You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: We introduce a stricter Web community definition to overcome boundary ambiguity of a Web community defined by Flake, Lawrence and Giles [2], and consider the problem of finding communities that satisfy our definition. We discuss how to find such communities and hardness of this problem.We also propose Web page partitioning by equivalence relation defined using the class of communities of our definition. Though the problem of efficiently finding all communities of our definition is NP-complete, we propose an efficient method of finding a subclass of communities among the sets partitioned by each of n-1 cuts represented by a Gomory-Hu tree [10], and partitioning a Web graph by equivalence relation defined using the subclass.According to our preliminary experiments, partitioning by our method divided the pages retrieved by keyword search into several different categories to some extent.

[2]: We de ne a communit y on the web as a set of sites that have more links (in either direction) to members of the community than to non-members. Members of such a community can be eAEciently iden ti ed in a maximum ow minim um cut framework, where the source is composed of known members, and the sink consists of well-kno wn non-members. A focused crawler that crawls to a xed depth can approximate community membership by augmenting the graph induced by the cra wl with links to a virtual sink node.The effectiveness of the approximation algorithm is demonstrated with several crawl results that iden tify hubs, authorities, w eb rings, and other link topologies that are useful but not easily categorized. Applications of our approach include focused cra wlers and search engines, automatic population of portal categories, and improved ltering.
[2] define communities as subsets of vertices that have more links (undirected) to each other than to the rest of the network nodes. To detect such communities on the web, they integrate a maximum flow strategy with an iterative crawling process. A stricter community definition was considered by [1] and a technique was devised to detect them that was based on both the maximum flow algorithm and an iterative graph partitioning and contraction process.