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This research studies community detection in multiplex dark networks. Our method seeks to intelligently select appropriate layers for aggregation to approximate communities in the whole network, while reducing the impact of over-modeling the network. Community evolution is explored as layers of different types of information are added to the partial picture of the network. We determine the set of...
Social media users often find it difficult to make appropriate access control decisions which govern how they share their information with a potentially large audience on these platforms. Community detection algorithms have been previously put forth as a solution which can help users by automatically partitioning their friend network. These partitions can then be used by the user as a basis for making...
We address a problem of extracting functionally similar regions in urban streets regarded as spatial networks. Such characteristics of regions will play important roles for developing and planning city promotion, travel tours and so on, as well as understanding and improving the usage of urban streets. In order to analyze such functionally similar regions, we propose an acceleration method of the...
During the past decade there has been a growing research interest in the effects of social support, characterized by social relationships and affiliation, on health. As health management is largely a social process, social networks have been theorized to impact health outcomes. We find however, an important gap in the literature. Little attention has been given to how the social structure and social...
In recent years, along with the prevalence of social networking sites, the illustrators of Wretch have accordingly transferred to new community platform. These illustrator's fan pages have become popular through viral marketing. Many companies have spotted enormous business opportunities and then worked with illustrators to boost sales by combining illustrators and commercial products. By utilizing...
Real world events are intrinsically dynamic and analytic techniques have to take into account this dynamism. This aspect is particularly important on complex network analysis when relations are channels for interaction events between actors. Sensing technologies open the possibility of doing so for sport networks, enabling the analysis of team performance in a standard environment and rules. Useful...
The team formation problem is required to find a group of individuals that can match the skills required by a collaborative task. Large-scale and comprehensive scientific research tasks need skilled experts from various fields to form a research team and work for it. This paper constructs a dataset and proposes team formation algorithms to find out research teams, which provides decision support for...
The advent of social networking and open health web forums such as PatientsLikeMe, WebMD, ehealth forum etc. have provided avenues for social user data that can prove instrumental in suggesting futuristic trends in healthcare. Homophily in social networks is a vital contributor for analyzing patterns for medical conditions, diagnosis and treatment options. Since, members with similar medical issues...
We present a detailed study on data collection, graph construction, and sampling in Twitter. We observe that sampling on semantic graphs (i.e., graphs with multiple edge types) presents fundamentally distinct challenges from sampling on traditional graphs. The purpose of our work is to present new challenges and initial solutions for sampling semantic graphs. Novel elements of our work include the...
Acquired immune deficiency syndrome (AIDS) is the last and the most life-threatening phase of Human Immunodeficiency Virus (HIV) disease. HIV attacks and heavily affects the immune system of the body which remains unable to resist the disease. HIV uses white blood cells to replicate itself and spreads everywhere in the body. The lifecycle of HIV disease, especially the replication stage must be prominently...
In order to secure vital personal and organizational system we require timely intelligence on cybersecurity threats and vulnerabilities. Intelligence about these threats is generally available in both overt and covert sources like the National Vulnerability Database, CERT alerts, blog posts, social media, and dark web resources. Intelligence updates about cybersecurity can be viewed as temporal events...
Cyberbullying is an important socio-technical challenge in Online Social Networks (OSN). With the growth trends of heterogeneous data in OSN, better network characterization, and textual feature sophistication, recent efforts have realized the value of looking at heterogeneous modes of information including textual features, social features, and image-based features for better cyberbullying detection...
Social network is a hot topic of interest for the researchers in the field of computer science in recent years. The vast amount of data generated by these social networks play a very important role in information diffusion. Social network data are generated by its users. So, user's behavior and activities are being investigated by the researchers to get a logical view of social network platform. This...
We studied the dynamic network of relationships among avatars in the massively multiplayer online game Planetside 2. In the spring of 2014, two separate servers of this game were merged, and as a result, two previously distinct networks were combined into one. We observed the evolution of this network in the seven month period following the merger. We found that some structures of original networks...
1Success of Meetup groups is of utmost importance for the members who organize them. Given a wide variety of such groups, a single metric may not be indicative of success for different groups; rather, success measure should be specific to the interest of a group. In this paper, accounting for the group diversity, we systematically define Meetup group success metrics and use them to generate labels...
In recommender systems, bad recommendations can lead to a net utility loss for both users and content providers. The downside (individual loss) management is a crucial and important problem, but has long been ignored. We propose a method to identify bad recommendations by modeling the users' latent preferences that are yet to be captured using a residual model, which can be applied independently on...
Community detection approaches are useful tools for revealing the structure and properties of networks. There are many approaches for identifying communities, some require inaccessible information, others need initializing parameters to perform well. Many existing algorithms require centralized decision maker to reveal communities. This paper proposes a non-centralized parameter-free method that only...
The study of Web user profiling can be traced back to 30 years ago, with the goal of extracting “semantic”-based user profile attributes from the unstructured Web. Despite slight differences, the general method is to first identify relevant pages of a specific user and then use machine learning models (e.g., CRFs) to extract the profile attributes from the page. However, with the rapid growth of the...
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