ◽  
Original Data ◽  
Published Data ◽  
Sensitive Information ◽  
Social Network Data ◽  
The Social

Any data that user creates or owns is known as the user's data (For example: Name, USN, Phone number, address, email Id). As the number of users in social networks are increasing day by day the data generated by the user's is also increasing. Network providers will publish the data to others for analysis with hope that mining will provide additional functionality to their users or produce useful results that they can share with others. The analysis of social networks is used in modern sociology, geography, economics and information science as well as in various fields. Publicizing the original data of social networks for analysis raises issues of confidentiality, the adversary can search for documented threats such as identity theft, digital harassment and personalized spam. The published data may contain some sensitive information of individuals which must not be disclosed for this reason social network data must be anonymized before it is published. To do the data in nominate the anonymization technique should be applied, to preserve the privacy of data in the social network in a manner that preserves the privacy of the user whose records are being published while maintaining the published dataset rich enough to allow for the exploration of data. In order to address the issue of privacy protection, we first describe the concept of k-anonymity and illustrate different approaches for its enforcement. We then discuss how the privacy requirements characterized by k-anonymity can be violated in data mining and introduce possible approaches to ensure the satisfaction of k-anonymity in data mining also several attacks on dataset are discussed.

scholarly journals Differential Privacy for Edge Weights in Social Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoye Li ◽  
Jing Yang ◽  
Zhenlong Sun ◽  
Jianpei Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Differential Privacy ◽  
Social Issues ◽  
Shortest Paths ◽  
Edge Weight ◽  
Sensitive Information ◽  
Original Order ◽  
Social Graphs ◽  
Inference Strategy

Social networks can be analyzed to discover important social issues; however, it will cause privacy disclosure in the process. The edge weights play an important role in social graphs, which are associated with sensitive information (e.g., the price of commercial trade). In the paper, we propose the MB-CI (Merging Barrels and Consistency Inference) strategy to protect weighted social graphs. By viewing the edge-weight sequence as an unattributed histogram, differential privacy for edge weights can be implemented based on the histogram. Considering that some edges have the same weight in a social network, we merge the barrels with the same count into one group to reduce the noise required. Moreover,k-indistinguishability between groups is proposed to fulfill differential privacy not to be violated, because simple merging operation may disclose some information by the magnitude of noise itself. For keeping most of the shortest paths unchanged, we do consistency inference according to original order of the sequence as an important postprocessing step. Experimental results show that the proposed approach effectively improved the accuracy and utility of the released data.

scholarly journals Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yanni Liu ◽  
Dongsheng Liu ◽  
Yuwei Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Smart City ◽  
Topic Model ◽  
Rapid Development ◽  
Mobile Internet ◽  
Time Curve ◽  
Public Opinions ◽  
The Social

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

Social Networks and Crime: Pitfalls and Promises for Advancing the Field

2019 ◽  
Vol 2 (1) ◽  
pp. 99-122 ◽  
Author(s):  
Katherine Faust ◽  
George E. Tita
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Potential Measurement ◽  
Fully Integrated ◽  
Social Network Data ◽  
The Past ◽  
Dynamic Social Network ◽  
Criminological Research ◽  
The Social ◽  
Criminological Theories

Over the past decade, a considerable literature has emerged within criminology stemming from the collection of social network data and the adoption of social network analysis by a cadre of scholars. We review recent contributions to four areas of crime research: co-offending networks, illicit networks, gang-rivalry networks, and neighborhoods and crime. Our review highlights potential pitfalls that one might encounter when using social networks in criminological research and points to fruitful directions for further research. In particular, we recommend paying special attention to the clear specifications of what ties in the network are assumed to be doing, potential measurement weaknesses that can arise when using police or investigative data to construct a network, and understanding dynamic social network processes related to criminological outcomes. We envision a bright future in which the social network perspective will be more fully integrated into criminological theories, analyses, and applications.

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 Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

Network Basics: Points, Lines, and Positions

2020 ◽  
pp. 15-33
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Ethical Issues ◽  
Building Blocks ◽  
Levels Of Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Basic Concepts ◽  
Statistical Approaches

This chapter presents an introduction to the basic concepts central to social network analysis. Written for those with little experience in the approach, the chapter aims to provide the necessary tools to dig deeper into exploring social networks via the subsequent chapters in this volume. It begins by introducing the building blocks of networks—nodes and edges—and their characteristics. Next, it outlines several of the major dimensions of network analysis, including the implications of boundary specification and levels of analysis. It also briefly introduces statistical approaches to networks and network data collection. The chapter concludes with a discussion of ethical issues that arise when collecting and analyzing social network data.

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