The increasing importance of Web 2.0 applications during the last years has created significant interest in tools for analyzing and describing collective user activities and emerging phenomena within the Web. Network structures have been widely employed in this context for modeling users, web resources and relations between them. However, the amount of data produced by modern web systems results in networks that are of unprecedented size and complexity, and are thus hard to interpret. To this end, community detection methods attempt to uncover natural groupings of web objects by analyzing the topology of their containing network. There are numerous techniques adopting a global perspective to the community detection problem, i.e. they operate on the complete network structure, thus being computationally expensive and hard to apply in a streaming manner. In order to add a local perspective to the study of the problem, we present Bridge Bounding, a local methodology for community detection, which explores the local network topology around a seed node in order to identify edges that act as boundaries to the local community. The proposed method can be integrated in an efficient global community detection scheme that compares favorably to the state of the art. As a case study, we apply the method to explore the topic structure of the LYCOS iQ collaborative question answering application by detecting communities in the networks created from the collective tagging activity of users.
@cite_30: We propose a novel method of community detection that is computationally inexpensive and possesses physical significance to a member of a social network. This method is unlike many divisive and agglomerative techniques and is local in the sense that a community can be detected within a network without requiring knowledge of the entire network. A global application of this method is also introduced. Several artificial and real-world networks, including the famous Zachary Karate club, are analyzed.
@cite_19: We present a method that allows for the discovery of communities within graphs of arbitrary size in times that scale linearly with their size. This method avoids edge cutting and is based on notions of voltage drops across networks that are both intuitive and easy to solve regardless of the complexity of the graph involved. We additionally show how this algorithm allows for the swift discovery of the community surrounding a given node without having to extract all the communities out of a graph.
@cite_18: 
Most of the methods presented above are global, meaning that they need to process the whole network in order to output the identified community structure. Even though some of these methods achieve low complexity (linear to the size of the network), their use is still prohibitive, when there is need for extremely responsive community detection, e.g. in interactive exploration of large networks, which can be only feasible by means of local processing of the network. We could only find two local methods @cite_30 @cite_18 that are suitable for identifying communities within such applications. However, we consider the first of those @cite_30 as unsuitable for graphs of scale-free nature (since the @math -shell would contain the whole graph after just few expansion steps), and the second @cite_19 as not achieving maximum efficiency, since it is not local by design (i.e. there are redundant computation steps when applying the method locally). We consider that our proposed methodology addresses the community detection problem from a local perspective in a more intuitive and efficient way.