Preserving structural properties in anonymization of social networks

Social networks are attracting significant interest from researchers in different domains, especially with the advent of social networking systems which enable large-scale collection of network information. However, as much as analysis of such social networks can benefit researchers, it raises serious privacy concerns for the people involved in them. To address such privacy concerns, several techniques, such as k-anonymity-based approaches, have been proposed in the literature to provide user anonymity in published social networks. However, these methods usually introduce a large amount of distortion to the original social network graphs, thus, raising serious questions about their utility for useful social network analysis. Consequently, these techniques may never be applied in practice. We propose two methods to enhance edge-perturbing anonymization methods based on the concepts of structural roles and edge betweenness in social network theory. We experimentally show significant improvements in preserving structural properties in an anonymized social network achieved by our approach compared to the original algorithms over several data sets.

Social media network analysis has become very popular in recent years. How do real networks evolve over time? What are the normal evolving behaviors in a social media network? In order to extract behaviors occurring regularly to reveal the microscopic evolving properties in social networks, the evolving process of networks is modeled as stochastic states transition, and the evolving behaviors are described as topological structure changes of a series of sub graphs. Then, based on Maximal Frequent Sub graph mining technology, RB Miner (Regular Behaviors Miner) algorithm is proposed to identify such regular behaviors in network dataset. The empirical evaluation using both synthetic and real dataset verifies that the proposed algorithm is valid, and the regular behavior patterns show more dynamic information hidden in evolving social networks than normal frequent sub graph patterns.

Some mining methods and algorithms will be introduced to describe the social network. Method based on similarity measurement and inductive logic programming are useful here to analyze social networks, moreover, some specific datasets are used to analyze the characters of social networks by Ucinet which is very powerful data mining software. The Zachary club social network's property can be found from analysis.

The aim of the study is a practical analysis of social network services as a place to store multimedia resources that allow you to store and share digital photos, audio and video recordings, text files, presentations, and organize resource discussions for free. They can be used as educational materials (educational videos, photos on various topics, audio and video recordings of scientists' speeches, eyewitnesses of events, etc.), as well as means to store student video, photo, audio files and creative works. Method. Theoretical materials of the research are the best practices of foreign and domestic scientists in the field of network communications and their impact on the future specialists preparing in geography and tourism, namely articles, monographs, abstracts, dissertations. The study uses the method of theoretical materials analysis, as well as methods of observation, abstraction in identifying the qualification characteristics of social Internet networks. Content analysis (analysis of the social networks community messages content); statistical and comparative information analysis was carried out. Empirical research on the social Internet networks lies in creating and filling a community on the social Internet network, as well as engaging students in that community. Results. In the research it was found out that social networks can be used to solve the following tasks: to organize the collective work of students in the classroom (and outside it), which will be favourable to the communication, to gain the team experience; to ensure the development of the students personalized learning environment; to promote students independent learning, as everyone works in his own rhythm; to promote informal communication between the tutor and the students; to change the tutor's role, who will become an assistant, friend, mentor; to create digital educational content of the disciplines. The scientific novelty of the obtained results lies in the fact that on the comprehensive analysis of social internet networks, internet blogs, tourist internet resources basis, their place and role in the information and educational environment of higher education institutions are determined, in particular: the specifics of the content, prospects and mechanisms of the development of the social Internet networks from the point of view of training future tourists are determined; the dependence of the functions of the Social Internet Network, Internet blogs on their structural elements is established. Practical significance. The obtained scientific results are used in the educational process of future specialists in the field of training "Tourism", with the educational qualification "tourism expert, organizer of excursion and animation activities" when teaching the disciplines "study of local lore and tourism", "tourist work", "recreational geography", "geography of Ukraine", "professional competencies in tourism business", "organization of tourist activities". The research practices can be used to conduct tourist, museum, local history, excursion and other types of student's practice.

While privacy preservation of data mining approaches has been an important topic for a number of years, privacy of social network data is a relatively new area of interest. Previous research has shown that anonymization alone may not be sufficient for hiding identity information on certain real world data sets. In this paper, we focus on understanding the impact of network topology and node substructure on the level of anonymity present in the network. We present a new measure, topological anonymity, that quantifies the amount of privacy preserved in different topological structures. The measure uses a combination of known social network metrics and attempts to identify when node and edge inference breeches arise in these graphs.

We address the problem of efficiently discovering the influential nodes in a social network under the susceptible/infected/susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with pruning and burnout strategies. We experimentally demonstrate that the proposed method gives much better solutions than the conventional methods that are solely based on the notion of centrality for social network analysis using two large-scale real-world networks (a blog network and a wikipedia network). We further show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by experimentation. The properties of the influential nodes discovered are substantially different from those identified by the centrality-based heuristic methods.KeywordsGreedy AlgorithmMaximization ProblemPruning MethodLinear ThresholdBond PercolationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.

The third-party enterprises, such as sociologists and commercial companies, are mining data published from online social network (OSN) websites (e.g., Face book, Twitter) to serve their diverse purposes. This process leads to critical user concerns over their privacy, especially sensitive relationship with others on OSNs. Existing anonymization techniques in publishing online social data are focused on user identities, as users' relationship privacy will be automatically protected in general, if their identities are hidden. However, in reality, some users can still be identified from an identity-anonymized OSN by an attacker, as an individual user may publish his personal information to the public, through blog for example, which can be exploited by the attacker to re-identify the user from the published data. Therefore, we intend to preserve relationship privacy between two users one of whom can even be identified in the released OSN data. We define the ℓ-diversity anonymization model to preserve users' relationship privacy. Additionally, we devise two algorithms to achieve the ℓ-diversity anonymization - one only removes edges while the other only inserts vertices/edges for maintaining as many topological properties of the original social networks as possible, thus retaining the utility of the published data for the third-parties. Extensive experiments are conducted on both synthetic and real-world social network data sets to demonstrate that except from the achievement of privacy preservation, the utility loss caused by our proposed graph manipulation based techniques is acceptable. Besides, we analyze the influence of social network topology (e.g., average degree, network scalability) on the performance of our algorithms.

This study sampled 24 interviews of Vietnamese Female Spouses living in Nantou. Interview and observation are used to realize their social network formation after they came to Taiwan. The social network was either duplication as their original community or a new multicultural group? Which one is more helpful for them? Besides, based on their social relationship, the study tried to explain why most of their friends are Vietnamese. This study found that the Vietnamese female spouses copied their original social network. Most members met at works and offered them great support. It proved that the brides have small, similar and closed social relationship in Taiwan. Ethnicity and class are two important factors, which influenced on their social network development.

The Internet of Things (IoT) has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses. Social network analysis (SNA) is a good example that has recently gained a lot of scientific attention. It has its roots in social and economic research, as well as the evaluation of network science, such as graph theory. Scientists in this area have subverted predefined theories, offering revolutionary ones regarding interconnected networks, and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon. The motivation of this study is to understand and capture the clustering properties of large networks and social networks. We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients. The random walk technique is paired with a triangle generating scheme in our proposed model. As a result, the clustering control mechanism and preferential attachment (PA) have been realized. This research builds on the present random walk model. We took numerous measurements for validation, including degree behavior and the measure of clustering decay in terms of node degree, among other things. Finally, we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods, and hence it could be a viable alternative for societal evolution.

Exploring social networks through stochastic multilayer graph modeling

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

Social support networks have been become one of the mostly influential metaphors in many areas around the world, especially in terms of mitigating disaster consequences and revivifying disaster affected livelihoods. More importantly, reciprocal supports ties are more powerful in healing disaster wounds of communities. Especially social capital legacies enrich by reciprocal support networks by the ways in which making may hopes among disaster victims. In this context, the objective of this study is to investigate the social support network behaviors in supporting 16 flood-affected households in the mass flooding event occurred in 2017. Particularly, the study researched social support network behaviors at different flood inundation phases such as before, during, and after. This study collected primary data (mainly social network data) using household survey and filed observations. The study used the Social Network Analysis (SNA) method for the network data analysis. This article shows that flood affected households have received social network supports in different magnitudes at before, during, and after flood inundation phases. More importantly, provision of foods, water and basic needs, sheltering, clearing contaminated households, and emotional supports have mobilized and reciprocated among victims in reviving their livelihoods. The social support network legacies have evolved at different flood inundation phases. This study fills the gabs in the flood disaster discourse on Sri Lankan context.

In the transition to an information society, a government structure that possesses integrated, efficient, transparent and simplified business processes is a basic principle. In line with this, providing interoperability within the public sector that encompasses not only corporations and organizations but also local administrations and identifying the necessary procedures and standards for this is of significant importance. In this context, the social network analysis (SNA) is an effective tool. Using SNA, this study examined real estate (immovable) appraisal (valuation) that is widely used by the public sector as well as the private sector that is realized through multi-headed organizational structures in Turkey. With the numerical and visual analysis conducted at the central county of the province of Afyonkarahisar, answers were sought to questions such as, “In the work relating to real estate appraisal which organization is most concerned?” “In obtaining real estate appraisal related data which organization is cooperated with the most?” “Of the organizations that undertake real estate appraisal which organization is known the most?” “Which organization that carries out real estate appraisal is trusted the most?” In conclusion, the relationship between organizations that undertook real estate appraisals were evaluated using the SNA and an unseeable social relationship network was revealed. Key words: Social networks, social network analysis, real estate appraisal, Turkey.

With the explosion of graph scale of social networks, it becomes increasingly impractical to study the original large graph directly. Being able to derive a representative sample of the original graph, graph sampling provides an efficient solution for social network analysis. We expect this sample could preserve some important graph properties and represent the original graph well. If one algorithm relies on the preserved properties, we can expect that it gives similar output on the original graph and the sampled graph. This leads to a systematic way to accelerate a class of graph algorithms. Our work is based on the idea of stratified sampling [14], a widely used technique in statistics. We propose a heuristic approach to achieve efficient graph sampling based on community structure of social networks. With the aid of ground-truth of communities available in social networks, we find out that sampling from communities preserves community- related graph properties very well. The experimental results show that our framework improves the performance of traditional graph sampling algorithms and therefore, is an effective method of graph sampling.