Locality-sensitive and Re-use Promoting Personalized PageRank computations

Kim, Jung Hyun; Candan, K. Selçuk; Sapino, Maria Luisa

doi:10.1007/s10115-015-0843-6

Cited by 5 publications

(6 citation statements)

References 43 publications

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“…For an input preference vector d , PPR calculates the PPR value of a node relative to d . The original PageRank definition is equivalent to the particular case when the preference vector d is a uniform distribution; if non‐uniform distribution, it is called personalized PageRank 22,23 . Kim et al 22 proposed an efficient method for computing locally sensitive PPR.…”

Section: Related Workmentioning

confidence: 99%

“…The original PageRank definition is equivalent to the particular case when the preference vector d is a uniform distribution; if non‐uniform distribution, it is called personalized PageRank 22,23 . Kim et al 22 proposed an efficient method for computing locally sensitive PPR. Fujiwara et al 23 studied the efficient calculation method of single node PPR and top‐ k nodes PPR.…”

Section: Related Workmentioning

confidence: 99%

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A novel top‐k key node query problem in subgraph matching and its greedy strategy

Xue

2021

Engineering Reports

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Top‐k node selection in graph data is an essential problem in computer science and applications. In view of an important issue in the field of graph data, subgraph matching issue, we define the problem and propose its method for the top‐k key node query w.r.t. the subgraph matching. Unlike the general top‐k query problem, we aim to find out k nodes that make the matching subgraphs in data graph G that are covered by the k nodes as more as possible. This is a problem of the maximum coverage of subgraph matching, which belongs to the NP‐hard problem. We study the problem based on a greedy algorithm and give an intuitive solution. Considering the characteristics of the top‐k problem, we propose an improved and more efficient greedy algorithm. Experiments on real social network graph data set (Twitter) show that the related results represent the key nodes that can better reveal the essential characteristics of the query graph in the data graph G. The key node query problem in subgraph matching proposed in this article may have extensive applications in reality, such as the assessment of the influence of specific group members in social network, the detection of abnormal communication in a computer communication network, the road traffic evaluation and load balance problem in a road traffic network, and so on.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A novel top‐k key node query problem in subgraph matching and its greedy strategy

Xue

2021

Engineering Reports

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“…It takes into account the connectivity of nodes in the graph by de ning the score of the node i ∈ V as the amount of time spent on i in a su ciently long random walk on the graph. e personalized PageRank (PPR) [9,18] technique extends this in a way that takes into account the context de ned by a given set of important nodes: given a set of seed nodes S ⊆ V , the PPR scores can be represented as a vector → r , where…”

Section: Node Ranking In Uncertain Graphsmentioning

confidence: 99%

Personalized PageRank in Uncertain Graphs with Mutually Exclusive Edges

Kim

Candan

et al. 2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Measures of node ranking, such as personalized PageRank, are utilized in many web and social-network based prediction and recommendation applications. Despite their e ectiveness when the underlying graph is certain, however, these measures become di cult to apply in the presence of uncertainties, as they are not designed for graphs that include uncertain information, such as edges that mutually exclude each other. While there are several ways to naively extend existing techniques (such as trying to encode uncertainties as edge weights or computing all possible scenarios), as we discuss in this paper, these either lead to large degrees of errors or are very expensive to compute, as the number of possible worlds can grow exponentially with the amount of uncertainty. To tackle with this challenge, in this paper, we propose an e cient Uncertain Personalized PageRank (UPPR) algorithm to approximately compute personalized PageRank values on an uncertain graph with edge uncertainties. UPPR avoids enumeration of all possible worlds, yet it is able to achieve comparable accuracy by carefully encoding edge uncertainties in a data structure that leads to fast approximations. Experimental results show that UPPR is very e cient in terms of execution time and its accuracy is comparable or be er than more costly alternatives.

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“…Their approach has two main parts: firstly they propose a local algorithm for approximating PPR values based on a careful simulation of random walks from the restart nodes; they also propose a multi-scale matrix sampling algorithm on the PPR result matrix. [28] proposed an alternative locality-sensitive, re-use promoting, approximate Personalized PageRank (LR-PPR) algorithm for efficiently computing the PPR values relying on the localities of the given seed nodes on the graph: The LR-PPR algorithm is (a) locality sensitive in the sense that it reduces the computational cost of the PPR computation process by focusing on the local neighborhoods of the seed nodes and is (b) re-use promoting in that instead of performing a monolithic computation for the given seed node set using the entire graph, LR-PPR divides the work into localities of the seeds and caches the intermediary results obtained during the computation. The robust personalized PageRank formulations proposed in this paper are also re-use promoting in the same sense, though they rely on a different mechanism for dividing the work relative to individual seed nodes to support caching and reuse of the intermediary results obtained during the computation.…”

Section: Obtaining Pagerank and Personalized Pagerank Scoresmentioning

confidence: 99%

Reducing seed noise in personalized PageRank

Huang

Candan

et al. 2016

Soc. Netw. Anal. Min.

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Network based recommendation systems leverage the topology of the underlying graph and the current user context to rank objects in the database. Random-walk based techniques, such as PageRank, encode the structure of the graph in the form of a transition matrix of a stochastic process from which the significances of the nodes in the graph are inferred. Personalized PageRank (PPR) techniques complement this with a seed node set which serves as the personalization context. In this paper, we note (and experimentally show) that PPR algorithms that do not differentiate among the seed nodes may not properly rank nodes in situations where the seed set is incomplete and/or noisy. To tackle this problem, we propose alternative robust personalized PageRank (RPR) strategies, which are insensitive to noise in the set of seed nodes and in which the rankings are not overly biased towards the seed nodes. In particular, we show that novel teleportation discounting and seed-set maximal PPR techniques help eliminate harmful bias of individual seed nodes and provide effective seed differentiation to lead to more accurate rankings. We also show that the proposed techniques lead to efficient implementations, where existing approximation algorithms and/or parallel implementations for computing the PPR scores can be easily leveraged. Moreover, the proposed formulations are reuse-promoting in the sense that, it is possible to divide the work relative to individual seed nodes and cache the intermediary results obtained during the computation, and especially in systems with large query throughputs, it may be possible to cluster queries based on the partial overlaps between the seed sets and reduce the overall robust PPR computation costs. Experiment results show that the proposed techniques are efficient and highly effective in improving recommendations and eliminating unwanted bias due to imperfections in the seed set.

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Locality-sensitive and Re-use Promoting Personalized PageRank computations

Cited by 5 publications

References 43 publications

A novel top‐k key node query problem in subgraph matching and its greedy strategy

A novel top‐k key node query problem in subgraph matching and its greedy strategy

Personalized PageRank in Uncertain Graphs with Mutually Exclusive Edges

Reducing seed noise in personalized PageRank

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