Optimizing Graph Theory Algorithms for Social Network Analysis

Community detection is crucial in the analysis of social networks. Its main goal is to find groups of users densely connected among them, and sparsely connected between themselves. There are so many algorithms which makes it difficult for researchers to choose the best one for a specific dataset. In this paper, we provide a thorough comparison between five Community Detection (CD) algorithms- Girman-Newman (GN), Clauset-Newman-Moore (CNM), Label Propagation Algorithm (LPA), Louvain and Leiden. To evaluate in real social network datasets like Zachary's Karate Club, Dolphin networks and bigger ones such as Facebook, Twitter, LinkedIn along with citation networks we used different metrics modularity and NMI. We employed Normalized Mutual Information (NMI) to quantify the agreement of detected communities with their true ground-truth score and modularity for evaluating the overall quality of partitions given by diverse methods. Our experiments show that greedy and modularity-optimization algorithms are particularly well-suited. Notably, the Leiden algorithm had a better modularity value than Louvain (Q = 0.9141 and Q = 0.9051 of LinkedIn Network respectively) in most of the dataset. The NMI plot provided more explanation about Clauset-Newman-Moore (CNM), Louvain and Label Propagation Algorithm (LPA)which are in good agreement on community detection and their NMI score. These results would allow us to choose more rationally among the different community detection algorithms for social network analysis, by providing accurate quantitative benchmarks.

Contributors

The current work emphasizes on a Taxi rental company which possess 30 cars. In an effort to benchmark the company’s performance and functionality/usability system, the owner of the company decided to utilize the cars as much as possible in such a way to avoid any Taxi remaining in an idle/inactive status. The company’s system typically was consisted of the following steps as follows: a Taxi car is usually used by a of two different drivers within 24 hours so as the first driver takes care of the morning half-day, while the other one takes care of the night half-day. Doing this can help the company to maximum its monetization process leading to optimum revenue and profits. However, one of the problems associated with the current system is that, in case any of the driver pairs will not be able to come to work punctually, then this is going to affect the overall time scheduling of the driving plan for that day leading to time conflict and loss of money for the company. Accordingly, the selection of the appropriate of drivers is crucial for the owner of the company. To solve these issues and in order to address the above-mentioned problems, a Process Mining technique based on the Social Network Analysis algorithm was applied and used with the intention of better analyzing and investigating the behavior of the drivers so as to select the best pair of drivers for the relevant working days. Subsequently, by using the resulting/generated Social Network graphs/models, the owner of the company was capable of simulating and illustrating the relationships and communicational dependencies amongst the drivers. Due to the fact that the company was using a very traditional way of data collection, therefore, the data was captured and stored manually within a paper-based approach. Nevertheless, this work can provide groundwork for further and future studies and research in such a way that several Process Mining techniques (including Social Network Mining methods) can be applied in versatile scenarios and situations whereas the data is typically captured, gathered and stored manually.

Classical Algorithms for Social Network Analysis: Future and Current Trends

Pregel is a system for large scale graph processing developed at Google. It provides a scalable framework for running graph analytics on clusters of commodity machines. In this paper, we present several important undirected graph algorithms for social network analysis which fit within this framework. We discuss various graph componentisation methods, diameter estimation, degrees of separations, along with triangle, k-core and k-truss finding and computing clustering coefficients. Finally we present some experimental results using our own implementation of the Pregel framework, and examine key features of the general framework and algorithmic design.

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

Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through SNA is the disease transmission path of Middle East respiratory syndrome (MERS). Purpose: To determine key roles using structure holes of SNA for further improvement, and to show the SNA advantage over traditional classic test theory. Methods: Data were records regarding 443 adult mentally retarded residents who were infected with amoebiasis and distributed in 10 houses in past 10 years. A series of intensive mass screenings and treatment interventions were conducted. Structure holes were applied to verify the efficacy of determining key roles and strong associations for the domains of interest in a network and compared with the result obtained from the traditional Chi-square statistics. Results: The classification of key roles in a network (e.g., with which year the residency room with amoebiasis cases has strongly association) can be effectively discriminated through the structure holes of SNA. Though the result is similar to the traditional Chi-square statistics, the structure holes can release much more useful and valuable information for further investigation. Conclusions: Because of advances in computer technology, the number of healthcare studies for the group classification and association assertion continues to increase and benefit comparisons of data if structure holes of SNA are applied.

This paper introduces a constructive approach to online social networks analysis. We suggest a conceptual model of a social network, formulate major problems of analysis and control in social networks, describe methods and algorithms for activity analysis in social networks, as well as technologies for social networks monitoring and analysis.

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

This chapter presents the terminology and concepts of graph theory , and describes basic matrix operations that are used in social network analysis. Both graph theory and matrix operations have served as the foundations of many concepts in the analysis of social networks (Hage and Harary 1983; Harary, Norman, and Cartwright 1965). In this chapter, the notation presented in Chapter 3 is used, and more concepts and ideas from graph theory are described and illustrated with examples. The topics covered in this chapter are important for the methods discussed in the remaining chapters of the book, but they are especially important in Chapter 5 (Centrality, Prestige, and Related Actor and Group Measures), Chapter 6 (Structural Balance, Clusterability, and Transitivity), Chapter 7 (Cohesive Subgroups), and Chapter 8 (Affiliations, Co-memberships, and Overlapping Subgroups). We start this chapter with a discussion of some reasons why graph theory and graph theoretic concepts are important for social network analysis. We then define a graph for representing a nondirectional relation. We begin with simple concepts, and progressively build on these to achieve more complicated, and more interesting, graph theoretic concepts. We then define and discuss directed graphs, for representing directional relations. Again, we begin with simple directed graph concepts and build to more complicated ideas. Following this, we discuss signed and valued graphs. We then define and discuss hypergraphs, which are used to represent affiliation networks.

Background Online social networks share similar topological characteristics as real-world social networks. Many studies have been conducted to analyze the online social networks, but it is difficult to link human interests with social network software design. Purpose The goal of this work is to propose a methodology involving the analysis of human interactions for use in designing online social network software. Methods We propose a novel use of social network analysis techniques to elicit requirements in order to design better online Social network-based software. The validation case study involved the collection of real-world data by means of a questionnaire to perform a network design construction and analysis. The key idea is to examine social network to help in the identification of behaviors and interests of people for better software requirements elicitation. Results The validation case study demonstrates how unexpected centrality measures can emerge in real world networks. Our case study can thus conducted as a baseline for better requirement elicitation studies for online social network software design. This work also indicates how sociometric methods may be used to analyze any social domain as a possible standard practice in online social network software design. Overall, the study proved the effectiveness of the proposed novel methodology for the design of online social network software. The methodology specifically improves upon traditional methods for software design by involving social network modeling and analysis to first study the behavior and elicit requirements to develop more resilient online social network sites.

In recent decades, social network analysis arise as a powerful tool for describing social competitive systems. Vertices, or entities represent states or nodes or agents, while edges represent relations between different nodes. A graph theory is a suitable way for simulating complicated systems. This paper presents framework and architecture to improve system performance using social network analysis. System crawling then web blog analysis is a methodology to clustering and exploring community nodes. Graph and semantic network used for simulate agents and edges. The proposed architecture involves two consecutive modules affecting system performance. Clustering is a first module which simulate vertices and it's edges with user supervised for filtering nodes and attribute , while supervised and machine learning module stimulate the agents for optimal path and performance under complicated environment circumstances. Data prepared from social networks, relations extracted, and crawling with breadth first search with some small graphs as a base are maintained. The proposed framework affect the performance of learning in university as a sample of community through analyzing and maintaining relations and features enhancement which are achieved via supervised machine learning. The proposed system can be implemented to a various social institutes or organizations, using its webs or blogs with different queries from interviews and emails to enhance its performance.

The social network model is powerful enough to provide for the analysis and study of a variety of application domains from daily life, including health care and health informatics. After the widespread appearance of automated tools capable of deriving and analyzing social networks, social network analysis (SNA) and mining in the health care domain has recently received considerable attention for its key role in understanding how various bodies within the health care system form communities and how they are socially connected with each other. This understanding helps enhance the organizational structures and process flows, among others. In this article, we show how SNA techniques can solve issues in the medical referral system in the Canadian health care system and the like, by analyzing the social network of general practitioners (GPs) and specialists (SPs). One of the main targets is to optimize the communication between GPs and SPs with hopes of decreasing the waiting time of patients to be seen by SPs. Various SNA and mining techniques are described and analyzed, backed by reporting some experimental results.

Although social networks and creativity have been studied together in the organizational and sociology literature, it has received less attention within the dominant creativity journals. The current chapter discusses social network analysis for creativity research. The chapter reviews the basic building blocks of social networks (nodes and edges) and the mathematical foundations of social network analysis (graph theory and matrix algebra). Key considerations of defining network boundaries are discussed for collecting social network data. Then, two commonly used measures and terms, centrality and subgroups, in the social network literature are defined. The chapter provides a brief overview of network visualization and available software packages for conducting social network research. The chapter concludes by highlighting the types of research questions that can be answered using social network analysis, presenting selected published studies as examples along with their findings.

Conflict among stakeholders is a familiar challenge to natural resource managers and researchers. Fostering trust and collaboration among diverse stakeholder groups is, therefore, a primary goal for natural resource conservation. One tool often used to understand stakeholder relationships and to foster collaborative conservation is social network analysis (SNA), a method that identifies patterns in social relationships among members of a population using networks and graph theory (Scott 2017). Through an explanatory sequential mixed-methods approach, this study applied SNA to better understand social dynamics among six stakeholder groups associated with Utah's Bonneville Salt Flats (Bonneville; USA). We sought to (1) build social network models (i.e., sociograms) depicting Bonneville-related social interactions among stakeholders, (2) identify potentially influential individuals (i.e., key players) in Bonneville's stakeholder network; and engage these key players in (3) 'member-checking' social interaction trends gathered during the data collection year, and (4) discussing perceptions of their network's influential social dynamics. Sharing SNA data and sociograms through semi-structured qualitative interviews with key players verified four seasons' worth of social interaction trends within and among Bonneville stakeholder groups. These conversations also evoked key players' reflection on social power dynamics, social network evolution, the influence of research into the Bonneville social network, and introspection about social connections therein. These emergent themes support applying SNA and qualitative interviews with key players in natural resource social networks to yield valuable information for managers who seek to foster collaboration while avoiding or abating resource-related conflict among stakeholder groups.