Social Network Analysis Algorithms Research Articles

Optimizing Graph Theory Algorithms for Social Network Analysis

Social network analysis (SNA) leverages graph theory to understand and visualize the complex relationships and structures within social networks. This research paper explores the optimization of graph theory algorithms tailored for SNA, focusing on efficiency improvements in handling large-scale networks. The study reviews key graph theory concepts, identifies common challenges in SNA, and evaluates various optimization techniques. Practical applications and case studies are presented to demonstrate the impact of these optimizations in real-world scenarios.

Poet-Composer Collaborations in Modern Egyptian Song: A Social Network Analysis Approach to Music History

In this paper I formulate and apply a social network analysis methodology for understanding the history of song on Egyptian radio. Music is a massively relational cultural form, involving interactions among composers, poets, arrangers, conductors, and performers, among others. The reality of music history thus emerges as a complex network of relationships, unfolding and changing over time. Song production, in particular, centers on poet-composer-singer collaborations. Many Arab music histories highlight narratives of the stars, presented in historical and cultural context, but neglect the broader network of productive relationships. However many important musical figures are not celebrities, and the full complexity of the non-linear network can only be grasped holistically, including big data empirical analysis, not pointillistically through case studies of celebrities. Such holistic analysis can reveal surprising emergent, structural patterns that are not apparent in any single narrative. Social network analysis (SNA) offers a powerful suite of tools enabling such an approach, including metrics for centrality and the detection of cohesive subgroups. Starting with a large dataset of songs broadcast on Egyptian radio, I extract a network of poet-composer collaborations, and apply SNA algorithms to reveal its social structure. I then interpret that structure in light of wider socio-cultural and historical factors. In this way, my paper both sheds light on Egypt’s musical history, and supplies a model and method that may be applied, mutatis mutandis, to other musical domains.

Graph Theory and Algorithms for Social Network Analysis

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

Research on Mixed Education Teaching Reform in the Era of Internet plus

Nowadays, the technology of "Internet plus" is developing very rapidly. Its cooperation with education promotes the innovative development of educational reform practice, improves the vitality of education. The cooperation of online and offline education has innovated teaching ideas and changed the teaching situation. It fully enhances students' proactive practical skills, thereby improving their comprehensive quality level, developing new educational concepts, and promoting innovation in new teaching models. Tourism behavior prediction based on social network analysis employs social network analysis techniques to predict the behavior patterns of tourists in different stages of their travel, such as destination selection, itinerary planning, and activity participation. By analyzing the behavior patterns of tourists on social media platforms, social network analysis algorithms can identify the factors that influence their decision-making, such as personal preferences, social influence, and situational context, and generate personalized recommendations to improve their travel experience. For example, if a tourist has shown a preference for outdoor activities on social media, tourism behavior prediction systems can recommend relevant activities and destinations that match their interests. These systems can also incorporate real-time data, such as weather forecasts and traffic conditions, to adjust recommendations accordingly. Overall, tourism behavior prediction based on social network analysis can provide tourists with more personalized and satisfactory travel experiences, while also promoting the development of local tourism industries.

Non-conservative diffusion and its application to social network analysis

Abstract Is the random walk appropriate for modelling and analysing social processes? We argue that many interesting social phenomena, including epidemics and information diffusion, cannot be modelled as a random walk, but instead must be modelled as broadcast-based or non-conservative diffusion. To produce meaningful results, social network analysis algorithms have to take into account differences between these diffusion processes. We formulate conservative (random walk-based) and non-conservative (broadcast-based) diffusion mathematically and show how these are related to well-known metrics: PageRank and Alpha-Centrality, respectively. This formulation allows us to unify two distinct areas of network analysis–centrality and epidemic models–and leads to insights into the relationship between diffusion and network structure, specifically, the existence of an epidemic threshold in non-conservative diffusion. We demonstrate, by ranking nodes in an online social network used for broadcasting news, that non-conservative Alpha-Centrality leads to a better agreement with empirical ranking schemes than conservative PageRank. In addition, we give a scalable approximate algorithm for computing the Alpha-Centrality in a massive graph. We hope that our investigation will inspire further exploration of the applications of non-conservative diffusion in social network analysis.

Research trends on big data domain using text mining algorithms

Abstract Most of the theories have considered big data as an interesting subject in the information technology domain. Big data is a term for describing huge databases that traditional methods in data processing suffer from analyzing them. Recognizing and clustering emerging topics in this area will help researchers whose aim is to work on this interesting subject. Text mining and social network analysis algorithms are utilized for identifying the emerging trends for big data domain. In this study, at first, we gathered the whole papers that are relevant to big data domain and then the word co-occurrence network was created based on the extracted keywords. Then the best clusters were identified and the relationship between keywords was recognized by the association rules technique. In conclusion, some suggestions were mentioned for future studies.

Detecting communities in social networks based on cliques

Social network analysis is an important tool that can be used in many domains. Among the social network analysis algorithms and tools we find the community structure detection. Many community structure detection algorithms have been developed over years, but most of them have a high computational complexity. In this paper we propose a new approach to find a community structure in networks. Our approach is more stable, accurate and effective to find the community structure in networks with high inter-community links. Our method operates in two phases. In the first phase, our method finds all the circuits in order to split the network into small elementary groups. Then (in the second phase)the community structure will be found by merging iteratively the different sub graphs resulting from the first phase by a fusion principle used in clique-based methods. Our method was evaluated on different types of networks. We tested our method on generated-computer and real-world networks. A comparison was held between our method and some known methods of community detection. The results show that our method is very effective in finding communities in networks and saving the time complexity.

On the Algorithms of Identifying Opinion Leaders in Social Networks

The present paper is dedicated to the algorithms of opinion leaders’ identification in social networks on the basis of social network theory and machine learning methods. The main applications of such algorithms for social network analysis are discussed, examples of existing opinion leaders search algorithms and their advantages and disadvantages are given. A modified algorithm for finding the most influential social network nodes, which takes into account not only the position of the node in the network, but also signs of its activity, such as the number of posts, the number of comments and the number of reposts is proposed. The suggested algorithm is more accurate than the algorithms, which are based only on the position of the node in the network. Limitations of using the algorithms as well as possible problems with machine learning application in the field of social network analysis are described.

QMSampler: Joint Sampling of Multiple Networks with Quality Guarantee

Because Online Social Networks (OSNs) have become increasingly important in the last decade, they have motivated a great deal of research on Social Network Analysis (SNA). Currently, SNA algorithms are evaluated on real datasets obtained from large-scale OSNs, which are usually sampled by Breadth-First-Search (BFS), Random Walk (RW), or some variations of the latter. However, none of the released datasets provides any statistical guarantees on the difference between the sampled datasets and the ground truth. Moreover, all existing sampling algorithms only focus on sampling a single OSN, but each OSN is actually a sampling of a complete social network. Hence, even if the whole dataset from a single OSN is sampled, the results may still be skewed and may not fully reflect the properties of the complete social network. To address the above issues, we have made the first attempt to explore the joint sampling of multiple OSNs and propose an approach called Quality-guaranteed Multi-network Sampler (QMSampler) that can jointly sample multiple OSNs. QMSampler provides a statistical guarantee on the difference between the sampled real dataset and the ground truth (the perfect integration of all OSNs). Our experimental results demonstrate that the proposed approach generates a much smaller bias than any existing method. QMSampler has also been released as a free download.

Hiding in plain sight

With just a handful of modifications to their social networks, individuals and groups can reduce the likelihood that they will be detected by others using standard social network analysis algorithms.

Graph Theory and Network Algorithms for Social Network Analysis

Graph Theory and Network Algorithms for Social Network Analysis

The application of social network analysis algorithms in a system supporting money laundering detection

Criminal analysis is a very complex task requiring to process huge amounts of data coming from different sources such as billings and bank account transactions in order to gain knowledge useful for an investigator. In order to support human analytic capabilities, dedicated software tools are needed, and therefore Money Laundering Detection System (MLDS) was proposed as one of such tools in our previous paper. In this paper, the social network analysis component for this system is presented. The component makes it possible to use data from bank statements and the National Court Register and construct and analyze social networks during an investigation into money laundering cases. The system can assign roles to persons from the network and allows for analysis of connections between them. The paper also includes results of experiments aimed at investigating the performance of the implemented algorithms and the correctness of the analysis.

Rethinking centrality: The role of dynamical processes in social network analysis

Many popular measures used in social network analysis, including centrality, are based on the random walk. The random walk is a model of a stochastic process where a node interacts with one other node at a time. However, the random walk may not be appropriate for modeling social phenomena, including epidemics and information diffusion, in which one node may interact with many others at the same time, for example, by broadcasting the virus or information to its neighbors. To produce meaningful results, social network analysis algorithms have to take into account the nature of interactions between the nodes.In this paper we classify dynamical processes as conservative and non-conservative and relate them to well-known measures of centrality used in network analysis: PageRank and Alpha-Centrality. We demonstrate, by ranking users in online social networks used for broadcasting information, that non-conservative Alpha-Centrality generally leads to a better agreement with an empirical ranking scheme than the conservative PageRank.

Social network analyses in organizations: challenges and approaches for studying work networks

In the past years, social network analysis (SNA) techniques got disseminated to a large number of fields, such as, disease spread, animal group behaviour, organization dynamics, and the like. A particular use for SNA is to help in understanding how organizations work. It focuses not only on issues relative to how to manage organizations, but how people accomplish their work and share their knowledge by means of their work networks. Understanding how people actually work in an organization helps managers to diagnose gaps in communication and collaboration, and consequently improve work efficiencies and effectiveness. In this paper we present some challenges in using SNA techniques to understand how work is done in a large IT service delivery organization. Using SNA it is possible to attain a better understanding of the working environment as well as diagnose some of the issues workers face everyday. Some major technical and methodological challenges are: to define the importance of each media and that there are a few SNA algorithms that can work with multi-edges or weighted graphs. Thus, a computational challenge in organization data rests on how to merge the different media available and how to measure the importance and consequently give value for e-mail, instant messenger, traces of work, hierarchical structure, collaborative documents, intern communities, and face-to- face communications, for example. In sum, this paper discusses some of these challenges that emerged in our exploration of work networks, and suggests a few techniques and approaches to tackle them.

A random graph generation algorithm for the analysis of social networks

Social network analysis (SNA) is a rapidly growing field with numerous applications in industry and government. However, the field still lacks means to generate random social networks with certain desired properties, thus inhibiting their ability to test new SNA algorithms and metrics. Available random graph generation algorithms suffer from tendencies to generate disconnected graphs and sometimes induce undesirable network properties. In this paper, we present an algorithm, the prescribed node degree, connected graph (PNDCG) algorithm, designed to generate weakly connected social networks. Extensions to the PNDCG algorithm allow one to create random graphs that control the clustering coefficient and degree correlation within the generated networks. Empirical test results demonstrate the capability of the PNDCG algorithm to produce networks with the desired properties.

Statistical methods for social networks

Network analysis and modeling have received considerable attention in recent times and require the solution of intricate mathematical problems, e.g. the problem of enumeration graphs under specified conditions, finding the largest complete graph and so on. Even though a lot of well-known algorithms have been proposed, some problems are still challenges from a computational point of view and their fast solutions are thus of great practical interest. This paper focuses on some parallel algorithms for social network analysis. In particular, a review of some existing parallel algorithms is carried out and a new parallel algorithm is proposed for parameters estimation in Exponential Random Graph Models.

Small world models for social network algorithms testing

Social networks have small-world property, hierarchical community structure, and some other properties. This paper proposes models of networks with these properties and algorithm for community structure recognition. The models are useable for testing effectiveness and efficiency of different algorithms for social network analysis.