scholarly journals Content Patterns in Topic-Based Overlapping Communities

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Sebastián A. Ríos ◽  
Ricardo Muñoz
Keyword(s):  
Network Topology ◽  
Online Social Networks ◽  
Virtual Communities ◽  
State Of The Art ◽  
Graph Structure ◽  
Textual Information ◽  
Overlapping Communities ◽  
Network Nodes ◽  
Detection Approach ◽  
Important Challenge

Understanding the underlying community structure is an important challenge in social network analysis. Most state-of-the-art algorithms only consider structural properties to detect disjoint subcommunities and do not include the fact that people can belong to more than one community and also ignore the information contained in posts that users have made. To tackle this problem, we developed a novel methodology to detect overlapping subcommunities in online social networks and a method to analyze the content patterns for each subcommunities using topic models. This paper presents our main contribution, a hybrid algorithm which combines two different overlapping sub-community detection approaches: the first one considers the graph structure of the network (topology-based subcommunities detection approach) and the second one takes the textual information of the network nodes into consideration (topic-based subcommunities detection approach). Additionally we provide a method to analyze and compare the content generated. Tests on real-world virtual communities show that our algorithm outperforms other methods.

scholarly journals State-based targeted vaccination

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Tomer Lev ◽  
Erez Shmueli
Keyword(s):  
Network Topology ◽  
State Of The Art ◽  
Vaccination Strategy ◽  
Network Nodes ◽  
Modern Times ◽  
Vaccination Strategies ◽  
Extensive Evaluation ◽  
Network Epidemiology ◽  
Network Topologies ◽  
Contagion Model

AbstractVaccination has become one of the most prominent measures for preventing the spread of infectious diseases in modern times. However, mass vaccination of the population may not always be possible due to high costs, severe side effects, or shortage. Therefore, identifying individuals with a high potential of spreading the disease and targeted vaccination of these individuals is of high importance. While various strategies for identifying such individuals have been proposed in the network epidemiology literature, the vast majority of them rely solely on the network topology. In contrast, in this paper, we propose a novel targeted vaccination strategy that considers both the static network topology and the dynamic states of the network nodes over time. This allows our strategy to find the individuals with the highest potential to spread the disease at any given point in time. Extensive evaluation that we conducted over various real-world network topologies, network sizes, vaccination budgets, and parameters of the contagion model, demonstrates that the proposed strategy considerably outperforms existing state-of-the-art targeted vaccination strategies in reducing the spread of the disease. In particular, the proposed vaccination strategy further reduces the number of infected nodes by 23–99%, compared to a vaccination strategy based on Betweenness Centrality.

scholarly journals Privacy and Security Issues in Online Social Networks

2018 ◽  
Vol 10 (12) ◽  
pp. 114 ◽  
Author(s):  
Shaukat Ali ◽  
Naveed Islam ◽  
Azhar Rauf ◽  
Ikram Din ◽  
Mohsen Guizani ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Service Providers ◽  
Security And Privacy ◽  
Security Issue ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Security Issues ◽  
Social Sphere

The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

scholarly journals Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Zhou ◽  
Chengdong Wu ◽  
Dali Chen ◽  
Zhenzhu Wang ◽  
Yugen Yi ◽  
...  
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Dictionary Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Critical Role ◽  
Medical Image Processing ◽  
Unified Framework ◽  
Semantic Relationships ◽  
Detection Approach

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

Privacy and Security for Virtual Communities and Social Networks

2013 ◽  
pp. 19-31 ◽  
Author(s):  
Georgios Michaelides ◽  
Gábor Hosszú
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Personal Information ◽  
Rapid Development ◽  
Virtual Community ◽  
Privacy And Security ◽  
Internet Privacy ◽  
Large Audience ◽  
Privacy Risks

The importance of the virtual communities’ privacy and security problems comes into prominence by the rapid development of online social networks. This article presents the multiple threats currently plaguing the virtual world, Internet privacy risks, and recommendations and countermeasures to avoid such problems. New generations of users feel comfortable publishing their personal information and narrating their lives. They are often unaware how vulnerable the data in their public profiles are, which a large audience daily accesses. A so-called digital friendship is built among them. Such commercial and social pressures have led to a number of privacy and security risks for social network members. The article presents the most important vulnerabilities and suggests protection methods and solutions that can be utilized according to the threat. Lastly, the authors introduce the concept of a privacy-friendly virtual community site, named CWIW, where privacy methods have been implemented for better user protection.

Exploring Healthy Connection

2018 ◽  
pp. 171-187
Author(s):  
Jessica L. Moore
Keyword(s):  
Human Health ◽  
Online Social Networks ◽  
Virtual Communities ◽  
New Technologies ◽  
Social Bonds ◽  
Social Network Sites ◽  
Way Of Life ◽  
Future Directions ◽  
Health And Wellbeing ◽  
Health Related

Virtual social connection has become a way of life for many people. The continued implementation of new technologies in social interaction presents an ever-escalating need for researchers and practitioners to understand the implications of mediated interaction and virtual communities on human health and wellbeing. Accordingly, this chapter presents research on the salience of communication and social bonds in relation to human health and wellbeing, explores ways in which individual as well as relational health and wellbeing are affected by the use of social network sites, and argues a case for research on the health-related functions of expressive narratives in virtual settings such as online social networks. Considerations and future directions for research of these issues conclude this chapter.

scholarly journals Graph Convolutional Neural Network for a Pharmacy Cross-Selling Recommender System

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 525
Author(s):  
Franz Hell ◽  
Yasser Taha ◽  
Gereon Hinz ◽  
Sabine Heibei ◽  
Harald Müller ◽  
...  
Keyword(s):  
Recommender System ◽  
Network Optimization ◽  
Deep Neural Networks ◽  
Product Information ◽  
State Of The Art ◽  
Pharmaceutical Product ◽  
Structured Data ◽  
Graph Structure ◽  
Convolutional Network ◽  
Underlying Graph

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance in recommender system benchmarks. Adapting these methods to pharmacy product cross-selling recommendation tasks with a million products and hundreds of millions of sales remains a challenge, due to the intricate medical and legal properties of pharmaceutical data. To tackle this challenge, we developed a graph convolutional network (GCN) algorithm called PharmaSage, which uses graph convolutions to generate embeddings for pharmacy products, which are then used in a downstream recommendation task. In the underlying graph, we incorporate both cross-sales information from the sales transaction within the graph structure, as well as product information as node features. Via modifications to the sampling involved in the network optimization process, we address a common phenomenon in recommender systems, the so-called popularity bias: popular products are frequently recommended, while less popular items are often neglected and recommended seldomly or not at all. We deployed PharmaSage using real-world sales data and trained it on 700,000 articles represented as nodes in a graph with edges between nodes representing approximately 100 million sales transactions. By exploiting the pharmaceutical product properties, such as their indications, ingredients, and adverse effects, and combining these with large sales histories, we achieved better results than with a purely statistics based approach. To our knowledge, this is the first application of deep graph embeddings for pharmacy product cross-selling recommendation at this scale to date.

scholarly journals Measure and compare the convergence time of network routing protocols

2018 ◽  
Vol 21 ◽  
pp. 00013
Author(s):  
Marek Bolanowski ◽  
Tomasz Byczek
Keyword(s):  
Network Topology ◽  
Routing Protocols ◽  
Network Routing ◽  
Convergence Time ◽  
Network Nodes ◽  
The Real ◽  
It Systems ◽  
Traffic Generator ◽  
Time Required

Currently, IT systems require more and more reliability. It can be guaranteed only by using redundancy both in the connectivity and network devices. The authors attempted to measure the convergence time for EIGRP and OSPF routing protocols after link or network nodes failure. Research have been conducted with the real devices and hardware traffic generator. In case of both protocols, failures have been simulated in various places in the network topology. On this basis, time in which tested protocol restored full connectivity for a specific topology with backup links have been precisely determined. Knowledge of the time required to restore the network after a failure can be useful during designing services based on networks with implemented routing. This can improve a tolerance of connectivity interruptions.

The Emergence of Agency in Online Social Networks

2010 ◽  
pp. 102-118 ◽  
Author(s):  
Jillianne R. Code ◽  
Nicholas E. Zaparyniuk
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Emergent Property ◽  
Psychological Processes ◽  
Social Situations ◽  
Group Interactions ◽  
Online Interactions ◽  
Practical Implications

Social and group interactions in online and virtual communities develop and evolve from expressions of human agency. The exploration of the emergence of agency in social situations is of critical importance to understanding the psychology of agency and group interactions in social networks. This chapter explores how agency emerges from social interactions, how this emergence influences the development of social networks, and the role of social software’s potential as a powerful tool for educational purposes. Practical implications of agency as an emergent property within social networks provide a psychological framework that forms the basis for pedagogy of social interactivity. This chapter identifies and discusses the psychological processes necessary for the development of agency and to further understanding of individual’s engagement in online interactions for socialization and learning.

scholarly journals Graph Dilated Network with Rejection Mechanism

2020 ◽  
Vol 10 (7) ◽  
pp. 2421
Author(s):  
Bencheng Yan ◽  
Chaokun Wang ◽  
Gaoyang Guo
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Great Success ◽  
Convolution Kernel ◽  
Graph Structure ◽  
Proposed Model ◽  
Rejection Mechanism ◽  
Laplacian Smoothing ◽  
Graph Neural Networks ◽  
High Level

Recently, graph neural networks (GNNs) have achieved great success in dealing with graph-based data. The basic idea of GNNs is iteratively aggregating the information from neighbors, which is a special form of Laplacian smoothing. However, most of GNNs fall into the over-smoothing problem, i.e., when the model goes deeper, the learned representations become indistinguishable. This reflects the inability of the current GNNs to explore the global graph structure. In this paper, we propose a novel graph neural network to address this problem. A rejection mechanism is designed to address the over-smoothing problem, and a dilated graph convolution kernel is presented to capture the high-level graph structure. A number of experimental results demonstrate that the proposed model outperforms the state-of-the-art GNNs, and can effectively overcome the over-smoothing problem.

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