1. 1 Further Investigations on Heat Diffusion Models Haixuan Yang Supervisors: Prof Irwin King and Prof Michael R. Lyu Term Presentation 2006

2. 2 Outline  Introduction  Input Improvement – Three candidate graphs  Outside Improvement – DiffusionRank  Inside Improvement – Volume-based heat difusion model  Summary

3. 3 Introduction PHDCVolume-based HDM DiffusionRank HDM on Graphs Inside Improvement Input Improvement Outside Improvement PHDC: the model proposed last year

4. 4 PHDC  PHDC is a classifier motivated by Tenenbaum et al (Science 2000)  Approximate the manifold by a KNN graph  Reduce dimension by shortest paths Kondor & Lafferty (NIPS 2002)  Construct a diffusion kernel on an undirected graph  Apply to a large margin classifier Belkin & Niyogi (Neural Computation 2003)  Approximate the manifold by a KNN graph  Reduce dimension by heat kernels Lafferty & Kondor (JMLR 2005)  Construct a diffusion kernel on a special manifold  Apply to SVM

5. 5 PHDC  Ideas we inherit Local information  relatively accurate in a nonlinear manifold. Heat diffusion on a manifold  a generalization of the Gaussian density from Euclidean space to manifold.  heat diffuses in the same way as Gaussian density in the ideal case when the manifold is the Euclidean space.  Ideas we think differently Establish the heat diffusion equation on a weighted directed graph.  The broader settings enable its application on ranking on the Web pages. Construct a classifier by the solution directly.

6. 6 Heat Diffusion Model in PDHC  Notations  Solution  Classifier G is the KNN Graph: Connect a directed edge (j,i) if j is one of the K nearest neighbors of i. For each class k, f(i,0) is set as 1 if data is labeled as k and 0 otherwise. Assign data j to a label q if j receives most heat from data in class q.

7. 7 Input Improvement  Three candidate graphs KNN Graph  Connect points j and i from j to i if j is one of the K nearest neighbors of i, measured by the Euclidean distance. SKNN-Graph  Choose the smallest K*n/2 undirected edges, which amounts to K*n directed edges. Minimum Spanning Tree  Choose the subgraph such that It is a tree connecting all vertices; the sum of weights is minimum among all such trees.

8. 8 Input Improvement  Illustration Manifold KNN Graph SKNN-Graph Minimum Spanning Tree

9. 9 Input Improvement  Advantages and disadvantages KNN Graph  Democratic to each node  Resulting classifier is a generalization of KNN  May not be connected  Long edges may exit while short edges are removed SKNN-Graph  Not democratic  May not be connected  Short edges are more important than long edges Minimum Spanning Tree  Not democratic  Long edges may exit while short edges are removed  Connection is guaranteed  Less parameter  Faster in training and testing

10. 10 Experiments  Experimental Setup Experimental Environments  Hardware: Nix Dual Intel Xeon 2.2GHz  OS: Linux Kernel 2.4.18- 27smp (RedHat 7.3)  Developing tool: C  Data Description 3 artificial Data sets and 6 datasets from UCI  Comparison Algorithms:  Parzen window KNN SVM KNN-H SKNN-H MST-H Results: average of the ten-fold cross validation Dataset Case s ClassesVariable Syn-110022 Syn-210023 Syn-320023 Breast-w68329 Glass21469 Iono351234 Iris15034 Sonar208260 Vehicle846418

11. 11 Experiments  Results Dataset SVM KNN PWAKNN-HMST-HSKNN-H Syn-166.067.080.093.095.0 Syn-234.067.083.094.0 89.0 Syn-354.079.592.091.090.092.0 Breast-w96.894.196.696.995.999.4 Glass68.161.263.568.168.770.5 Iono93.783.289.296.3 Iris9697.395.398.092.094.7 Sonar88.580.353.990.991.894.7 Vehicle84.863.066.065.583.566.6

12. 12 Conclusions  KNN-H, SKNN-H and MST-H Candidates for the Heat Diffusion Classifier on a Graph.

13. 13 Application Improvement  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: Two problems:  The incomplete information about the Web structure.  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.

14. 14 Why PageRank is susceptible to web spam?  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 reasons of PageRank Points are not equal as some points 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.

15. 15 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:

16. 16 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.

17. 17 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

18. 18 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

19. 19 DiffusionRank  Advantages Can detect Group-group relations Can cut Graphs Anti-manipulation +1 γ= 0.5 or 1

20. 20 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

21. 21 Results  The tendency of DiffusionRank when γ becomes larger  On the toy graph

22. 22 Anti-manipulation On the toy graph

23. 23 Anti-manipulation on the middle graph and the large graph

24. 24 Stability--the order difference between ranking results for an algorithm before it is manipulated and those after that

25. 25 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.

26. 26 Inside Improvement  Motivations Finite Difference Method is a possible way to solve the heat diffusion equation.  the discretization of time  the discretization of space and time

27. 27 Motivation  Problems where we cannot employ FD directly in the real data analysis: The graph constructed is irregular; The density of data varies; this also results in an irregular graph; The manifold is unknown; The differential equation expression is unknown even if the manifold is known.

28. 28 Intuition

29. 29 Volume-based Heat Diffusion Model  Assumption There is a small patch SP[j] of space containing node j; The volume of the small patch SP[j] is V (j), and the heat diffusion ability of the small patch is proportional to its volume. The temperature in the small patch SP[j] at time t is almost equal to f(j,t) because every unseen node in the small patch is near node j.  Solution

30. 30 Volume Computation  Define V(i) to be the volume of the hypercube whose side length is the average distance between node i and its neighbors. a maximum likelihood estimation

31. 31 Experiments K: KNN P: Parzen window U: UniverSvm L: LightSVM C: consistency method VHD-v: by the best v VHD: v is found by the estimation HD: without volume consideration C1: 1st variation of C C2: 2nd variation of C

32. 32 Conclusions  The proposed VHDM has the following advantages: It can model the effect of unseen points by introducing the volume of a node; It avoids the difficulty of finding the explicit expression for the unknown geometry by approximating the manifold by a finite neighborhood graph; It has a closed form solution that describes the heat diffusion on a manifold; VHDC is a generalization of both the Parzen Window Approach (when the window function is a multivariate normal kernel) and KNN.

33. 33 Summary  The input improvement of PHDC provide us more choices for the input graphs.  The outside improvement provides us a possible penicillin for Web spamming, and a potentially useful tool for group- group discovery and graph cut.  The inside improvement shows us a promising classifier.