Aspects Of Social Network Analysis Research Articles

A Deep Community Detection Approach in Real Time Networks

Community detection in real time networks is one of the important aspect of social network analysis. Deep learning has been applied successfully in a variety of research fields in recent years. Proximity matrix is frequently used as the representation of the network structure. However, there are issues with the proximity matrix's insufficient spatial contiguity information. As a result, this research provides a deep learning applied community identification approach that combines the reorganization of the matrices, spatial attribute uprooting, and community identification. For obtaining a spatial proximity matrix, the primary proximity matrices in a real time graph is recreated using the highest weight and adjacent users. The dimensional proximity matrix can obtain a subdomain of the network, allowing the convolutional neural network (CNN) to draw out dimensional localization more easily and fast. Ten different real time datasets of social networks are used in tests to examine our proposed approach. Our results show that the proposed community identification approach has higher compatibility than existing deep learning-based strategies. As a result, the proposed deep community identification approach is capable of detecting the excellent clusters in real time networks.

Link prediction for existing links in dynamic networks based on the attraction force

Link prediction has become an important technology which can be widely applied to many aspects of social network analysis. Numerous link prediction methods have been proposed in recent years, which can detect missing links in the current network and predict whether two disconnected nodes will be connected in the future. However, some existing links may be spurious links that should be removed from the current network, and some existing links will leave the network in the future because network changes over time. In this paper, a novel link prediction approach based on the attraction force between nodes in dynamic networks is proposed to detect spurious links at time t and predict whether existing links at time t will leave the network at time t + 1. At first, the attraction force between nodes is defined according to the law of gravitation, and a level is assigned to each node in the network at time t. Then, the connection probability of each pair of connected nodes is calculated according to the level of nodes and the attraction force between nodes. Next, the connection probability of all existing links is calculated to detect whether they were spurious links that should be removed from the network at time t. For an existing link, the lower the connection probability, the more likely it is to be a spurious link. Furthermore, the changes of network from time t to t + 1 is considered to build a virtual network and the connection probability of all existing links in the virtual network is calculated to predict whether they will leave the network at time t + 1. Experimental results on some real-world networks show that the proposed approach can achieve acceptable prediction accuracy and can be effectively applied to the prediction of existing links in dynamic networks.

Evaluating Methods for Efficient Community Detection in Social Networks

Exploring a community is an important aspect of social network analysis because it can be seen as a crucial way to decompose specific graphs into smaller graphs based on interactions between users. The process of discovering common features between groups of users, entitled “community detection”, is a fundamental feature for social network analysis, wherein the vertices represent the users and the edges their relationships. Our study focuses on identifying such phenomena on the Twitter graph of posts and on determining communities, which contain users with similar features. This paper presents the evaluation of six established community-discovery algorithms, namely Breadth-First Search, CNM, Louvain, MaxToMin, Newman–Girvan and Propinquity Dynamics, in terms of four widely used graphs and a collection of data fetched from Twitter about man-made and physical data. Furthermore, the size of each community, expressed as a percentage of the total number of vertices, is identified for the six particular algorithms, and corresponding results are extracted. In terms of user-based evaluation, we indicated to some students the communities that were extracted by every algorithm, with a corresponding user and their tweets in the grouping and considered three different alternatives for the extracted communities: “dense community”, “sparse community” and “in-between”. Our findings suggest that the community-detection algorithms can assist in identifying dense group of users.

A Guide for Choosing Community Detection Algorithms in Social Network Studies: The Question Alignment Approach

Community detection, the process of identifying subgroups of highly connected individuals within a network, is an aspect of social network analysis that is relevant but potentially underutilized in prevention research. Guidance on using community detection methods stresses aligning methods with specific research questions but lacks clear operationalization. The Question Alignment approach was developed to help address this gap and promote the high-quality use of community detection methods. A total of 6 community detection methods are discussed: Walktrap, Edge-Betweenness, Infomap, Louvain, Label Propagation, and Spinglass. The Question Alignment approach is described and demonstrated using real-world data collected in 2013. This hypothetical case study was conducted in 2019 and focused on targeting a hand hygiene intervention to high-risk communities to prevent influenza transmission. Community detection using the Walktrap method best fit the hypothetical case study. The communities derived using the Walktrap method were quite different from communities derived through the other 5 methods in both the number of communities and individuals within communities. There was evidence to support that the Question Alignment approach can help researchers produce more useful community detection results. Compared to other methods of selecting high-risk groups, the Walktrap produced the most communities that met the hypothetical intervention requirements. As prevention research incorporating social networks increases, researchers can use the Question Alignment approach to produce more theoretically meaningful results and potentially more useful results for practice. Future research should focus on assessing whether the Question Alignment approach translates into improved intervention results.

Is less more? A commentary on the practice of ‘metric hacking’ in animal social network analysis

The use of social network analysis to quantify animal social relationships has increased exponentially over the last two decades. A popular aspect of social network analysis is the use of individually based network metrics. Despite the diversity of social network metrics that exist and the large number of studies that generate network metrics, little guidance exists on the number and type of metrics that should be analysed in a single study. Here, we comment on the ‘hypothesize after results are known’ (HARKing) phenomenon in the context of social network analysis, a practice that we term ‘metric hacking’ and define as the use of statistical criteria to select which metrics to use rather than a priori choice based on a research hypothesis. We identify three situations where metric hacking can occur in studies quantifying social network metrics: (1) covariance among network metrics as explanatory variables in the same model; (2) covariance among network metrics as response variables in multiple models; and (3) covariance between response and explanatory variables in the same model. We outline several quantitative and qualitative issues associated with metric hacking, provide alternative options and guidance on the appropriate use of multiple network metrics to avoid metric hacking. By increasing awareness of the use of multiple social network metrics, we hope to encourage better practice for the selection and use of social network metrics in animal social network analysis.

Análise de redes sociais: o método e sua utilização nas Ciências Sociais brasileiras

This article analyzes descriptively the use of the method of social network analysis in the main publications in the Brazilian Social Sciences, in order to offer contributions for two questions: (1) what is the social network analysis?; and (2) how often has the method been used in the main publications in this area? About the first question, extensive bibliographical research is undertaken. Regarding the second question, the publications, between 2006 and 2016, of the journals with the best ranking for the following areas are analyzed: (1) Anthropology and Archeology, (2) Political Science and International Affairs, and (3) Sociology. The analysis of this method’s use indicates the existence of a small number of papers, and the absence of substantial differences between the journals of the three areas. However, the papers that were found exemplify the interdisciplinary aspect of social network analysis and its capability to be combined with other techniques in the study of various phenomena. Keywords: Method; Social network analysis; Social Sciences; Journals.

Social Network Analysis of Terrorist Networks

Terrorist Activities worldwide has led to the development of sophisticated methodologies for analyzing terrorist groups and networks. Ongoing and past research has found that Social Network Analysis (SNA) is most effective method for predictive counter-terrorism. Social Network Analysis (SNA) is an approach towards analyzing the terrorist networks to better understand the underlying structure of a network and to detect key players within the network and their links throughout the network. It is also need of the hour to convert available raw data into valuable information for the purpose of global security. Comparative study among SNA tools testify their applicability and usefulness for data gathered through online and offline social sources. However it is advised to incorporate temporal analysis using data mining methods, to improve the capability of SNA tools to handle dynamic social media data. This paper examine various aspects of Social Network Analysis as applied to terrorism, taking empirical data, and open source data based studies into account. This work primarily focuses on different types of decentralized terrorist networks and nodes. The nodes can be classified as organizations, places or persons. We take help of varied centrality measures to identify key players in this network.

Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach

The increased popularity and accessibility of social network analysis has improved our ability to test hypotheses about complex animal social structures. To gain a deeper understanding of the use and application of animal social network analysis, we systematically surveyed the literature and extracted information on publication trends from articles using social network analysis. We synthesize trends in social network research over time and highlight variation in the use of different aspects of social network analysis. The use of social network analysis in empirical articles has increased over time. In the context of social network methods, we found that many studies did not use an association index to account for missing individuals or observations of individuals; that the number and type of social network metrics calculated in a given study varied substantially (median = 2); and that focal observation was by far the most common method used to generate social networks, although the use of biologging devices increased over time. We also observed that most species studied using social networks are mammals (55%) or birds (23%), and that the majority are species of least concern (59%; International Union for the Conservation of Nature, IUCN, www.iucn.org ). Based on our findings, we highlight four key recommendations for future studies: (1) the use of association indices is almost always necessary; (2) the a priori selection of specific network metrics and associated hypotheses increases transparency; (3) combination of focal observation with biologging devices could improve our understanding of remotely sensed behaviours; and (4) because most studies rarely study species of conservation concern, it may be practical to generate networks for similar species or populations, which could help inform management decisions. We highlight emerging trends in social network research that may be valuable for distinct groups of social network researchers: students new to social network analysis, experienced behavioural ecologists interested in using social network analysis and advanced social network users interested in trends of social network research. Our findings also shed light on past research and provide guidance for future studies using social network analysis.

Social Network Analysis in Archaeology

Social network analysis (SNA) in archaeology has become important for a range of theoretical and methodological approaches that can more generally be characterized as relational. They are relational in that it is the ties between actors (or nodes) that define social connections. Archaeologists are currently employing a diversity of theoretical approaches to networks, and the perspective taken in this review is that SNA can provide insights into a number of different social processes using different theories. Following a brief historical overview, I discuss two aspects of SNA: the structural position of the actor or node, and characterizations of whole networks. I then summarize several broad classes of archaeological networks: historical, spatial, and material. I conclude with a call for more bridging approaches to span alternative theoretical and methodological approaches in the archaeology of networks.

Different Aspects of Visualizing Social Network Data

A social network is a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange. Social network analysis focuses on the analysis of patterns of relationships among people, organizations, states and such social entities. Social network analysis provides both a visual and a mathematical analysis of humanunderstanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful by visualizing this data, it is possible to greatly improve our understanding. In this paper a state of the art survey of the works done on social network analysis ranging from pure mathematical analyses in graphs to analyzing the social networks in Semantic Web is given. The main goal is to provide a road map for researchers working on different aspects of Social Network Analysis.

Big data analytics for security and criminal investigations

Applications of various data analytics technologies to security and criminal investigation during the past three decades have demonstrated the inception, growth, and maturation of criminal analytics. We first identify five cutting‐edge data mining technologies such as link analysis, intelligent agents, text mining, neural networks, and machine learning. Then, we explore their recent applications to the criminal analytics domain, and discuss the challenges arising from these innovative applications. We also extend our study to big data analytics which provides some state‐of‐the‐art technologies to reshape criminal investigations. In this paper, we review the recent literature, and examine the potentials of big data analytics for security intelligence under a criminal analytics framework. We examine some common data sources, analytics methods, and applications related to two important aspects of social network analysis namely, structural analysis and positional analysis that lay the foundation of criminal analytics. Another contribution of this paper is that we also advocate a novel criminal analytics methodology that is underpinned by big data analytics. We discuss the merits and challenges of applying big data analytics to the criminal analytics domain. Finally, we highlight the future research directions of big data analytics enhanced criminal investigations. WIREs Data Mining Knowl Discov 2017, 7:e1208. doi: 10.1002/widm.1208This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Computer Architectures for Data Mining

Authentication With Block-Chain Algorithm and Text Encryption Protocol in Calculation of Social Network

Community detection is an important aspect of social network analysis, but social factors such as user intimacy, influence, and user interaction behavior are often overlooked as important factors. Most of the existing methods are single classification algorithms; multi-classification algorithms that can discover overlapping communities are still incomplete. In former works, we calculated intimacy based on the relationship between users, and divided them into their social communities based on intimacy. However, a malicious user can obtain the other user relationships, thus to infer other users interests, and even pretend to be the another user to cheat others. Therefore the information users concerned about needs to be transferred in the manner of privacy protection. In this paper, we propose an efficient privacy preserving algorithm to preserve the privacy of information in social networks. First, during expansion of communities on the base of mining seed, in order to prevent others from malicious users, we verify their identities after they send a request. We make use of the recognition and nontampering of the block chain to store the user’s public key and bind to the block address, which is used for authentication. At the same time, in order to prevent the honest but curious users from illegal access to other users’ information, we do not send plaintext directly after the authentication, but hash the attributes by mixed hash encryption to make sure that users can only calculate the matching degree rather than know specific information of other users. Analysis shows that our protocol would serve well against different types of attacks.

Network completion by leveraging similarity of nodes

The analysis of social networks has attracted much attention in recent years. Link prediction is an important aspect of social network analysis and an area of key research within that is the network completion problem, where it is assumed that only a small sample of a network (e.g., a complete or partially observed subgraph of a social graph) is observed and we would like to infer the unobserved part of the network. In a typical network completion problem the standard methods, such as matrix completion, are inapplicable due the nonuniform sampling of observed links. This paper investigates the network completion problem and demonstrates that by effectively leveraging the side information about the nodes (such as the pairwise similarity), it is possible to predict the unobserved part of the network with high accuracy. To this end, we propose an efficient algorithm that decouples the completion from transduction stage to effectively exploit the similarity information. This crucial difference greatly boosts the performance where appropriate similarity information is used. The recovery error of the proposed algorithm is analyzed theoretically based on the richness of the similarity information and the size of the observed subnetwork. To the best of our knowledge, this is the first algorithm that addresses the network completion with similarity of nodes with provable guarantees. Through extensive experiments on four real-world datasets, we demonstrate that (1) leveraging side information in matrix completion by decoupling the completion from transduction significantly improves the link prediction performance, (2) proposed two-stage method can deal with the cold-start problem that arises when a new entity enters the network, and (3) our approach is scalable to large-scale networks.

Balancing the Quantitative and Qualitative Aspects of Social Network Analysis to Study Complex Social Systems

Social Network Analysis (SNA) can be used to investigate complex social systems. SNA is typically applied as a quantitative method, which has important limitations. First, quantitative methods are capable of capturing the form of relationships (e.g. strength and frequency), but they are less suitable for capturing the content of relationships (e.g. interests and motivations). Second, while complex social systems are highly dynamic, the representations that SNA creates of such systems are often static. These limitations can be overcome by balancing a quantita- tive approach to SNA with a qualitative approach. In the article two different approaches that seek this balance are demonstrated. The illustrations show that in this combination quantitative SNA is most useful for revealing system-level patterns, but that a deeper understanding of the mechanisms that produce these patterns is more easily achieved through the interpretation of qualitative data.

국내 통계학 관련 학술지의 인용지수 비교 및 네트워크 분석

최근 들어 연구자의 연구능력평가와 함께 학술지의 내용과 질적 수준에 대한 평가가 중요한 이슈가 되고 있다. 본 연구는 한국데이터정보과학회지를 중심으로 통계학 관련 국내 학술지들이 어떠한 수준의 영향력을 가지고 있는지를 다양한 KCI 인용지수를 이용하여 비교해보고, 상호인용 빈도를 활용하여 학술지 간 연결망을 사회네트워크분석 관점에서 조망해 보았다. 그 결과 다음을 알 수 있었다. 첫째, 자기인용비율이 비교적 높은 것으로 나타났다. 둘째, 수학, 물리학, 화학에 비하여 통계학 관련 학술지들의 영향력지수는 높은 것으로 나타났으나, 타 학술지들에서 통계학 관련 학술지들을 인용한 빈도는 높지 않았다. 셋째, 한국데이터정보과학회지의 경우 연결망분석에서 중심적인 위치를 차지하고 있으나, 노력의 여지는 있어 보인다. Evaluating contents and quality of the journal along with the research ability of researcher is becoming an important issue recently. This research compared level of impact of the journals related to statistics in nation, 'Journal of the Korean Data & Information Science Society'-centric, using various KCI citation index. Moreover, this research surveyed network between the journals in the aspect of social network analysis, using co-citation frequency. From that, the following conclusions were drawn. First, percentage of self-citation was relatively high. Second, even though Statistics journal had higher impact index than the mathematics, physics and chemistry, frequency of citing statistics journal in other journals was not that high. Third, 'Journal of the Korean Data & Information Science Society' serves central role in network analysis, however it seems that more efforts are required.

Neoliberalism, policy advocacy networks and think tanks in the Spanish educational arena: The case of FAES

The way in which societies around the globe are governed is changing towards more flexible and networked forms where the government is not the sole and main responsible actor in the organisation, funding, delivery and evaluation of public services. Analysing the new roles of philanthropic organisations, think tanks and business organisations is a key task in order to understand the expansion of neoliberal ideas and solutions in the field of education policy. Taking as a starting point the set of ideological pillars and discursive practices of the Spanish neoliberal think tank Foundation for Social Studies and Analysis (Fundación para el Análisis y los Estudios Sociales, FAES) and its networks of academics and experts, this article aims to offer a deeper understanding of the processes through which new models of public management and market-based solutions are being promoted and enacted in the Spanish educational arena. To do this, we will experiment with tools derived from what is known as ‘network ethnography’, a new methodological approach that combines aspects of Social Network Analysis with more traditional ethnographic methods.

A polyhedral study of the maximum edge subgraph problem

The study of cohesive subgroups is an important aspect of social network analysis. Cohesive subgroups are studied using different relaxations of the notion of clique in a graph. For instance, given a graph and an integer k, the maximum edge subgraph problem consists of finding a k-vertex subset such that the number of edges within the subset is maximum. This work proposes a polyhedral approach for this NP-hard problem. We study the polytope associated to an integer programming formulation of the problem, present several families of facet-inducing valid inequalities, and discuss the separation problem associated to these families. Finally, we implement a branch and cut algorithm for this problem. This computational study illustrates the effectiveness of the classes of inequalities presented in this work.

A polyhedral study of the maximum edge subgraph problem

Abstract The study of cohesive subgroups is an important aspect of social network analysis. Cohesive subgroups are studied using different relaxations of the notion of clique in a graph. For instance, given a graph and an integer k , the maximum edge subgraph problem consists in finding a k -vertex subset such that the number of edges within the subset is maximum. This work proposes a polyhedral approach for this NP-hard problem. We study the polytope associated to an integer programming formulation of the problem, present several families of facet-inducing valid inequalities, and discuss the separation problem associated to these families.

Darwin and Lotka

Abstract Population was the subject of two major conceptual developments in the second quarter of the 20th century. Both were inspired by biology. Lotka developed a mathematics of evolution in human and other species by analogy to thermodynamic models. His theory followed demographic practice in treating populations as closed units, commonly macro-scale, and in inferring underlying processes of change from aggregate outcomes. In contrast, the - a collaborative product of research in experimental and population genetics, natural history, and related fields of biology - followed Darwin in insisting that close observation of small-scale population processes and local environments is necessary to understand population change. Because gene-environment interactions rely on expanding and contracting networks of individuals, the populations in question are by nature open. Despite the apparent conflict between these positions, the broke new ground in the history of population thought by showing how the two approaches could be combined. Demography, however, moved away from and population biology as a source of theory in the early post-war era, and this conceptual redevelopment of population was scarcely remarked upon. More recently, the tremendous development of genetics has recalled demographers' attention to theory as an inescapable element of modern population thought. This paper provides a historical introduction to mid-20th-century developments in Darwinian population thinking, and the implications of its dual conceptualisation of population for demography. Its potential importance extends beyond the problem of gene-environment interactions to many aspects of social network analysis. 1. Introduction The theory of population proposed in Lotka's two main works was formulated as a contribution to biology. The Elements of Physical Biology (1925) elaborated a general mechanics of evolution in which relations between species are modelled as isolated systems obeying laws analogous to thermodynamics. This was reiterated in Part One of the Theorie analytique des associations biologiques (1934), and human populations were then treated as a special case in Part Two (1939). The period in which Lotka was writing was one of tremendous ferment in biology. The major contemporary breakthrough, which has come to be known as the evolutionary synthesis (Mayr and Provine 1998), was, however, formulated in terms that contrasted sharply to Lotka's. It addressed the dynamics of intra-species variation (Dobzhansky 1937). This focus, emphasizing relationships between individuals making up distinctive sub-populations, rather than species as undifferentiated wholes, was to have a lasting impact on the utility and reception of Lotka's work as a general theory of population for biology. Lotka's method acquired an eminent role only in a sub-field of population ecology, the study of predator-prey relations, where it parallels work by Volterra (Scudo and Ziegler 1978; Kingsland 1985). This marginalisation is reflected in histories of theory, in which the reader looks in vain even for mention of Lotka's two works (e.g. Provine 1971; Mayr 1982; Gould 2002). Lotka's fate as a theorist of population evolution has not troubled demographers, assuming they are aware of it. His papers on American demographics (e.g., 1925 [with L.I. Dublin], 1936) established his pre-eminence in the mathematics of human populations, and no effort was made to translate the Theorie for almost half a century after his death (Lotka 1998). The argument that Lotka formulated the core of demographic theory was consolidated in a series of influential syntheses in the early post-war era: a magisterial survey of the social scientific domain of population research by Hauser and Duncan (1959: 1-117); Ryder's elegant sociological generalisation of Lotka's concept of population, taking account of subsequent developments in cohort analysis (1964); and a summary of the development of modern population thought (Lorimer 1959). …

WebKDD/SNAKDD 2007

In this report, we summarize the contents and outcomes of the recent joint WebKDD/SNAKDD 2007 workshop on Web Mining and Social Network Analysis that was held in conjunction with the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), August 12-15, 2007, in San Jose, California. In 2007, WebKDD was organized for the ninth time in a successful series of workshops on knowledge discovery on the Web. The first SNA-KDD workshop is created to bring together researchers who currently contribute to different aspects of social network analysis, including knowledge discovery and data mining in social network, social network modeling, multi-agent based social network simulation, complex generic network analysis. The joint 9th WEBKDD and 1st SNA-KDD workshop was co-held with KDD 2007 and solicited papers on broad overview subject "Web Mining and Social Network Analysis". We reflect on the results of the workshop, as captured in the invited talk and presentations.