Link transmission centrality in large-scale social networks

A novel measure of edge centrality in social networks

Burnout is typically viewed as an individual condition, and no link has been identified between burnout and loneliness. To investigate the association of burnout with loneliness and social network degree and centrality. A survey containing the Maslach Burnout Inventory (MBI), a 3-question loneliness scale, and a social connectivity component was sent to residents in a large urban academic medical center internal medicine residency program. The response rate was 77% (95 of 124 residents). We defined significant burnout as MBI subscores of ≥ 27 for emotional exhaustion (EE), ≥ 10 for depersonalization (DP), or both. This was met by 43 (45%), 47 (49%), and 31 (33%) out of 95 respondents, respectively. Those with significant burnout had higher loneliness scores: 5.6 versus 4.5 for EE (P = .002; OR = 1.50; 95% CI 1.15-1.95); 5.4 versus 4.6 for DP (P = .024; OR = 1.33; 95% CI 1.03-1.71); and 5.8 versus 4.6 for both EE and DP (P = .001; OR = 1.54; 95% CI 1.17-2.02). Rating a larger number of coresidents as closer connections on a 5-point Likert scale was not associated with lower burnout scores. No measures of centrality were associated with burnout scores for EE and/or DP. High personal accomplishment subscores on the MBI did correlate significantly with several measures of centrality. Burnout was associated with loneliness in a dose-dependent fashion. Greater sense of personal accomplishment was associated with greater network centrality.

This paper deals with the problem of summarization and visualization of communication patterns in a large scale corporate social network. The solution to the problem can have significant impact in understanding large scale social network dynamics. There are three key aspects to our approach. First we propose a ring based network representation scheme – the insight is that visual displays of temporal dynamics of large scale social networks can be accomplished without using graph based layout mechanisms. Second, we detect three specific network activity patterns – periodicity, isolated and widespread patterns at multiple time scales. For each pattern we develop specific visualizations within the overall ring based framework. Finally we develop an activity pattern ranking scheme and a visualization that enables us to summarize key social network activities in a single snapshot. We have validated our approach by using the large Enron corpus – we have excellent activity detection results, and very good preliminary user study results for the visualization.

Finding structural hole spanners based on community forest model and diminishing marginal utility in large scale social networks

The main aim of Influence Maximization (IM) in a given social network aims is to find set of seeds of group of users or individuals who are responsible for maximum information diffusion or dissemination of information, opinion or ideas. Since decades, Influence Maximization remained an active area of research specifically in analysing Social Networks. Due to the practical and potential importance of this problem, it has been studied in different variations and above if various states of art algorithms and methodologies have been proposed. This paper introduces literature review of various existing and ongoing works in and around IM problem in Large Scale Social Networks (LSSN) focusing on location property. This will help the researchers to understand the existing work on design, methods and algorithms adopted so far.

Automation of data collection using online resources has led to significant changes in traditional practices of social network analysis. Social network analysis has been an active research field for many decades; however, most of the early work employed very small datasets. In this paper, a number of issues with traditional practices of social network analysis in the context of dynamic, large-scale social networks are pointed out. Given the continuously evolving nature of modern online social networking, we postulate that social network analysis solutions based on incremental algorithms will become more important to address high computation times for large, streaming, over-time datasets. Incremental algorithms can benefit from early pruning by updating the affected parts only when an incremental update is made in the network. This paper provides an example of this case by demonstrating the design of an incremental closeness centrality algorithm that supports efficient computation of all-pairs of shortest paths and closeness centrality in dynamic social networks that are continuously updated by addition, removal, and modification of nodes and edges. Our results obtained on various synthetic and real-life datasets provide significant speedups over the most commonly used method of computing closeness centrality, suggesting that incremental algorithm design is a fruitful research area for social network analysts.

Popularity of online social networks provides the chance to make sentiment analysis on every user instead of every document or sentence. And relations between users on social media sites often indicate correlation (negation) between users' opinions. In this work, we study how user's opinion spread in social networks. We employ the data from Tencent.com, the largest social network of China to empirically study the problem. Our work focuses on six different topics including policy, products, brand, sports, movie and politician. We study the distributions of peoples' opinions on different topics and how users' opinions are influenced by those he is following. We propose a graphical model to capture the essence of social network as well as an algorithm to perform semi-supervised learning. The learning algorithm can be used to accurately predict users' sentiment in the social network.

Social Networking activities are still occupying the majority of the time that Internet users are spending in the Web. The generated content and social dynamics represent precious resources that everybody wishes to control. This scenario poses several challenges including the fact that different implementations, technologies, and formats are used to manage web content and social dynamics in heterogeneous, often antagonistic, Social Networking Sites. In order to master this heterogeneity the SocIoS project has defined an API that enables the aggregation of data and functionality made available by different Social Networking Sites APIs and their combination into complex and novel application workflows. However, the dependency on Social Networking Sites does not allow users of the SocIoS API to control the Quality of Service provided by the underlying platforms. In this paper we show how the QoSMONaaS (QoSMONitoring as a Service) component can be used to monitor and evaluate relevant metrics, such as availability and response time of the API calls, that are specified in the Service Level Agreement document. QoSMONaaS has been developed within the context of the SRT-15 project to implement a dependable (i.e. unbiased, reliable, and timely) monitoring of Quality of Service.

We consider here the problem of adding diversity requirements for the results of continuous top-k queries in a large scale social network, while preserving an efficient, continuous processing. We propose the DA-SANTA algorithm, which smoothly adds content diversity to the continuous processing of top-k queries at the social network scale. The experimental study demonstrates the very good properties in terms of effectiveness and efficiency of this algorithm.

Social networks have been part of people's daily life and plenty of users have registered accounts in multiple social networks. Interconnections among multiple social networks add a multiplier effect to social applications when fully used. With the sharp expansion of network size, traditional standalone algorithms can no longer support computing on large scale networks while alternatively, distributed and parallel computing become a solution to utilize the data-intensive information hidden in multiple social networks. As such, synergistic partitioning, which takes the relationships among different networks into consideration and focuses on partitioning the same nodes of different networks into same partitions. With that, the partitions containing the same nodes can be assigned to the same server to improve the data locality and reduce communication overhead among servers, which are very important for distributed applications. To date, there have been limited studies on multiple large scale network partitioning due to three major challenges: 1) the need to consider relationships across multiple networks given the existence of intricate interactions, 2) the difficulty for standalone programs to utilize traditional partitioning methods, 3) the fact that to generate balanced partitions is NP-complete. In this paper, we propose a novel framework to partition multiple social networks synergistically. In particular, we apply a distributed multilevel k-way partitioning method to divide the first network into k partitions. Based on the given anchor nodes which exist in all the social networks and the partition results of the first network, using MapReduce, we then develop a modified distributed multilevel partitioning method to divide other networks. Extensive experiments on two real data sets demonstrate that our method can significantly outperform baseline independent-partitioning method in accuracy and scalability.

Structural features and evolutionary mechanisms of industrial symbiosis networks: Comparable analyses of two different cases

Mobile devices are becoming the largest sensor network around the world. They could be used to collect a large amount of data with little effort and cost which is leading to a promising future for participatory sensing networks or urban sensing. However, privacy concerns of the mobile users are the major inhibitors hindering massive participation. This paper proposes a solution to user privacy preserving problem in a participatory sensing network. Each user is considered as a node in a social network and users are connected through friendship links which are represented as edges on the network. Typically, each user contributes to the participatory system by uploading his/her acquired data to a server. Instead of uploading data to the server directly, we devised a Hot-Potato-Privacy-Protection Algorithm (HP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) in which data is sent to one of the friends of the user and the friend will choose another friend to deliver the data to the next hop. Hopping goes on until some user-defined threshold is reached, then the last user uploads the data to the server. Friend selection is random and the number of hops is also random and independent. HP3 ensures that the probability that the server can make a successful attack on the data owner is no better than where n is the number of mobile users in the system. Therefore, HP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> protects location privacy as well as data ownership privacy of mobile users. We simulate our approach on some large scale social networks and report some findings in the paper. Experiments show that our system achieves privacy protection for each user against the server with tolerable communication overhead.

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.

PurposeThis study aims to analyze social networking and network centrality in the case of community enterprises, which the existing literature has not addressed. To do so, the authors use the survey of community enterprises from the One Tambon One Product (OTOP) entrepreneurship program of Nong Khai border province in Thailand as a case study.Design/methodology/approachSocial network analysis (SNA) is a tool to study and understand the relationship patterns of units of analysis, which can be individual, household, community or production units, and how those units interact through social or economic activities. Network positions are important when identifying the centralization of links in a particular network. If a representative is close to the center of a network, it is possible that the production unit will be able to contact or coordinate with a greater number of other members of the cluster, create knowledge sharing, promote collaboration and then typically demonstrate greater performance.FindingsThe results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Enterprises in partnerships with external agencies are mostly cooperating with branding and marketing dimensions, followed by the design dimension. There is no cooperation regarding production and distribution. Most community enterprises have established at least a one-dimensional network of cooperation with external agencies; only five community enterprises have isolated nodes with no partnerships having been created within this group.Research limitations/implicationsThe study was limited by the surveys having been conducted in a single area and, therefore, can be used only as a case study for this area. Surveys in larger group sizes and in a wider range of areas would lead to results with greater applicability and reliability.Practical implicationsThese results bring to mind policy proposals to increase the competitiveness of community enterprises through the development of social networks as follows: firstly, knowledge should be created with community enterprise operators to understand their supply chains and analyze the strengths and weaknesses and core competencies of their enterprises; and secondly, enterprises should be educated about which agencies can assist businesses at each stage in the value chain system and encouraged to ask for help in adding value at each stage of production.Social implicationsA “OTOP to Business Networking” platform for community and private enterprises should be created with projects/activities that offer venues to exchange business learning and opportunities. Holding meetings among people in a variety of business sectors may help inspire entrepreneurs to innovate to further their businesses, which can lead to networking businesses conducting activities across community enterprises and the private sector for the mutual benefit of producing merchandise for large customers and markets. Learning systems, production techniques and cooperation should be created, as well as opportunities to increase market share and profitability, if this platform is successful.Originality/valueThis paper is the first study, to the best of the authors’ knowledge, to utilize SNA to examine the use of social networking among community enterprises participating in Thailand’s OTOP entrepreneurship program in Nong Khai province. The results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Therefore, the government can play an important role in helping to develop a network of community enterprises with external entities at each stage of the value chain to enhance the competitiveness of each enterprise.

Communication has become a lot easier with the advent of easy and cheap means of reaching people across the globe. This has allowed the development of large networked communities and, with the technology available to track them, has opened up the study of social networks at unprecedented scales. This has necessitated the scaling up of various network analysis algorithms that have been proposed earlier in the literature. While some algorithms can be readily adapted to large networks, in many cases the adaptation is not trivial. In this work, we explore the scaling up of a class of node centrality algorithms based on cooperative game theory. These were proposed earlier as an efficient alternatives to traditional measure of information diffusion centrality. We present here distributed versions of these algorithms in a Map-Reduce framework, currently the most popular distributed computing paradigm. We empirically demonstrate the scaling behavior of our algorithm on very large synthetic networks thereby establishing the utility of these methods in settings such as online social networks.