Download miễn phí Khóa luận Research on node ranking in peer-to-peer networks



Chapter 1
Table of Contents
Abstract.
List images. 5
List tables. 7
Chapter 1: Peer to Peer and Ranking Problem . 5
1.1. Peer to Peer . 5
1.1.1. Peer to Peer overview . 5
1.1.2. Architecture of Peer to Peer Systems .7
1.1.3. Distributed hash tables. 8
1.2. Ranking in Peer to Peer networks. 9
1.2.1. Introduction.
1.2.2. Ranking Roles.
1.2.3. Research’s important objects .
Chapter 2: Ranking on DHT Peer to Peer Networks. 11
2.1. Chord Protocol . 11
2.2. Pagerank. 12
2.2.1. Description. 12
2.2.2. Algorithms . 13
2.3. Distributed Computing . 17
2.2.1. Introduction. 17
2.2.2. Algorithms .
2.4 if-idf.18
Chapter 3: Building a new algorithm for ranking in chord networksError! Bookmark not define
3.1. Targets and Missions of Research .
3.2. Idea.
3.2.1. Major problems to exploit .
3.2.2. Ranking Idea .
Chapter 4: Ranking on Details .
4.1. Ranking algorithm .
4.2. Ranking’s features .
Chapter 5: Evaluation . 50
Chapter 6: Related Work . 52
Chapter 7: Contributions and future work. 53
References . 54


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s index, Google evaluates the PageRank
score. When Google increases the document quantity in its collection, PageRank
initial approximation for all document reduction.
The convention use obtains tastelessly, in several clicks and switch after random page
a random surf rider’s model. The page PageRank value reflection random surfrider
will land in that page through the click in the link opportunity. May understand that
takes the condition is the page, and the transition is equally all possible and is between
the page link Markov chain.
If the page link to other data, it has not become the water trough, and terminates
the random surfing the process. However, the explanation is quite simple. If the
random surf rider arrives at the water trough page, it picks another URL stochastically,
and continues again the surfing.
When calculates PageRank, the page has not linked outward the supposition and in
collection other data of connections. Therefore their PageRank score is divided evenly
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Research on Node Ranking – Peer to Peer …. Hoàng Cường
19
in other data. In other words, is fair with is not water trough's page, these random
transitions increase to net's all knots, when remaining possible usual d = 0.85,
estimated that uses their browser' from a frequency common surfrider; s bookmark
characteristic.
Therefore, the equality is as follows:
where p1,p2,...,pN are the data under consideration, M(pi) is the set of data that link
to pi, L(pj) is the number of outbound links on page pj, and N is the total number of
data.
jacency matrix. This makes PageRank a particularly elegant metric: the
eigenvector is
The PageRank values are the entries of the dominant eigenvector of
the modified ad
where R is the solution of the equation
where the adjacency function is 0 if page pj does not link to pi, and
normalized such that, for each i
,
i.e. the elements of each column sum up to 1 (for more details see
the computation section below). This is a variant of the eigenvector centrality measure
sed commonly in network analysis.
the PageRank eigenvector are fast to approximate (only a few iterations are
needed).
u
Because of the large eigengap of the modified adjacency matrix above, the
values of
Research on Node Ranking – Peer to Peer …. Hoàng Cường
As a result of the Markov theory, may display page PageRank be the possibility
is in that page after many clicks. This accidentally equals t − 1 t is expectation of) the
place request's click (or jumps willfully quantity obtains from the page returns to itself.
The major object is it favors a older page, because is new, the very good first
page, will not even have many links, only if it will be an existing stand (is a stand part
crowded wrap page which will connect, for example Wikipedia). The Google table of
contents (itself derivative opening table of contents project) allows the user to look in
the category the PageRank sorting result. The Google table of contents is PageRank
determined directly the demonstration order Google provides only service. In Google'
the s other search service (e.g. its main net search) PageRank uses in considering the
relevance in search result demonstration dozens of data. Several strategies proposed
that accelerates PageRank the computation. Operated PageRank various strategies to
arrange to use diligently together the improvement search result ranking and decides
as the currency to do to the link the advertisement.
These strategies have attacked the PageRank concept reliability severely, seeks
determined that which documents in fact take seriously by the net community. Google
knew that the punishment the link farm which and other plans designs inflates
artificially PageRank. Google starts in December, 2007 to punish effectively sells the
paid text link the stand. How does Google identify the link farm and other PageRank
operational tool is in Google' In; s business secret.
2.3 Distributed Computing
The distributed computing is the computer science area research distributional
system. Distributional system through a computer network service including many
autonomous computers. The computer achieves a common goal mutually according to
the order interaction. The computer program which runs in the distributional system
said that a distributed program, the distribution programming writes such program' s
process. And the distributed computing mentions the use distributional system
explanation estimate question.
In the distributed computing, the question is divided many responsibilities, the
computer explains everybody.
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Research on Node Ranking – Peer to Peer …. Hoàng Cường
Image 2.3: Distributed Nodes Graph example
We pass use computer’s hope automation; s many responsibilities held
responsible with answer the type: We hope to ask the question, and the computer
should cause the answer. In the computer science theoretically, is called the estimate
question like this voluntarily. It is estimated that the question has each template
including the instance is an explanation officially together. The example is the
question which we asked that and the explanation is anticipates the answer to these
questions.
(How does the theory computer science seek needs to understand the estimate
question possibly through use that the complex theory solution computer (the
computability theory) and high efficiency computation). In the tradition, said the
question perhaps through the use solution computer, if perhaps we design all concrete
instances are correct explanation algorithm causes. Perhaps such algorithm possibly
implements the computer program which runs in an general calculator: Studies from
the input question instance's holiday eye, carries out some computation, and causes the
explanation to adopt the product.
Formalism for example random access ' perhaps the s machine or the universal
Turing machine use the achievement to carry out such algorithm continuously general
calculator' s abstraction model. In many computer situations, consistent and distributed
computing area research similar question or execution interaction process system
computer: Which estimate question how can solve in such network and the high
efficiency place? However, it is not obvious in concurrent or the distributional system
situation, “solves the problem is all meanings”
2.4 Computing PageRank in a distributed system
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Research on Node Ranking – Peer to Peer …. Hoàng Cường
Lectured the net graph in distribution system's recent research work to divide
into messes up the website or the domain case. The net is molded takes many messes
up the network server. Is divided in net's ultra link two categories, the internal cut-off
link and the mutual server link. The internal server link is between the page link in the
server, and these links use in calculating on each server's place PageRank intermediate
vector. The mutual server link is between the page link with the different server, and
they use in calculating ServerRank. ServerRank surveys the different network server's
relative importance. The server which submits is being merged finally from many
network server's result causes an arrangement ultra link name list.
The ranking algebra proposed that deals with the ranking in the different
granularity level, is utilized possibly also in gathering the place ranking and the stand
ranking obtains the global ranking. Has in one disperses the system fully in the
PageRank approximation work, each of the same generation is autonomous, and
perhaps of the same generation mutually overlaps. Was proposing the JXP algorithm,
each of the same generation calculates the place PageRank score, then meets other of
the same generations and increases it gradually through the exchange information
willfully about the global net graph knowledge, then recomputation in place of the
same generation's PageRank score.
This conference and the recomputation process is duplicated, collects the
enough information until of the same generation. If of the same generation meets the
sufficient number of times exchange information finally, JXP score polymerization to
the real global PageRank score. Supposes is each page of out degree in global graph
awareness. However, these operations are providing the approximation the focal point
are the global graph, in centralized system or distribution system.
2.5. tf-idf
The tf–idf weight (term frequency–inverse document frequency) is a weight
often used in information retrieval and text mining. This weight is a statistical measure
used to evaluate how important a word is to a document in a collection or corpus. The
importanc...

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