The complexity of crime network data: a case study of its consequences for crime control and the study of networks.

Social network analysis and visualization is an active area of study but good organizations of social network information are lacking. This paper proposes a domain ontology model focusing on social network information, which abstracts the impersonal existences in social network information domain into some primary ontologies. According to the requirements of social network structure analysis, we propose a Subgroup Analysis Layout (SAL) algorithm based on domain ontology model. SAL algorithm analyzes the subgroups through the analysis of roles and key attributes. Then the results of subgroup analysis are used to improve the force directed layout algorithm in analyzing and visualizing the structure of social network. Results with the case of terrorist information demonstrate its advantages. A fine solution is to organize the social network data with domain ontology, and then develop layout algorithms based on it. Ontology is a formal explicit specification of a shared conce- ptualization(2). With the help of domain Ontology, we can organize the information and standardize the definitions fine in the field of social network analysis and visualization. Therefore, methods of social network analysis and new needs of social network visualization or analysis can be defined easily. In this paper, we propose a domain ontology model for field of social network visualization and analysis which abstract the imper- sonal existences in the field into some primary ontologies. To overcome the disadvantages of traditional force directed layout algorithms in analyzing and visualizing the structure of social network, we propose the Subgroup Analysis Layout (SAL) algorithm based on our domain ontology model.

This paper amalgamates the field of positive psychology and social network analysis to explore what are the character strengths and virtues of individuals with high centrality measures within a close group of adolescent females. Research in the field of social network analysis in the last few decades has given a good understanding of different centrality measures. Today, we know what does an individual with high degree centrality, high betweenness centrality etc. signify in a group, this paper goes a step further, we attempt to find the correlation between individuals with high centrality measures and their character strengths. We analyze the friendship relationship of three different close groups of adolescent females studying in an undergraduate class. We construct the network of each class based on the friendship status and measure in-degree centrality, and betweenness centrality. The individuals with high centrality measures were then asked to undergo VIA character strength analysis. To investigate the correlation we quantified the character strength of the adolescent females with high centrality measures into numeric values using inverse transform and weighted sum technique. Our results show that an individual with highest in-degree centrality have kindness as one of her most prominent character strength, and individual with highest betweenness centrality have honesty, fairness and leadership as her prominent character strengths. Since an individual can be trained for particular character strengths, this correlation can help in training adolescent females (and may be extended to other groups) for specific role in an organization and society as a whole.

This paper amalgamates the field of positive psychology and social network analysis to explore what are the character strengths and virtues of individuals with high centrality measures within a close group of adolescent females. Research in the field of social network analysis in the last few decades has given a good understanding of different centrality measures. Today, we know what does an individual with high degree centrality, high betweenness centrality etc. signify in a group, this paper goes a step further, we attempt to find the correlation between individuals with high centrality measures and their character strengths. We analyze the friendship relationship of three different close groups of adolescent females studying in an undergraduate class. We construct the network of each class based on the friendship status and measure in-degree centrality, and betweenness centrality. The individuals with high centrality measures were then asked to undergo VIA character strength analysis. To investigate the correlation we quantified the character strength of the adolescent females with high centrality measures into numeric values using inverse transform and weighted sum technique. Our results show that an individual with highest in-degree centrality have kindness as one of her most prominent character strength, and individual with highest betweenness centrality have honesty, fairness and leadership as her prominent character strengths. Since an individual can be trained for particular character strengths, this correlation can help in training adolescent females (and may be extended to other groups) for specific role in an organization and society as a whole.

A criminal network is a kind of social networks with both secrecy and efficiency. The hidden knowledge in criminal networks can be regarded as an important indicators for criminal investigations which can help finding the criminal's relationship and identifying suspects. However such criminal network analysis has not been studied well in an applied way and remains primarily a manual process. To assist investigators to find criminals relationship, criminal leader, and identify suspicious guys of a conspiracy, we built a comprehensive indicator model using methods developed in the field of Social Network Analysis (SNA). A simulation is done on a large office from open source reports and the ranked list with respect to comprehensive indicator indicates that we provide a reasonable ranking based on the proposed model.

Today, any online social media collects a huge volume of data not just about who is linked with whom (aka link data) but also, about who is interacting with whom (aka interaction data). The presence of both variety and volume in these datasets pose new challenges, and thereby opportunities for the field of social network analysis (SNA). Traditionally, SNA techniques are designed to work only with the link data. Recently, there have been some attempts to analyze link data in conjunction with interaction data. In this paper, we advance this research agenda further by introducing a notion called signature of a social network and propose an efficient approach to compute it. The signature of a social network is essentially a sparse subgraph of the original social network such that it succinctly captures key information contained within the data sources (both linked and interaction data). The signature of a social network need not be unique. The value behind computing such a signature stems from the fact that once computed, any subsequent SNA (e.g. community detection, influence propagation, etc.) becomes much faster while not compromising much with quality. The concept of importance weights of the edges has been the guiding principle for us behind the idea of signature of a social network. In our approach, we start with deriving importance weights of the edges based on the information contained in these data sources. Next, we apply a novel graph sparsification technique to generate signature of the given social network by dropping edges that are not so informative. We demonstrate the efficacy of the signature of social network with an application to community detection on certain well-known social network datasets such as Digg, Youtube, Epinions, DBLP, and Amazon. We obtained effective community detection results on these datasets using our proposed approach while achieving about 40 times speed-up.

Background/Objectives: Social Networking has been entertaining people for sharing their common ideas and proposals which are analyzed through social relations among them. The problem in the field of social network analysis is the absence of adequate computing resources to handle huge amount of data on World Wide Web. Therefore, users are unable to gather needed information correctly and thereby, the aim is to locate right information at the right time and delivering it to distinct group of people. Methods: Present paper gives the insight into the existing deployment of social network analysis and various ranking techniques which have been devised by various researchers for the social networking capabilities over the network. In order to accomplish the aim, virtual environment can be created for social network analysis. This analysis can be performed by various mining methods such as opinionmining, expert mining, etc. and ranking techniques like object average rating, neighbour variance rating, random rating and many more. Although these techniques optimize the information overload problem accordingly, still there is a need for expert identification. Findings: The future enhancement for social network analysis includes collaborative thinking. Social Network Analysis gathers people having similar interest by creating collaboration among users. This collaboration leads to resource sharing in an efficient manner after the creation of virtual environment. Furthermore, the field of social network analysis may take a turn to link analysis and its various algorithms like Page Rank, Weighted Page Rank and Weighted Page Content Rank which will further help in finding the expert and enhances the information effectively. Application/Improvements: The application to social network analysis is to discover the network of innovators in a regional economy, enhancing dark web analysis and spam behaviour detection. The arduous task of expert identification is an upcoming trend that can be implemented through virtual environment. Keywords: Expert Mining, Interest Mining, Social Network, Virtual Community, Web 2.0

There have been numerous attempts at the aggregation of attributes for relational data mining. Recently, an increasing number of studies have been undertaken to process social network data, partly because of the fact that so much social network data has become available. Among the various tasks in link mining, a popular task is link-based classification, by which samples are classified using the relations or links that are present among them. On the other hand, we sometimes employ traditional analytical methods in the field of social network analysis using e.g., centrality measures, structural holes, and network clustering. Through this study, we seek to bridge the gap between the aggregated features from the network data and traditional indices used in social network analysis. The notable feature of our algorithm is the ability to invent several indices that are well studied in sociology. We first define general operators that are applicable to an adjacent network. Then the combinations of the operators generate new features, some of which correspond to traditional indices, and others which are considered to be new. We apply our method for classification to two different datasets, thereby demonstrating the effectiveness of our approach.KeywordsSocial NetworkSocial Network AnalysisCentrality MeasureBetweenness CentralityStructural HoleThese 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.

In this paper, we analyzed the structure of the South Korean automotive industry using social network analysis (SNA) metrics. Based on the data collected from 275 companies, a social network model of the supply network was constructed. Centrality measures in the SNA field were used to interpret the result and identify key companies. The results show that SNA metrics can be useful to understand the structure of a supply network. The most significant contribution of this research is that this is the first trial on applying SNA methods to large scale supply networks for an entire automotive industry of a country.

Purpose We analyzed the structure of a community of authors working in the field of social network analysis (SNA) based on citation indicators: direct citation and bibliographic coupling metrics. We observed patterns at the micro, meso, and macro levels of analysis. Design/methodology/approach We used bibliometric network analysis, including the “temporal quantities” approach proposed to study temporal networks. Using a two-mode network linking publications with authors and a one-mode network of citations between the works, we constructed and analyzed the networks of citation and bibliographic coupling among authors. We used an iterated saturation data collection approach. Findings At the macro-level, we observed the global structural features of citations between authors, showing that 80% of authors have not more than 15 citations from other works. At the meso-level, we extracted the groups of authors citing each other and similar to each other according to their citation patterns. We have seen a division of authors in SNA into groups of social scientists and physicists, as well as into other groups of authors from different disciplines. We found some examples of brokerage between different groups that maintained the common identity of the field. At the micro-level, we extracted authors with extremely high values of received citations, who can be considered as the most prominent authors in the field. We examined the temporal properties of the most popular authors. Research limitations The main challenge in this approach is the resolution of the author’s name (synonyms and homonyms). We faced the author disambiguation, or “multiple personalities” (Harzing, 2015) problem. To remain consistent and comparable with our previously published articles, we used the same SNA data collected up to 2018. The analysis and conclusions on the activity, productivity, and visibility of the authors are relative only to the field of SNA. Practical implications The proposed approach can be utilized for similar objectives and identifying key structures and characteristics in other disciplines. This may potentially inspire the application of network approaches in other research areas, creating more authors collaborating in the field of SNA. Originality/value We identified and applied an innovative approach and methods to study the structure of scientific communities, which allowed us to get the findings going beyond those obtained with other methods. We used a new approach to temporal network analysis, which is an important addition to the analysis as it provides detailed information on different measures for the authors and pairs of authors over time.

An overview of the field of social network analysis (SNA) is presented with a view to improving accessibility of SNA processes and tools for both academic and business users. A brief history of the multidisciplinary origins of the discipline is provided followed by a characterization of the current state of affairs in SNA. Key issues are discussed with respect to data collection, visualization and analysis as well as the lack of standards for tools and technologies for SNA processes, weak links to business goals addressed by SNA, community of practice for SNA professionals, and ethics and incentives for non-academic participants. A number of recommendations are suggested as fruitful avenues to pursue in the near future to help others who, like the authors, embarked upon SNA solutions for the first time.

Graph theory and algorithms play a crucial role in the analysis of social networks, which are complex systems composed of individuals or entities connected by various types of relationships. This paper provides an overview of graph theory concepts and algorithms commonly used in social network analysis. It discusses how these tools can be applied to understand the structure and dynamics of social networks, identify key actors or communities, and analyze information flow and influence propagation. The paper also highlights some of the challenges and future directions in the field of social network analysis using graph theory. The study of graph theory and algorithms for social network analysis is of significant importance due to its wide-ranging applications in various fields. Understanding the structure and dynamics of social networks can provide valuable insights into human behavior, information diffusion, organizational dynamics, and the spread of diseases or ideas. By applying graph theory concepts such as centrality measures, community detection algorithms, and link prediction techniques, researchers can uncover hidden patterns, identify influential nodes or groups, and predict future interactions in social networks. This knowledge can be used to design more effective strategies for marketing, public health interventions, social media management, and other areas where understanding and influencing human behavior are critical. Versatile with distinctive options. MOORA is a new optimization approach that has been proposed. This objective method signifies a matrix of potential replies, but it also proposes better policies based on the rates employed. Well established. For comparison, the reference point method is another multi-objective optimization technique. After several competitions, this proved to be the best pick among the methods. From the result network models is in first position whereas Graph density is in 5th position

Analytical methods for customer relationship management (CRM) have gained increasing importance in today's businesses. Some industry sectors such as the telecommunication industry accumulate huge amounts of data not only about the usage behaviour of individual customers, but also about how customers interact. In addition to traditional data mining and statistical techniques, methods from the field of social network analysis (SNA) are essential to leverage this special set of data. For example, call detail records of telephone operators can be used to evaluate the network of customers and derive measures for the influence of persons in such a network. This information is relevant to viral marketing, as well as various other forms of advertising and campaign management. Research in network analysis has led to a number of different centrality measures, which are potentially useful statistics for such purposes. In this paper, we compare different centrality measures based on a variety of different network topologies and model assumptions

The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. Firstly, new sources (e. g. online social networks, published databases) constantly become available for gathering, analyzing, and interpreting data. Thus, new sources should be modelled and embedded in existing data structures maintaining logical consistency. Secondly, new techniques and underlying algorithms are also constantly being developed. Therefore, analysis results should be integrated with source data, and metamodels that describe the integration should be adaptable and extensible. Thirdly, the dynamism of semi-structured objects entails constant changes in knowledge models produced by domain experts and knowledge engineers. Considering that the same data could be used by different domain experts it is crucial not only to support traceability of changes in models but also to ensure independence of expert interpretation of these models. The analysis of existing approaches to information integration shows lack of solutions implementing traceability of changes. This paper introduces a novel approach to information integration based on ontological and production knowledge models to fill this gap. A conceptual description of the approach and an underlying set-theoretical model are given. The main difference of the given approach from the existing ones is the uniformity to the integration of different kinds of ontologies as well as different versions of ontologies using rule-based model of ontological mappings, which is demonstrated by the example of solving the special case of the problem of identifying key users (so-called bridges) in social networks.

Social network analysis (SNA) is an approach concerned with analysing networks of relations and interactions among a defined set of actors. In recent years, SNA has become known as a useful tool for analysing a wide range of criminal networks, including networks of serious financial crime. However, using SNA in the study of crime is hindered by the aim of actors involved in these to conceal their interactions, making data collection complicated. These complications stem from issues with data availability, validity and reliability. To tackle these issues, we first introduce a framework for thinking about six aspects of network data collection: nodes, ties, attributes, levels, dynamics and context. In the light of this framework, we subsequently review three types of data sources usable for analysing financial crime networks in the context of the United Kingdom. These data sources are documents accompanying Deferred Prosecution Agreements, enforcement case files and commercial transaction data. We illustrate the contents of each of these data sources together with their potential for extracting network data and the types of conclusions that can be drawn through analysing them. These data sources share common problems in being of a secondary non-scientific nature and being prone to contain missing information. In conclusion, we illustrate further uses of SNA and possible extensions of the introduced data sources to other types of criminal networks and jurisdictions beyond the United Kingdom.

The connection between theory and data is an iterative one. In principle, each is informed by the other: data provide the basis for theory that in turn generates the need for new information. This circularity is reflected in the notion of abduction, a concept that focuses on the space between induction (generating theory from data) and deduction (testing theory with data). Einstein, in the 1920s, placed scientific creativity in that space. In the field of social network analysis, some remarkable theory has been developed, accompanied by sophisticated tools to develop, extend, and test the theory. At the same time, important empirical data have been generated that provide insight into transmission dynamics. Unfortunately, the connection between them is often tenuous and the iterative loop is frayed. This circumstance may arise both from data deficiencies and from the ease with which data can be created by simulation. But for whatever reason, theory and empirical data often occupy different orbits. Fortunately, the relationship, while frayed, is not broken, to which several recent analyses merging theory and extant data will attest. Their further rapprochement in the field of social network analysis could provide the field with a more creative approach to experimentation and inference.