Probabilistic SimRank computation over uncertain graphs

Du, Lingxia; Li, Cuiping; Hong, Chen; Tan, Liwen; Zhang, Yinglong

doi:10.1016/j.ins.2014.10.030

Cited by 19 publications

(19 citation statements)

References 24 publications

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“…Intuitively, if v i = v j for some 1 ≤ i, j ≤ k and i = j, then, on any possible world G, the transition from v i to v i+1 and the transition from v j to v j+1 are not independent. This finding distinguishes our work from the work by Du et al [7], in which they make an unreasonable assumption that Pr G (…”

Section: Computing Transition Probabilitiescontrasting

confidence: 86%

SimRank computation on uncertain graphs

Zhu

Zou

2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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Abstract-SimRank is a similarity measure between vertices in a graph, which has become a fundamental technique in graph analytics. Recently, many algorithms have been proposed for efficient evaluation of SimRank similarities. However, the existing SimRank computation algorithms either overlook uncertainty in graph structures or is based on an unreasonable assumption (Du et al). In this paper, we study SimRank similarities on uncertain graphs based on the possible world model of uncertain graphs. Following the random-walk-based formulation of SimRank on deterministic graphs and the possible worlds model of uncertain graphs, we define random walks on uncertain graphs for the first time and show that our definition of random walks satisfies Markov's property. We formulate the SimRank measure based on random walks on uncertain graphs. We discover a critical difference between random walks on uncertain graphs and random walks on deterministic graphs, which makes all existing SimRank computation algorithms on deterministic graphs inapplicable to uncertain graphs. To efficiently compute SimRank similarities, we propose three algorithms, namely the baseline algorithm with high accuracy, the sampling algorithm with high efficiency, and the two-phase algorithm with comparable efficiency as the sampling algorithm and about an order of magnitude smaller relative error than the sampling algorithm. The extensive experiments and case studies verify the effectiveness of our SimRank measure and the efficiency of our SimRank computation algorithms.

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Section: Computing Transition Probabilitiescontrasting

confidence: 86%

SimRank computation on uncertain graphs

Zhu

Zou

2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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“…Third, although the semantics in the direction of links is lost, it is advantageous to disregard the direction of links in similarity computation. Finally, since the computation of C-Rank is fundamentally the same as that of SimRank, except that C-Rank disregards the direction of links, many of the methods [8,9,14,22,35,43,45,49,50] to improve the time complexity of SimRank can be applied to C-Rank.…”

Section: Discussionmentioning

confidence: 99%

“…However, the worst-case time complexity of all existing iterative measures including C-Rank is O(n 4 ). Many methods have been proposed to improve the time complexity of SimRank [8,9,14,22,35,43,45,49,50]. These methods can be applied to C-Rank because the computation of C-Rank is fundamentally the same as that of SimRank except that C-Rank disregards the direction of links.…”

Section: Algorithmmentioning

confidence: 99%

C-Rank: A link-based similarity measure for scientific literature databases

Yoon

Kim

Park

2016

Information Sciences

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a b s t r a c tAs the number of people who use scientific literature databases has grown, the demand for literature retrieval services has steadily increased. One of the most popular retrieval service methods is to find a set of papers similar to the paper under consideration, which requires a measure that computes the similarities between the papers. Scientific literature databases exhibit two interesting characteristics that are not found in general databases. First, the papers cited by older papers are often not included in the database due to technical and economic reasons. Second, since a paper references previously published papers, few papers cite recently published papers. These two characteristics cause all existing similarity measures to fail in at least one of the following cases: (1) measuring the similarity between old, but similar papers, (2) measuring the similarity between recent, but similar papers, and (3) measuring the similarity between two similar papers: one old, the other recent. In this paper, we propose a new link-based similarity measure called C-Rank, which uses both in-link and out-link references, disregarding the direction of the references. In addition, we discuss the most suitable normalization method for scientific literature databases and we propose an evaluation method for measuring the accuracy of similarity measures. For the experiments, we used real-world papers from DBLP's database with reference information crawled from Libra. We then compared the performance of C-Rank with that of existing similarity measures. Experimental results showed that C-Rank achieved a higher accuracy than existing similarity measures.

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“…A di erent node-centric uncertain graph model and node ranking approach are presented in [23]: in particular, [23] collapses the uncertain parts of a graph into a cloud graph, where the end of every undetected link is connected to this cloud graph and computes PageRank scores on this transformed graph. [12] considered uncertain graphs, where edges are annotated with existence probabilities and extended the SimRank measure [14] under probabilistic interpretations of edge existence and transition matrices.…”

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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Probabilistic SimRank computation over uncertain graphs

Cited by 19 publications

References 24 publications

SimRank computation on uncertain graphs

SimRank computation on uncertain graphs

C-Rank: A link-based similarity measure for scientific literature databases

Personalized PageRank in Uncertain Graphs with Mutually Exclusive Edges

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