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Articles 1 - 7 of 7
Full-Text Articles in Computer Sciences
Codis: Community Detection Via Distributed Seed-Set Expansion On Graph Streams, Austin Anderson
Codis: Community Detection Via Distributed Seed-Set Expansion On Graph Streams, Austin Anderson
Master's Projects
Community detection has been and remains a very important topic in several fields. From marketing and social networking to biological studies, community detec- tion plays a key role in advancing research in many different fields. Research on this topic originally looked at classifying nodes into discrete communities, but eventually moved forward to placing nodes in multiple communities. Unfortunately, community detection has always been a time-inefficient process, and recent data sets have been simply to large to realistically process using traditional methods. Because of this, recent methods have turned to parallelism, but all these methods, while offering sig- nificant decrease in …
Overlapping Community Detection In Social Networks, Akshar Panchal
Overlapping Community Detection In Social Networks, Akshar Panchal
Master's Projects
Social networking sites are important to connect with the world virtually. As the number of users accessing these sites increase, the data and information keeps on increasing. There are communities and groups which are formed virtually based on different factors. We can visualize these communities as networks of users or nodes and the relationships or connections between them as edges. This helps in evaluating and analyzing different factors that influence community formation in such a dense network. Community detection helps in revealing certain characteristics which makes these groups in the network unique and different from one another. We can use …
Tsar : A System For Defending Hate Speech Detection Models Against Adversaries, Brian Tuan Khieu
Tsar : A System For Defending Hate Speech Detection Models Against Adversaries, Brian Tuan Khieu
Master's Projects
Although current state-of-the-art hate speech detection models achieve praiseworthy results, these models have shown themselves to be vulnerable to attack. Easy to execute lexical manipulations such as the removal of whitespace from a given text create significant issues for word-based hate speech detection models. In this paper, we reproduce the results of five cutting edge models as well as four significant evasion schemes from prior work. Only a limited amount of evasion schemes that also maintain readability exists, and this works to our advantage in the recreation of the original data. Furthermore, we demonstrate that each lexical attack or evasion …
Community Detection Via Neighborhood Overlap And Spanning Tree Computations, Ketki Kulkarni, Aris Pagourtzis, Katerina Potika, Petros Potikas, Dora Souliou
Community Detection Via Neighborhood Overlap And Spanning Tree Computations, Ketki Kulkarni, Aris Pagourtzis, Katerina Potika, Petros Potikas, Dora Souliou
Faculty Publications, Computer Science
Most social networks of today are populated with several millions of active users, while the most popular of them accommodate way more than one billion. Analyzing such huge complex networks has become particularly demanding in computational terms. A task of paramount importance for understanding the structure of social networks as well as of many other real-world systems is to identify communities, that is, sets of nodes that are more densely connected to each other than to other nodes of the network. In this paper we propose two algorithms for community detection in networks, by employing the neighborhood overlap metric …
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Faculty Publications
Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations.In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the …
Community Detection In Social Networks, Ketki Kulkarni
Community Detection In Social Networks, Ketki Kulkarni
Master's Projects
The rise of the Internet has brought people closer. The number of interactions between people across the globe has gone substantially up due to social awareness, the advancements of the technology, and digital interaction. Social networking sites have built societies, communities virtually. Often these societies are displayed as a network of nodes depicting people and edges depicting relationships, links. This is a good and e cient way to store, model and represent systems which have a complex and rich information. Towards that goal we need to nd e ective, quick methods to analyze social networks. One of the possible solution …
A Trust-Aware System For Personalized User Recommendations In Social Networks, Magdalini Eirinaki, Malamati Louta, Iraklis Varlamis
A Trust-Aware System For Personalized User Recommendations In Social Networks, Magdalini Eirinaki, Malamati Louta, Iraklis Varlamis
Faculty Publications
Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networking applications, microblogging, or customer review sites. In this environment, trust is becoming an essential quality among user interactions and the recommendation for useful content and trustful users is crucial for all the members of the network. In this paper, we introduce a framework for handling trust in social networks, which is based on a reputation mechanism that captures the implicit and explicit connections between the network members, analyzes the semantics and dynamics of these …