Efficient personalized pagerank with accuracy assurance

Fujiwara, Yasuhiro; Nakatsuji, Makoto; Yamamuro, Takeshi; Shiokawa, Hiroaki; Onizuka, Makoto

doi:10.1145/2339530.2339538

Cited by 68 publications

(46 citation statements)

References 27 publications

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“…A few studies have applied a direct method to compute PPR [22,23]. However, as discussed in Section 7, the computational cost of a direct method largely depends on graph structures.…”

Section: Basic Algorithmsmentioning

confidence: 99%

Computing personalized PageRank quickly by exploiting graph structures

et al. 2014

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We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR; however, it requires many iterations. Thus reducing the number of iterations is the main challenge.We achieve this by exploiting graph structures of web graphs and social networks. The convergence of our algorithm is very fast. In fact, it requires up to 7.5 times fewer iterations than the Power method and is up to five times faster in actual computation time.To the best of our knowledge, this is the first time to use graph structures explicitly to solve PPR quickly. Our contributions can be summarized as follows.1. We provide an algorithm for computing a tree decomposition, which is more efficient and scalable than any previous algorithm. 2. Using the above algorithm, we can obtain a core-tree decomposition of any web graph and social network. This allows us to decompose a web graph and a social network into (1) the core, which behaves like an expander graph, and (2) a small tree-width graph, which behaves like a tree in an algorithmic sense. 3. We apply a direct method to the small tree-width graph to construct an LU decomposition. 4. Building on the LU decomposition and using it as preconditoner, we apply GMRES method (a state-of-theart advanced iterative method) to compute PPR for whole web graphs and social networks.

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“…A few studies have applied a direct method to compute PPR [22,23]. However, as discussed in Section 7, the computational cost of a direct method largely depends on graph structures.…”

Section: Basic Algorithmsmentioning

confidence: 99%

Computing personalized PageRank quickly by exploiting graph structures

et al. 2014

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“…Fujiwara el al. proposed fast methods for searching PPR by using LU decomposition and QR decomposition [6], [7]. Unlike the method of Tong et al, these methods have the merit of being able to perform accurate searches on PPR.…”

Section: Related Workmentioning

confidence: 99%

“…In this experiment, we compare our approach with three other methods -the Monte Carlobased approach proposed by Avrachenkov et al [12], the matrix-based approach proposed by Fujiwara et al [7], and the original iterative approach [3]. The results obtained with these four methods are labelled "Castanet", "Monte", "Matrix" and "Original", respectively.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Fast Ad-Hoc Search Algorithm for Personalized PageRank

Fujiwara

Nakatsuji²,

Shiokawa

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYPersonalized PageRank (PPR) is a typical similarity metric between nodes in a graph, and node searches based on PPR are widely used. In many applications, graphs change dynamically, and in such cases, it is desirable to perform ad hoc searches based on PPR. An ad hoc search involves performing searches by varying the search parameters or graphs. However, as the size of a graph increases, the computation cost of performing an ad hoc search can become excessive. In this paper, we propose a method called Castanet that offers fast ad hoc searches of PPR. The proposed method features (1) iterative estimation of the upper and lower bounds of PPR scores, and (2) dynamic pruning of nodes that are not needed to obtain a search result. Experiments confirm that the proposed method does offer faster ad hoc PPR searches than existing methods.

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“…<Figure 6> Applying BRW to a Signed Graph is the Equation Solving Approach to simplify the entire calculation by using the inverse of matrices [8]. However, this method has its own disadvantage, in that if a graph changes, it is necessary to recalculate the inverse of matrices again.…”

Section: Algorithms In the First Category Exploitmentioning

confidence: 99%

Fast Random Walk with Restart over a Signed Graph

Myung

Shim

Suh

2015

The Journal of Society for e-Business Studies

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RWR (Random Walk with Restart) is frequently used by many graph-based ranking algorithms, but it does not consider a signed graph where edges may have negative weight values. In this paper, we apply the Balance Theory by F. Heider to RWR over a signed graph and propose a novel RWR, Balanced Random Walk (BRW). We apply the proposed technique into the domain of recommendation system, and show by experiments its effectiveness to filter out the items that users may dislike. In order to provide the reasonable performance of BRW in the domain, we modify the existing Top-k algorithm, BCA, and propose a new algorithm, Bicolor-BCA. The proposed algorithm yet requires employing a threshold. In the experiment, we show how threshold values affect both precision and performance of the algorithm.

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Efficient personalized pagerank with accuracy assurance

Cited by 68 publications

References 27 publications

Computing personalized PageRank quickly by exploiting graph structures

Computing personalized PageRank quickly by exploiting graph structures

Fast Ad-Hoc Search Algorithm for Personalized PageRank

Fast Random Walk with Restart over a Signed Graph

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