A New Local Algorithm for Detecting Communities in Networks

We present the principles, implementation and evaluation of the SAw-SHARC algorithm, a Stability Aware Sharper Heuristic for Assignment of Robust Communities. The objective of this contribution is to perform distributed detection of densely and reliably interconnected users of a wireless ad hoc network and group them in communities. Community assignment is achieved using the computation of a neighborhood similarity measure in conjunction with a node-centric estimation of the communication links relative quality and epidemic propagation of community labels. Our algorithm also implements countermeasures to prevent domination of a single community in highly dynamic networks.We consider this contribution as a valuable tool for the development of large ad hoc mobile social networks (MoSoNets) where person-to-person interactive applications require a high quality of service in communications. Identifying subsets of reliably interconnected users would allow to meet those requirements. Besides, it will help cope the scalability of those networks, while maintaining their user-oriented nature.We evaluated our contribution using simulation of meaningful scenarios of social interactions and ensured its performance. Results show that SAw-SHARC favors creation of size-bounded stable communities and prevents the community domination effect in dynamic networks.

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“…In [18], the authors proceed to a greedy agglomerative algorithm, starting with the edge of highest contribution to its community.…”

Section: Community Detection Algorithmsmentioning

confidence: 99%

On the generation of stable communities of users for dynamic mobile ad hoc social networks

Herbiet

Bouvry

2011

The International Conference on Information Networking 2011 (ICOIN2011)

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“…Some algorithms have been proposed to detect local communities. For example, Tian et al [72] proposes an algorithm designed for both unweighted and weighted graphs.…”

Section: Local Community Detectionmentioning

confidence: 99%

From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

Brun

Castagnos

Boyer

2011

Advances in Multimedia

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The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt one state-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that the precision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a reduced set of mentors.

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Social Network Analysis Techniques for Social-Oriented Mobile Communication Networks

Herbiet

Bouvry

2012

Computational Social Networks

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A New Local Algorithm for Detecting Communities in Networks

Cited by 5 publications

References 14 publications

On the generation of stable communities of users for dynamic mobile ad hoc social networks

On the generation of stable communities of users for dynamic mobile ad hoc social networks

From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

Social Network Analysis Techniques for Social-Oriented Mobile Communication Networks

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