1 DiffusionRank: A Possible Penicillin for Web Spamming Haixuan Yang Group Meeting Jan. 16, 2006.
-
date post
19-Dec-2015 -
Category
Documents
-
view
215 -
download
3
Transcript of 1 DiffusionRank: A Possible Penicillin for Web Spamming Haixuan Yang Group Meeting Jan. 16, 2006.
1
DiffusionRank: A Possible Penicillin for Web Spamming
Haixuan Yang
Group MeetingJan. 16, 2006.
2
Outline
Introduction DiffusionRank
Model Establishment Computation consideration Discussion on γ
Results Conclusions
3
Introduction
PageRank Tries to find the importance of a Web page based on
the link structure. The importance of a page i is defined recursively in
terms of pages which point to it:
It proves to be effective for ranking Web pages.
4
Introduction PageRank
Two problems: The incomplete information about the Web structure.
Solution: predict the Web Structure as a random graph. The web pages manipulated by people for
commercial interests. About 70% of all pages in the .biz domain are spam About 35% of the pages in the .us domain belong to spam
category. Two methods used for manipulating spam pages
Link Stuffing Keyword Stuffing
Solution: DiffusionRank
5
An example for manipulation
The rank value of node 1 can be increased greatly!
6
Why? Two reasons
Over-democratic All pages are born equal--equal voting ability of one
page: the sum of each column is equal to one. Input-independent
For any given non-zero initial input, the iteration will converge to the same stable distribution.
Heat Diffusion Model -- a natural way to avoid these two factors
Pages are not equal as some pages are born with high temperatures while others are born with low temperatures.
Different initial temperature distributions will give rise to different temperature distributions after a fixed time period.
7
DiffusionRank On an undirected graph
Assumption: the amount of the heat flow from j to i is proportional to the heat difference between i and j.
Solution:
8
DiffusionRank On an undirected graph
Assumption: the amount of the heat flow from j to i is proportional to the heat difference between i and j.
Solution:
On a directed graph Assumption: there is extra energy imposed on
the link (j, i) such that the heat flow only from j to i if there is no link (i,j).
Solution:
On a random directed graph Assumption: the heat flow is proportional to the
probability of the link (j,i). Solution:
9
DiffusionRank On a random directed graph
Solution:
The initial value f(i,0) in f(0) is set to be 1 if i is trusted and 0 otherwise according to the inverse PageRank.
10
Computation consideration Approximation of heat kernel
N=? When N>=30, the real eigenvalues of are
less than 0.01; when N>=100, they are less than 0.005. We use N=100 in the paper.
When N tends to infinity
11
Discuss γ
γcan be understood as the thermal conductivity. When γ=0, the ranking value is most robust to
manipulation since no heat is diffused, but the Web structure is completely ignored;
When γ= ∞, DiffusionRank becomes PageRank, it can be manipulated easily.
Whenγ=1, DiffusionRank works well in practice
12
DiffusionRank Advantages
Can detect Group-group relations Can cut Graphs Anti-manipulation
+1
-1
γ= 0.5 or 1
13
DiffusionRank Experiments
Data: a toy graph (6 nodes) a middle-size real-world graph (18542 nodes) a large-size real-world graph crawled from CUHK
(607170 nodes) Compare with TrustRank and PageRank
14
Results The tendency of
DiffusionRank when γ becomes larger
On the toy graph
15
Anti-manipulation On the toy graph
16
Anti-manipulation on the middle graph and the large graph
17
Stability--the order difference between ranking results for an algorithm before it is manipulated and those after that
18
Conclusions
This anti-manipulation feature enables DiffusionRank to be a candidate as a penicillin for Web spamming.
DiffusionRank is a generalization of PageRank (when γ=∞).
DiffusionRank can be employed to detect group-group relation.
DiffusionRank can be used to cut graph.