Tag spam creates large non-giant connected components

Neubauer, Nicolas; Wetzker, Robert; Obermayer, Klaus

doi:10.1145/1531914.1531925

Cited by 4 publications

(2 citation statements)

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“…Neubauer et al [8] represent users, web pages, and tags in social bookmarks as different types of nodes, and construct a spam classifier based on the graph structure. Sun et al [11] propose a method for ranking authors using graphs comprised of authors and conferences as nodes and authorship relations as edges.…”

Section: Related Workmentioning

confidence: 99%

“…Social network and co-authorship networks are famous examples. For this reason, many researchers have been trying to analyze such graphs [19,11,8] to extract useful information. In particular, ranking nodes in a graph is of great interest, because it can be applied in wide spectrum of applications, such as extracting important users (important papers/authors) from a social network (a bibliographic network).…”

Section: Introductionmentioning

confidence: 99%

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A Local Method for ObjectRank Estimation

Sakakura

Yamaguchi

Amagasa

et al. 2013

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

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ObjectRank is a method of link structure analysis to evaluate the importance of objects in a database. ObjectRank is known to be computationally expensive, because it requires iterative computations over a large graph. However, in many real applications, it is sufficient to compute the ObjectRank scores for only small fraction of objects. To address this problem, this paper proposes a novel method for estimating ObjectRank scores for specific objects by applying local computation over partial graphs, thereby allowing us to maintain low computational cost even for large graphs. Our basic idea is that, for a given target node, we induce a local graph by checking the edge weights and pruning the edges with considering their weights. We conduct experiments to compare our method with some comparative methods. The experimental results show that our method can reduce the computational cost while maintaining the accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%