16,026 research outputs found
A Taxonomy of Hyperlink Hiding Techniques
Hidden links are designed solely for search engines rather than visitors. To
get high search engine rankings, link hiding techniques are usually used for
the profitability of black industries, such as illicit game servers, false
medical services, illegal gambling, and less attractive high-profit industry,
etc. This paper investigates hyperlink hiding techniques on the Web, and gives
a detailed taxonomy. We believe the taxonomy can help develop appropriate
countermeasures. Study on 5,583,451 Chinese sites' home pages indicate that
link hidden techniques are very prevalent on the Web. We also tried to explore
the attitude of Google towards link hiding spam by analyzing the PageRank
values of relative links. The results show that more should be done to punish
the hidden link spam.Comment: 12 pages, 2 figure
Segmentation and tracking of video objects for a content-based video indexing context
This paper examines the problem of segmentation and tracking of video objects for content-based information retrieval. Segmentation and tracking of video objects plays an important role in index creation and user request definition steps. The object is initially selected using a semi-automatic approach. For this purpose, a user-based selection is required to define roughly the object to be tracked. In this paper, we propose two different methods to allow an accurate contour definition from the user selection. The first one is based on an active contour model which progressively refines the selection by fitting the natural edges of the object while the second used a binary partition tree with aPeer ReviewedPostprint (published version
Taxonomy and clustering in collaborative systems: the case of the on-line encyclopedia Wikipedia
In this paper we investigate the nature and structure of the relation between
imposed classifications and real clustering in a particular case of a
scale-free network given by the on-line encyclopedia Wikipedia. We find a
statistical similarity in the distributions of community sizes both by using
the top-down approach of the categories division present in the archive and in
the bottom-up procedure of community detection given by an algorithm based on
the spectral properties of the graph. Regardless the statistically similar
behaviour the two methods provide a rather different division of the articles,
thereby signaling that the nature and presence of power laws is a general
feature for these systems and cannot be used as a benchmark to evaluate the
suitability of a clustering method.Comment: 5 pages, 3 figures, epl2 styl
Stigmergic hyperlink's contributes to web search
Stigmergic hyperlinks are hyperlinks with a "heart beat": if used they stay healthy and online; if
neglected, they fade, eventually getting replaced. Their life attribute is a relative usage measure that
regular hyperlinks do not provide, hence PageRank-like measures have historically been well
informed about the structure of webs of documents, but unaware of what users effectively do with
the links.
This paper elaborates on how to input the users’ perspective into Google’s original, structure centric,
PageRank metric. The discussion then bridges to the Deep Web, some search challenges, and how
stigmergic hyperlinks could help decentralize the search experience, facilitating user generated
search solutions and supporting new related business models.info:eu-repo/semantics/publishedVersio
Using Markov Chains for link prediction in adaptive web sites
The large number of Web pages on many Web sites has raised
navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past
visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability
matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site
Clustering based on Random Graph Model embedding Vertex Features
Large datasets with interactions between objects are common to numerous
scientific fields (i.e. social science, internet, biology...). The interactions
naturally define a graph and a common way to explore or summarize such dataset
is graph clustering. Most techniques for clustering graph vertices just use the
topology of connections ignoring informations in the vertices features. In this
paper, we provide a clustering algorithm exploiting both types of data based on
a statistical model with latent structure characterizing each vertex both by a
vector of features as well as by its connectivity. We perform simulations to
compare our algorithm with existing approaches, and also evaluate our method
with real datasets based on hyper-textual documents. We find that our algorithm
successfully exploits whatever information is found both in the connectivity
pattern and in the features
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