Social Network Graph Research Articles

On M-Polynomials of Dunbar Graphs in Social Networks

Topological indices describe mathematical invariants of molecules in mathematical chemistry. M-polynomials of chemical graph theory have freedom about the nature of molecular graphs and they play a role as another topological invariant. Social networks can be both cyclic and acyclic in nature. We develop a novel application of M-polynomials, the ( m , n , r ) -agent recruitment graph where n > 1 , to study the relationship between the Dunbar graphs of social networks and the small-world phenomenon. We show that the small-world effects are only possible if everyone uses the full range of their network when selecting steps in the small-world chain. Topological indices may provide valuable insights into the structure and dynamics of social network graphs because they incorporate an important element of the dynamical transitivity of such graphs.

Privacy-Preserving Approach PBCN in Social Network With Differential Privacy

Currently, lots of real social relations in social networks force users to face the potential risk of privacy leakage. Consequently, data holders would like to disturbor anonymize their individual data before publishing them, for the purpose of privacy protection. Due to the characteristics of high sensitivity and large volume data of social network graph structure, it is difficult for privacy protection schemes to enable a reasonable allocation of noises while keeping desirable data availability and execution efficiency. On the basis of differential privacy model, combining with clustering and randomization algorithms, a privacy protection approach PBCN (Privacy Preserving Approach Based on Clustering and Noise) is proposed. This proposal is composed of five algorithms including random disturbance based on clustering, graph reconstruction after disturbing degree sequence and noise nodes generation, etc. Furthermore, a privacy measure algorithm based on adjacency degree is put forward in order to objectively evaluate the privacy-preserving strength of various schemes against graph structure and degree attacks. Simulation experiments are conducted to achieve performance comparisons between PBCN, Spctr Add/Del, Spctr Switch, DER and HPDP. The experimental results show that PBCN realizes more satisfactory data availability and execution efficiency. Finally, parameters utility analysis demonstrates PBCN can achieve a “trade-off” between data availability and privacy protection level.

COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.

BackgroundSince the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it.ObjectiveThe aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation.MethodsThis paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined.ResultsSocial network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter.ConclusionsThe combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

Incorporating Reverse Search for Friend Recommendation with Random Walk

Recommending friends is an important mechanism for social networks to enhance their vitality and attractions to users. The huge user base as well as the sparse user relationships give great challenges to propose friends on social networks. Random walk is a classic strategy for recommendations, which provides a feasible solution for the above challenges. However, most of the existing recommendation methods based on random walk are only weighing the forward search, which ignore the significance of reverse social relationships. In this paper, we proposed a method to recommend friends by integrating reverse search into random walk. First, we introduced the FP-Growth algorithm to construct both web graphs of social networks and their corresponding transition probability matrix. Second, we defined the reverse search strategy to include the reverse social influences and to collaborate with random walk for recommending friends. The proposed model both optimized the transition probability matrix and improved the search mode to provide better recommendation performance. Experimental results on real datasets showed that the proposed method performs better than the naive random walk method which considered the forward search mode only.

Publishing Social Network Graph Eigenspectrum With Privacy Guarantees

Online social networks (OSNs) often refuse to publish their social network graphs due to privacy concerns. Recently, differential privacy has become the widely accepted criteria for privacy preserving data publishing. Although some work has been done on publishing matrices with differential privacy, they are computationally unpractical as they are not designed to handle large matrices such as adjacency matrices of OSN graphs. In this paper, we propose a random matrix approach to OSN data publishing, which achieves storage and computational efficiency by reducing dimensions of adjacency matrices and achieves differential privacy by adding a small amount of noise. Our key idea is to first project each row of an adjacency matrix into a low-dimensional space using random projection, and then perturb the projected matrix with random noise, and finally publish the perturbed and projected matrix. In this paper, we first prove that random projection plus random perturbation preserve differential privacy, and also that the random noise required to achieve differential privacy is small. We validate the proposed approach and evaluate the utility of the published data for three different applications, namely node clustering, node ranking, and node classification, using publicly available OSN graphs of Facebook, LiveJournal, and Pokec.

Electronic cigarettes: Emerging trends and research hotspots.

INTRODUCTIONResearch on electronic cigarettes is an emerging field, with the number of articles in this field noted to have grown exponentially over recent years. We used a bibliometric analysis method (co-word analysis) to analyze the emerging trends and research hotspots in this field.METHODSPublication data on electronic cigarettes from 2010 to 2018 were retrieved and downloaded from the PubMed database. Theme trends and knowledge structures were analyzed on the relevant research fields of electronic cigarettes by using a biclustering analysis, strategic diagram analysis, and social network analysis methods. Research hotspots were extracted and compared from three periods.RESULTSCore topics that have continuously develop between the years 2010 and 2018 include: tobacco use cessation devices; tobacco products; tobacco use cessation devices/adverse effects; smoking prevention and adverse effects; electronic nicotine delivery systems/economics; and public health. Some currently undeveloped topics that could be considered as new future research directions include: tobacco use disorder/therapy; tobacco use disorder/epidemiology; students/psychology; students/statistics and numerical data; adolescent behavior/psychology; nicotine/toxicity; nicotinic agonists/administration and dosage; and electronic nicotine delivery systems/legislation and jurisprudence.CONCLUSIONSResults suggest that some currently immature topics in strategic coordinates and emerging hotspots in social network graphs can be used as future research directions.

Closed Walk Sampler: An Efficient Method for Estimating Eigenvalues of Large Graphs

Eigenvalues of a graph are of high interest in graph analytics for Big Data due to their relevance to many important properties of the graph including network resilience, community detection and the speed of viral propagation. Accurate computation of eigenvalues of extremely large graphs is usually not feasible due to the prohibitive computational and storage costs and also because full access to many social network graphs is often restricted to most researchers. In this paper, we present a series of new sampling algorithms which solve both of the above-mentioned problems and estimate the two largest eigenvalues of a large graph efficiently and with high accuracy. Unlike previous methods which try to extract a subgraph with the most influential nodes, our algorithms sample only a small portion of the large graph via a simple random walk, and arrive at estimates of the two largest eigenvalues by estimating the number of closed walks of a certain length. Our experimental results using real graphs show that our algorithms are substantially faster while also achieving significantly better accuracy on most graphs than the current state-of-the-art algorithms.

Eigenvalues make the difference – A network analysis of the Chinese Super League

To evaluate collective (macro) performance data in the Chinese Super League, social network analyses and graph theory were implemented in team sports performance analyses. For each team, we constructed a weighted and directed network in which nodes corresponded to players and arrows to passes. A total of 1200 matches during the 2014–2018 seasons were analysed. The results showed significant differences in general network measures among team competitive levels, match locations and match outcomes. Successful teams and home teams had significantly higher link, diameter, density and cluster coefficient values than did the unsuccessful teams or visiting teams; however, the winning teams had significantly lower density and cluster coefficient 2 values than did the losing teams. This study suggested that successful teams or teams with advantages (home, winner) have a high level of total passes and eigenvalues. This is the first report in which eigenvalues (the degree of a team’s distribution of the ball during the match) were able to capture changes in dynamics between different teams. There were positive correlations between all the network variables and the total shots and no correlations between all the network variables and goals.

Visualized analysis of literature on sepsis caused by Gram positive bacteria in SinoMed

To understand the current situation of research in the field of sepsis caused by Gram positive bacteria (G+ bacteria) in China, to clarify the research content and analyze its general research direction, so as to find the hot topics of research in recent years. The literatures in SinoMed related to sepsis caused by G+ bacteria and published in Chinese from building database to October 2019 were screened. The distribution and trend of the published year, journals, research institutions and researchers of relevant literature were analyzed, and Ucinet 6.0 software was used to draw the social network graph of the researchers and to analyze their internal relations. The subject words of related literatures were extracted. The relationship among the subject words in related literatures was arranged according to the centrality by NetDraw in Ucinet 6.0 software, the bibliographic information co-occurrence analysis system software (BICOMS2 software) was used to classify the subject words and the visualization matrix was generated. The graph clustering tool software (gCLUTO software) was used to cluster the subject words, and the visualization surface graph was generated to analyze the current research hot spot, research trend and research direction of G+ bacteria-induced sepsis. A total of 1 976 literatures about sepsis caused by G+ bacteria were retrieved, and 26 literatures in conference summaries, news reports, research information, missing content, or inconsistent with the theme were excluded. Finally, a total of 1 950 literatures were enrolled in final analysis. The number of published literatures analysis showed that from 1979 to 1992, there were few studies about sepsis caused by G+ bacteria, which increased geometrically from 2008, and the number of literatures published from 2008 to 2018 was 1 144, accounting for 58.67% (1 144/1 950). From 1979 to 2019, 23 high-yield institutions published more than 5 literatures, of which 6 were institutions with 10 or more literatures, and only one institution with more than 20 literatures. There were only 5 journals with more than 100 articles, 5 381 authors involved in the literatures, but few authors with more than 10 literatures published, and no inter-provincial or inter-municipal cooperation was found. A social network analysis of 103 high-frequency subject words that appeared more than 5 times showed that the study of sepsis caused by G+ bacteria mainly focused on "sepsis", including the incidence of sepsis caused by drug resistant Staphylococcus aureus was on the rise, especially in newborns and children with weakened immune systems, the selection of therapeutic drugs gradually developed to glycopeptides with strong anti-drug resistance and synthetic oxazolidinones. The research and development of drugs for the treatment of sepsis caused by G+ bacteria might become a new research direction or field in the future. Cluster analysis of 103 high-frequency subject words showed that the research hot spots of G+ bacteria-induced sepsis mainly focused on five topics, namely early diagnosis of sepsis; bacterial infection pathway of sepsis, nosocomial infection and bacterial drug resistance; the basis of epidemiological prevention and treatment of sepsis; venous catheter infection-related sepsis; the treatment, nursing and prognosis of patients with sepsis. The studies of sepsis caused by G+ bacteria are winning more and more attention, but the resources sharing and academic exchanges among hospitals need to be further improved.

Graph Based Digital Spammer Identification by User Click Behavior

As social media network popularity increases day by day, content writer are spamming on these platform for potential benefits of some organizations. Some of blogs are fabricate which invite users to external phishing sites or malware downloads huge security issue online and undermined the user experience. This work has proposed an un-supervised technique for identifying the real users from the social network spammers. Here graph based clustering algorithm was proposed to develop a binary cluster on the basis of serial or sequential action perform by the user. As per series of action social network graph is reduce into spanning tree where highly distance nodes are identified as abnormal behavior. So group of highly distance nodes are consider as the social spammers while other are real users. Experiment was perform on real social sequential dataset of twitter. Results were compared on various evaluation parameters and it was obtained that proposed approach has improved all such parameter values as compared to previous approach adopt by researcher.

Reference architecture for social networks graph analysis tool

When analyzing social networks, graph data structures are often used. Such graphs may have a complex structure that makes their operational analysis difficult or even impossible. This paper discusses the key problems that researchers face in the field of processing big graphs in that particular area. The paper proposes a reference architecture for storage, analysis and visualization of social network graphs, as well as a big graph process Pipeline. Based on this pipeline it is possible to develop a tool that will be able to filter, aggregate and process in parallel big graphs of social networks, and at the same time take into account its structure. The paper includes the implementation of that pipeline using the OrientDB graph database for storage, parallel processing for graph measures calculation and visualization of big graphs using D3. The paper also includes the conducted experiments based on the calculation of betweenness centrality of some graphs collected from the VKontakte social net.

The role of services content for manufacturing competitiveness: A network analysis

This paper explores the phenomenon of international servicification of manufacturing from the period 1995 to 2011. By applying empirical techniques of Social Network Analysis and graph theory, we find that the network of flows of intermediate services embodied in manufacturing exports is still slightly dense and would not correspond to a traditional centre-periphery structure. The mapping shows a numerous, highly cohesive group of countries, with China, the USA and Germany as central economies and an increasing leading role of Asian economies, which would indicate their commitment to upgrading within global value chains. We go a step further by empirically analysing the impact of the countries’ centrality in the global network of intermediate services on manufacturing competitiveness. Our findings reveal that, together with the level of embodiment of intermediate services into manufacturing exports, who the providers of those services inputs are is a key determining factor for manufacturing competitiveness.

AD-C: a new node anomaly detection based on community detection in social networks

Anomaly detection in social networks as a challenging task has gained great attention. Every unusual behavioural pattern in a social network can be spotted as an anomaly which provides useful information. In this paper, a new method is proposed to identify anomaly based on community detection (AD-C) for the social network graph. Our model is made up of weighting in pre-processing step and three principle processes, including community detection, auxiliary community detection and node filtering. AD-C method offers a flexible framework for anomaly detection, which can be employed in different stages of its related algorithms. The experiments are conducted on two social media datasets, including Facebook and Flickr datasets. Experimental results indicate more efficiency in comparison to other anomaly methods as baselines in terms of the F-score. Also, the results indicate that applying the proposed steps lead to increased accuracy of the community detection methods.

Providing a new method for link prediction in social networks based on the meta-heuristic algorithm

Social network analysis is one of the most important research fields in data mining today. The purpose of the analysis of these networks is to extract the embedded knowledge in the data set and to learn the behavior of users in the social networking environment. One of the most attractive and central applications of social network analysis is link prediction. The purpose of link prediction in social networks is to identify missing and unknown information from users or to predict the future link between two users. In recent years, various artificial intelligence algorithms have been introduced as one of the most important tools for resolving link prediction and big data. In this research, a strategy based on a meta-heuristic is used to improve link prediction in social networks. The proposed method is based on the characteristics of the signed social networks provided and turns the link prediction problem into a two-class classification problem. Then uses the capability of the Particle Swarm Optimization (PSO) and the topological properties of the social network graph to create a database with two classes, the first class pointing to the existence of a connection between the users and the second class pointing to the absence of this relationship. Creates a database using the support vector machine model for categorization work and uses the classic Katz similarity criterion for end-user suggestions. Twitter social network information has been used to compare and evaluate the proposed method. The results of the experiments show the superiority of the proposed method with 0.23, 0.99, and 6.32, respectively, compared to Meta-Path, Katz and CN algorithms in F-measure criterion.

Modeling influence and community in social media data using the digital methods initiative-twitter capture and analysis toolkit (DMI-TCAT) and Gephi

The methods summarized in this video tutorial series are based on the open source Digital Methods Initiative – Twitter Capture and Analysis Toolkit (DMI-TCAT) that allows media researchers to collect tweets off the STREAM API (application programming interface) on an ongoing basis. With DMI - TCAT and the open source data visualization software Gephi, social data in the millions of units is quickly and easily sorted by algorithms to find users or items of importance on Twitter, such as in the Fig. 1 below.While these figures and the data gathered though the DMI-TCAT do not provide full firehose access to all historical tweets, they do provide a generally representative sample of tweets that is relatively proportional to the total volume of tweets being posted at any given time (Gerlitz & Rieder, 2013; Groshek & Tandoc, 2016). For more details on the DMI-TCAT and its operation, we encourage readers to visit its github page (https://github.com/digitalmethodsinitiative/dmi-tcat) and note that this cloud-based analytics program is free and customizable.The specific techniques covered in the methodology reported here in text and expanded upon in the video tutorial series include how to:• Model influence users by sizing nodes with the betweenness centrality algorithm;• Identify community groups by adding color using the modularity algorithm;• Spatialize networks through applying the openord algorithm;• Make social network graphs dynamic and interactive online.

Social network analysis of law information privacy protection of cybersecurity based on rough set theory

PurposeThe purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance.Design/methodology/approachIn this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed.FindingsThe results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment.Research limitations/implicationsIn the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification.Practical implicationsIn the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining.Originality/valueIn this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

Estimating PageRank deviations in crawled graphs

Most real-world graphs collected from the Web like Web graphs and social network graphs are partially discovered or crawled. This leads to inaccurate estimates of graph properties based on link analysis such as PageRank. In this paper we focus on studying such deviations in ordering/ranking imposed by PageRank over crawled graphs. We first show that deviations in rankings induced by PageRank are indeed possible. We measure how much a ranking, induced by PageRank, on an input graph could deviate from the original unseen graph. More importantly, we are interested in conceiving a measure that approximates the rank correlation among them without any knowledge of the original graph. To this extent we formulate the HAK measure that is based on computing the impact redistribution of PageRank according to the local graph structure. We further propose an algorithm that identifies connected subgraphs over the input graph for which the relative ordering is preserved. Finally, we perform extensive experiments on both real-world Web and social network graphs with more than 100M vertices and 10B edges as well as synthetic graphs to showcase the utility of HAK and our High-fidelity Component Selection approach.

Efficient Fault-Tolerant Transactions for Distributed Graph Database

Through the ongoing trend in graph technologies due to the massive growth of linked data produced by social networks graph databases gained popularity. Replication, a common approach to increase availability in databases, is also used by diverse graph database solutions. Few approaches implementing fault-tolerance in graph databases have been proposed yet.This paper considers deferred update replication using atomic broadcast in order to implement fault-tolerance in distributed graph databases. The main contribution of this paper is a deferred update algorithm adapted to graph databases offering a more scalable and faster solution, showing a performance advantage of over 30\% compared to existing approaches.

A Novel Graph-modification Technique for User Privacy-preserving on Social Networks

The growing popularity of social networks and the increasing need for publishing related data mean that protection of privacy becomes an important and challenging problem in social networks. This paper describes the (k,l k,l k,l)-anonymity model used for social network graph anonymization. The method is based on edge addition and is utility-aware, i.e. it is designed to generate a graph that is similar to the original one. Different strategies are evaluated to this end and the results are compared based on common utility metrics. The outputs confirm that the na¨ıve idea of adding some random or even minimum number of possible edges does not always produce useful anonymized social network graphs, thus creating some interesting alternatives for graph anonymization techniques.

A Weight based Scheme for Improving the Accuracy of Relationship in Social Network

The usage of social media has become unavoidable in the last decade. The social media is highly dynamic in nature and grows rapidly. The community network offers a rich expedient of various data. The detection of communities is based on the frequency in the networks which is usually represented by graphs. The vertices (nodes) are representing the social actor and the edges (links) represent the relation between those actors. The community link detection is as hard as the graph increases up to millions of vertices and edges. The accuracy of link prediction for inferring missing (erased or broken) links is very complex due to the dynamic nature of links. The links are updated from time to time and the new links are established dynamically. As the links are appeared and disappeared dynamically, the accuracy of identifying the edges of the social network graph of the user is complex in nature. Many efforts have been put up in developing link prediction algorithms in the past, but still there is a lacuna in accuracy in predicting inferred / broken links. A weight based link prediction algorithm is proposed to improve the accuracy of the link prediction on inferred / broken links in the social media. In this method, a weight based link analysis is employed to quantify the relative value between two nodes in the community network. The correlation value for relationship is also determined over a period of time using the designed relationship matrix. The relationship value between the nodes is computed by a Euclidian distance approach. The relationship value of each node is determined by the relationship equation using weight values. The proposed approach is experimented in constrained environment for 2 users’ Facebook usages over a period of a year. The accuracy of relationship is used as performance metrics. The results shown that the accuracy is improved 2.35% more than random predictor method.