Efficient search algorithm for SimRank

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

doi:10.1109/icde.2013.6544858

Cited by 58 publications

(89 citation statements)

References 29 publications

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“…In contrast to the best-known single-source SimRank [4,9,10], Corollary 1 has the following advantages: (1) Unlike the O(∆ 2k ) time of [10] that increases exponentially w.r.t. k, our method scales well to |E|.…”

Section: Improving Single-source Simrankmentioning

confidence: 99%

“…Their approach is deterministic, but requires O(r|V | 2 ) precomputation time to determine input matrices L and R for Algorithm 2 of [4], which is costly.…”

Section: Single-source Simrankmentioning

confidence: 99%

“…Recently, Fujiwara et al [4] has devised an appealing matrix-based SimMat in O(r|V |) time (exclude preprocessing time) to retrieve top-K s(⋆, j), where r is the low rank of approximation. Their approach is deterministic, but requires O(r|V | 2 ) precomputation time to determine input matrices L and R for Algorithm 2 of [4], which is costly.…”

Section: Single-source Simrankmentioning

confidence: 99%

“…We refer to this phenomenon as "high iteration coupling". To the best of our knowledge, there are several pioneering works trying to break the "high iteration coupling" barrier, such as single-pair SimRank [3,6,12], and top-K single-source SimRank [4,9,10], but they still require excessive time and memory, or deliver probabilistic results. (see Related Work section for a detailed comparison).…”

Section: Introductionmentioning

confidence: 99%

“…First, it is a unified model, containing as special cases the existing all-pairs [13,16], single-source [4,10], and single-pair [6,12] SimRank, when (A, B) is taken to be (V, V ), (V, {j}), and ({i}, {j}), respectively, with queries i and j. Thus, it is applicable to any domains where all-pairs, single-source, and single-pair SimRank apply.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient partial-pairs simrank search on large networks

McCann

2015

Proc. VLDB Endow.

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The assessment of node-to-node similarities based on graph topology arises in a myriad of applications, e.g., web search. SimRank is a notable measure of this type, with the intuition that "two nodes are similar if their in-neighbors are similar". While most existing work retrieving SimRank only considers all-pairs SimRank s(⋆, ⋆) and single-source SimRank s(⋆, j) (scores between every node and query j), there are appealing applications for partial-pairs SimRank, e.g., similarity join. Given two node subsets A and B in a graph, partial-pairs SimRank assessment aims to retrieve only {s(a, b)} ∀a∈A,∀b∈B . However, the best-known solution appears not self-contained since it hinges on the premise that the SimRank scores with node-pairs in an h-go cover set must be given beforehand. This paper focuses on efficient assessment of partial-pairs SimRank in a self-contained manner. (1) We devise a novel "seed germination" model that computes partial-pairs SimRank in O(k|E| min{|A|, |B|}) time and O(|E|+k|V |) memory for k iterations on a graph of |V | nodes and |E| edges.(2) We further eliminate unnecessary edge access to improve the time of partial-pairs SimRank to O(m min{|A|, |B|}), where m ≤ min{k|E|, ∆ 2k }, and ∆ is the maximum degree. (3) We show that our partial-pairs SimRank model also can handle the computations of all-pairs and single-source SimRanks. (4) We empirically verify that our algorithms are (a) 38x faster than the best-known competitors, and (b) memory-efficient, allowing scores to be assessed accurately on graphs with tens of millions of links.

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Section: Improving Single-source Simrankmentioning

confidence: 99%

“…Their approach is deterministic, but requires O(r|V | 2 ) precomputation time to determine input matrices L and R for Algorithm 2 of [4], which is costly.…”

Section: Single-source Simrankmentioning

confidence: 99%