Finding Strong Groups of Friends among Friends in Social Networks

Cameron, Juan J.; Leung, Carson K.; Tanbeer, Syed Khairuzzaman

doi:10.1109/dasc.2011.141

Cited by 45 publications

(14 citation statements)

References 16 publications

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“…Many authors focus on the analysis of the user's affinities and tie-strenght of the relationships in order identify new relevant groups [8], [9], [10], [11], [12]. This research reinforces the idea of groups as a mechanism to promote interaction among users that share the same set of interests and/or properties.…”

Section: Introductionmentioning

confidence: 78%

Measuring Popularity in Social Network Groups

Leitão¹,

Morgado²,

Cunha³

2012

2012 Second International Conference on Cloud and Green Computing

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As the number of users of social network sites increases, the amount of produced information is also increasing at a very fast rate, concerning various types of shared contents like messages, photos, videos and even links, as well as information related to user interactions. Due to this, it becomes necessary to help users to better filter and select the information that will be more useful to them according to their personal interests. Social networking environments typically promote the organization of their users according to groups of individuals who are related through their common characteristics, interests, and interactions.In this paper we present an approach for assisting the user in the evaluation of popularity of the information shared in the context of social networking groups, and we propose a model for measuring the relevance of a user to a group and the group relevance for its members, based on the popularity and freshness of the contents posted by the users.We describe an architecture supporting the model implementation in the Facebook social network, and we describe a Facebook application that collects information concerning the user groups, and the users activities within the groups. The application offers a simple interface where users can have access to some statistic values concerning group-related relevance indicators, for example allowing to observe the users who contribute positively to the group activity, as well as the ones that produce less relevant information or have less activity in a group. We present experimental results of our study, and preliminary conclusions regarding the observation of the behavior of users and groups.

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Section: Introductionmentioning

confidence: 78%

Measuring Popularity in Social Network Groups

Leitão¹,

Morgado²,

Cunha³

2012

2012 Second International Conference on Cloud and Green Computing

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show abstract

“…Nowadays, various graph data sources can easily generate high volumes of streams of graphs (e.g., direct acyclic graphs representing human interactions in meetings 14 , social networks representing connections or friendships among social individuals 8,29 , semantic graphs linking web documents 28 ). Problems and state-of-the-art solutions are highlighted in recent studies.…”

Section: Introductionmentioning

confidence: 99%

Effectively and Efficiently Mining Frequent Patterns from Dense Graph Streams on Disk

Braun

Cameron

Cuzzocrea

et al. 2014

Procedia Computer Science

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In this paper, we focus on dense graph streams, which can be generated in various applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. We also investigate the problem of effectively and efficiently mining frequent patterns from such streaming data, in the targeted case of dealing with limited memory environments so that disk support is required. This setting occurs frequently (e.g., in mobile applications/systems) and is gaining momentum even in advanced computational settings where social networks are the main representative. Inspired by this problem, we propose (i) a specialized data structure called DSMatrix, which captures important data from dense graph streams onto the disk directly and (ii) stream mining algorithms that make use of such structure in order to mine frequent patterns effectively and efficiently. Experimental results clearly confirm the benefits of our approach.

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“…A more systematic approach is needed. In recent years, data mining techniques have been applied to social computing for extracting implicit, previously unknown, and potentially useful information or interesting knowledge (e.g., finding cohesive subgroups [9], diverse social entities [10], [25], important friends [21], significant friends [20], [26], and strong friends [5], [27]) from social networks. For example, the works for finding strong or significant friends are based on the number of messages posted by users in social networking sites such as Facebook.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Really Popular Friends from Social Networks

Jiang

Leung

Liu

et al. 2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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Advances in social computing and networking software and technologies enable users to intersect social behaviour with computing systems for creating social conventions and contexts. In recent years, social networking sites have become popular to facilitate collaboration and knowledge sharing between users. A rich set of information is embedded in these social media data. In this paper, we propose algorithms that incorporate both connectivity and frequency information in helping users to discover really popular friends from social networks. Experimental results show the effectiveness of our algorithms in the discovery of really popular friends from social networks.

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Finding Strong Groups of Friends among Friends in Social Networks

Cited by 45 publications

References 16 publications

Measuring Popularity in Social Network Groups

Measuring Popularity in Social Network Groups

Effectively and Efficiently Mining Frequent Patterns from Dense Graph Streams on Disk

Discovery of Really Popular Friends from Social Networks

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