Large Social Networks Research Articles

Parallel Computing for Analysis of Large Scale Social Network

Parallel Computing for Analysis of Large Scale Social Network

Toward a critical theory of science, technology, engineering, and mathematics doctoral persistence: Critical capital theory

AbstractThe proportion of Black, Latinx, and American Native individuals annually earning science, technology, engineering, and mathematics (STEM) doctoral degrees in the United States (US) has been enduringly and inequitably low for decades compared to their White peers. Despite the intransigent connection between race and STEM doctoral outcomes, US STEM education policy documents typically fail to identify racism as influencing this racial inequity. This paper presents critical capital theory (CCT)—an integration of critical race theory, forms of capital, and fictive kinship—to give racism full explanatory power within the context of US STEM doctoral outcomes. CCT proposes that access to large and affluent social networks containing supportive individuals who have knowledge of how to successfully navigate institutions of power is currently core to STEM doctoral success. This access reinforces STEM identity and belonging, but has been and continues to be primarily preserved for White students via an ever‐evolving system of racism. CCT proposes systems supporting STEM faculty’s consistent provision of high‐quality mentorship experiences for all their students—coupled with accountability for providing this mentorship—would result in more equitable STEM doctoral outcomes.

Link transmission centrality in large-scale social networks

Understanding the importance of links in transmitting information in a network can provide ways to hinder or postpone ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. In this work, we propose a new measure based on stochastic diffusion processes, the transmission centrality, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process. We propose a simple algorithmic solution to compute transmission centrality and to approximate it in very large networks at low computational cost. Finally we apply transmission centrality in the identification of weak ties in three large empirical social networks, showing that this metric outperforms other centrality measures in identifying links that drive spreading processes in a social network.

Location- and relation-based clustering on privacy-preserving social networks

Graph clustering has a long-standing problem in that it is difficult to identify all the groups of vertices that are cohesively connected along their internal edges but only sparsely connected along their external edges. Apart from structural information in social networks, the quality of the location-information clustering has been improved by identifying clusters in the graph that are closely connected and spatially compact. However, in real-world scenarios, the location information of some users may be unavailable for privacy reasons, which renders existing solutions ineffective. In this paper, we investigate the clustering problem of privacy-preserving social networks, and propose an algorithm that uses a prediction-and-clustering approach. First, the location of each invisible user is predicted with a probability distribution. Then, each user is iteratively assigned to different clusters. The experimental results verify the effectiveness and efficiency of our method, and our proposed algorithm exhibits high scalability on large social networks.

CONSUMERS’ PERCEPTION OF E-TAILORED FASHION MARKETING – A CROSS-CULTURAL COMPARISON BETWEEN CHINA AND UK

Western social media platforms like Facebook, YouTube, etc are banned in China. In their places, Weibo, WeChat, Youku and more are the main social media channels in China and thus the main battlefields of social marketing for brands entering China Market. WeChat is the largest social network in China, with over 900 million users daily, for nearly all types of services, including booking flights, restaurant table reservation, shopping, paying bills, etc. To cater the needs of the new generation of Chinese digital natives, a mobile app eM++ was developed that creates new customer services and enables tailored fashion marketing. This new mobile app eM++ works well in China, will this be also well received in other countries? This research investigates consumers’ perception of this new fashion e-tailoring e-shopping concept in UK. Will this type of e-tailored services be welcome in UK? Will consumers welcome this type of e-shopping service in UK? Or they prefer the traditional way of tailoring and shopping? Which social media platforms should be used for promotion? The sample population of this research covers both male and females from China and UK aged 18 years old or above with experience of searching and/or buying clothing items online. Convenient sampling and snow-balling sampling methods are used. In UK, recruitment of volunteers for this project will be via emails to colleagues and previous students, as well as via posters of recruiting volunteers for this project posted on campus. Details of the project and experiment will be included in both emails and posters. Volunteered participants are asked to answer a pre-experiment online questionnaire. Based on their answers, suitable participants will be invited to participate the experimental part (which is trying a fashion app and then answer the post-experiment questionnaire). Participants successfully completed the experiment and post-experiment questionnaire will be given their own body measurements with a 3D model in user’s customised shape, as well as a discount coupon for future use when the app is officially launched. This research will have mainly quantitative data analysis, SPSS will be used to analyse the data. There will have a few open-ended questions that qualitative data analysis method will be employed. This research will explore the feasibility of this service in UK and formulate a cross-cultural comparison between China and UK.

Influence maximization in large social networks: Heuristics, models and parameters

Online social networks play a major role not only in socio psychological front, but also in the economic aspect. The way social network serves as a platform of information spread, has attracted a wide range of applications at its doorstep. In recent years, lot of efforts are directed to use the phenomenon of vast spread of information, via social networks, in various applications, ranging from poll analysis, product marketing, identifying influential users and so on. One such application that has gained research attention is the influence maximization problem. The influence maximization problem aims to fetch the top influential users in the social networks. The aim of the paper is to provide a comprehensive analysis on the state of art approaches towards identifying influential users. In this review, we discuss various challenges and approaches to identify influential users in online social networks. This review concludes with future research direction, helping researchers to bring possible improvements to the existing body of work.

Local structure can identify and quantify influential global spreaders in large scale social networks

Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node's global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.

Facebook, Inc.: A Look at Corporate Governance

This case examines Facebook's corporate governance by reviewing information in the company's proxy statement. Students become familiar with the nature and type of information in proxy statements. They also learn how to examine various facets of executive compensation and corporate governance such as the use of stock options and restricted stock to compensate executives, dual class share structures, board composition and characteristics, controlled companies, and classified boards. Excerpt UVA-C-2411 Rev. Aug. 23, 2018 Facebook, Inc.: A Look at Corporate Governance Sarah Jones returned to her desk after the half-hour meeting with her manager, Joe Thomas. She was excited about her first assignment as an analyst at Salomon Partners, an investment research company that sold reports to a variety of clients. Thomas had asked her to write a report on the governance structure at Facebook, Inc. He had recently read an article in the Wall Street Journal suggesting that technology companies were increasingly using multiple classes of shares when going public. He anticipated having to field questions about what that meant for his clients who had invested in, or were thinking about investing in, Facebook, as he knew the company had an interesting governance structure. Thomas wondered what he could learn from Facebook. He asked Jones to investigate, and he suggested she start by reading Facebook's annual proxy statement. Company Description Facebook was a large US social media and social networking company headquartered in Menlo Park, California. According to its website, Facebook's mission was “to give people the power to build community and bring the world closer together. People use Facebook to stay connected with friends and family, to discover what's going on in the world, and to share and express what matters to them.” In addition to the social networking site Facebook.com, the company's products included Instagram, an online community for sharing visual stories using photos, videos, and messages; Messenger and WhatsApp, both messaging applications; and Oculus, a virtual reality platform. The company's revenues, which totaled $ 40.7billion in 2017, came primarily from advertising. The company had more than 25,000 employees at the end of 2017. (Facebook's consolidated balance sheets and consolidated statements of income are presented in Exhibits 1 and 2.) . . .

Scalable Minimum-Cost Balanced Partitioning of Large-Scale Social Networks: Online and Offline Solutions

With the remarkable proliferation of intelligent mobile devices and fast growing broadband wireless technology, social networking is undergoing explosive growth in recent years as more and more users access social networks via mobile platforms. It is often expensive or even impossible to deploy a large online social network (OSN) on a single server. A cost-effective approach is horizontal scaling, where the OSN is partitioned and deployed on a set of low-cost servers. In this research, we study the problem of minimum-cost balanced partitioning of OSNs. Our goal is to achieve the best partitioning by minimizing the total inter-server traffic cost and at the same time balancing the load among servers. Given its NP-hardness, we propose new techniques and explore efficient heuristics to address the problem, especially for extremely large OSNs with an enormous volume of social nodes, social connections, and social data. Our key contributions include a localized approach with $O(\delta ^2)$ complexity to explicitly calculate the projected gain in inter-server traffic cost (named Server Change Benefit (SCB) ). Built upon this technique, we devise two algorithms that offer online and offline solutions to achieving minimum-cost balanced partitioning of OSNs. The online algorithm is fast and highly efficient to process newly arrival individual nodes. The offline algorithm uses the current online result as a starting point. It further reduces inter-server traffic cost by applying relocation and swapping. It employs a merging process to group the nodes according to the social structure and swap the groups with similar size to further reduce the total inter-server traffic cost. We implement both algorithms and evaluate them based on a variety of real-world OSN datasets from Facebook, Arxiv, Gnutella, Amazon, and Twitter. The simulations demonstrate that the proposed algorithms can significantly reduce the execution time by an average of three folds and at the same time yield supreme performance (i.e., inter-server traffic cost) in comparison with existing solutions.

Iterated Greedy algorithm for performing community detection in social networks

The spreading of social networks in our society has aroused the interest of the scientific community in hard optimization problems related to them. Community detection is becoming one of the most challenging problems in social network analysis. The continuous growth of these networks makes exact methods for detecting communities not suitable for being used, since they require large computing times. In this paper, we propose a metaheuristic approach based on the Iterated Greedy methodology for detecting communities in large social networks. The computational results presented in this work show the relevance of the proposal when compared with traditional community detection algorithms in terms of both quality and computing time.

Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks

The increasing access to large social network data has generated substantial interest in the marketing community. However, due to its large scale, traditional analysis methods often become inadequate. In this paper, we propose a sequential sampling enhanced composite likelihood approach for efficient estimation of social intercorrelations in large-scale networks using the spatial model. The proposed approach sequentially takes small samples from the network, and adaptively improves model parameter estimates through learnings obtained from previous samples. In comparison to population-based maximum likelihood estimation that is computationally prohibitive when the network size is large, the proposed approach makes it computationally feasible to analyze large networks and provide efficient estimation of social intercorrelations among members in large networks. In comparison to sample-based estimation that relies on information purely from the sample and produces underestimation bias in social intercorrelation estimates, the proposed approach effectively uses information from the population without compromising computation eciency. Through simulation studies based on simulated networks and real networks, the authors demonstrate significant advantages of the proposed approach over benchmark estimation methods and discuss managerial implications.

Social Capital in Social Media Networks

New social media such as Facebook and Google+ are web-based communication platforms that enable socially meaningful interactions between contacts in the virtual space (Ellison et al. 2014). Studies show that new social media are particularly conducive to social capital development, as they offer its users the possibility of creating heterogeneous, extremely large electronic social networks (Hampton et al. 2011). This article presents the results of a quantitative research study on the social capital and trust of the Lithuanian population in the electronic social networks. The study used an adapted D. Williams’ Internet Social Capital Scale (2006).

Influence Propagation Model for Clique-Based Community Detection in Social Networks

Social media community detection is a fundamental challenge in social data analytics, in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, the majority of research has been based on social networks with static structures. Our findings within large social networks, such as Twitter, show that only a few users have interactions or communications at fixed time intervals. Finding active communities that demonstrate constant interactions between its members comprises a reasonable perspective. Communities examined from this perspective will provide time-variant social relationships, which may greatly improve the applicability of social data analytics. In this paper, we address the problem of temporal interaction-biased community detection using a four-step process. First, we develop a partition approach using an objective function based on clique structure, to enhance the time efficiency of our methodology. Second, we develop an influence propagation model that gives greatest weight to active edges or to inactive edges in close proximity to active edges. Third, we develop expansion-driven algorithms to efficiently find the activity-biased densest community. Finally, we verify the effectiveness of the extended community metric and the efficiency of the algorithms using three real data sets and a case study conducted on Twitter dynamic data set.

Maximum co-located community search in large scale social networks

The problem of k-truss search has been well defined and investigated to find the highly correlated user groups in social networks. But there is no previous study to consider the constraint of users' spatial information in k-truss search, denoted as co-located community search in this paper. The co-located community can serve many real applications. To search the maximum co-located communities efficiently, we first develop an efficient exact algorithm with several pruning techniques. After that, we further develop an approximation algorithm with adjustable accuracy guarantees and explore more effective pruning rules, which can reduce the computational cost significantly. To accelerate the real-time efficiency, we also devise a novel quadtree based index to support the efficient retrieval of users in a region and optimise the search regions with regards to the given query region. Finally, we verify the performance of our proposed algorithms and index using five real datasets.

Who Spread That Rumor: Finding the Source of Information in Large Online Social Networks With Probabilistically Varying Internode Relationship Strengths

We address the problem of estimating the source of a rumor in large-scale social networks. Previous works studying this problem have mainly focused on graph models with deterministic and homogenous internode relationship strengths. However, internode relationship strengths in real social networks are random. We model this uncertainty by using random, nonhomogenous edge weights on the underlying social network graph. We propose a novel two-stage algorithm that uses the modularity of the social network to locate the source of the rumor with fewer sensor nodes than other existing algorithms. We also propose a novel method to select these sensor nodes. We evaluate our algorithm using a large data set from Twitter and Sina Weibo. Real-world time series data are used to model the uncertainty in social relationship strengths. Simulations show that the proposed algorithm can determine the actual source within two hops, 69%–80% of the time, when the diameter of the networks varies between 7 and 13. Our numerical results also show that it is easier to estimate the source of a rumor when the source has higher betweenness centrality. Finally, we demonstrate that our two-stage algorithm outperforms the alternative algorithm in terms of the accuracy of localizing the source.

FSIM: A Fast and Scalable Influence Maximization Algorithm Based on Community Detection

Influence maximization algorithms try to select a set of individuals in social networks that are more influential. The Influence maximization problem is important in marketing and many researchers has researched on it and proposed new algorithms. All proposed algorithms are not scalable and are very time consuming for very large social networks generally. In this paper, a fast and scalable influence maximization algorithm called FSIM is proposed based on community detection. FSIM algorithm decreases number of nodes that must be examined without loss of the operations quality therefore it can find seeds quickly. FSIM can maximize influence in large social networks. Experimental results show FSIM is faster and more scalable than existing algorithms.

Efficient Information Flow Maximization in Probabilistic Graphs

Reliable propagation of information through large networks, e.g., communication networks, social networks or sensor networks is very important in many applications concerning marketing, social networks, and wireless sensor networks. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure. Our evaluation shows that these heuristics lead to high quality solutions, thus yielding high information flow, while maintaining low running time.

Dynamic Detection of Communities and Their Evolutions in Temporal Social Networks

In this paper, we propose a novel community detection model, which explores the dynamic community evolutions in temporal social networks by modeling temporal affiliation strength between users and communities. Instead of transforming dynamic networks into static networks, our model utilizes normal distribution to estimate the change of affiliation strength more concisely and comprehensively. Extensive quantitative and qualitative evaluation on large social network datasets shows that our model achieves improvements in terms of prediction accuracy and reveals distinctive insight about evolutions of temporal social networks.

Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics

Community detection has been extensively studied for various applications, focusing primarily on network topologies. Recent research has started to explore node contents to identify semantically meaningful communities and interpret their structures using selected words. However, links in real networks typically have semantic descriptions, e.g., comments and emails in social media, supporting the notion of communities of links. Indeed, communities of links can better describe multiple roles that nodes may play and provide a richer characterization of community behaviors than communities of nodes. The second issue in community finding is that most existing methods assume network topologies and descriptive contents to be consistent and to carry the compatible information of node group membership, which is generally violated in real networks. These methods are also restricted to interpret one community with one topic. The third problem is that the existing methods have used top ranked words or phrases to label topics when interpreting communities. However, it is often difficult to comprehend the derived topics using words or phrases, which may be irrelevant. To address these issues altogether, we propose a new unified probabilistic model that can be learned by a dual nested expectation-maximization algorithm. Our new method explores the intrinsic correlation between communities and topics to discover link communities robustly and extract adequate community summaries in sentences instead of words for topic labeling at the same time. It is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach, and evaluate the quality of the results by a case study.

Social Network Monitoring for Bursty Cascade Detection

Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks brings value to a wide range of real-life applications, such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this article, we present a general sensor selection solution for different burst detection approaches. We formulate this problem as a constraint satisfaction problem that has high computational complexity. To reduce the computational cost, we first reduce most of the constraints by making use of the fact that bursty cascades are rare among the whole population. We then transform the problem into an Linear Programming (LP) problem. Furthermore, we use the sub-gradient method instead of the standard simplex method or interior-point method to solve the LP problem, which makes it possible for our solution to scale up to large social networks. Evaluating our solution on millions of real information cascades, we demonstrate both the effectiveness and efficiency of our approach.