Personalized PageRank to a Target Node, Revisited

Wang, Hanzhi; Wei, Zhewei; Gan, Junhao; Wang, Sibo; Huang, Zengfeng

doi:10.1145/3394486.3403108

Cited by 43 publications

(19 citation statements)

References 52 publications

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“…To identify species that indirectly support ecosystem services, we first applied a personalized PageRank approach 61 . This method builds on Google's PageRank™ algorithm, which ranks web pages as "important" or relevant to user's searches 62 .…”

Section: Supporting Speciesmentioning

confidence: 99%

An ecological network approach to predict ecosystem service vulnerability to species losses

et al. 2021

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Human-driven threats are changing biodiversity, impacting ecosystem services. The loss of one species can trigger secondary extinctions of additional species, because species interact–yet the consequences of these secondary extinctions for services remain underexplored. Herein, we compare robustness of food webs and the ecosystem services (hereafter ‘services’) they provide; and investigate factors determining service responses to secondary extinctions. Simulating twelve extinction scenarios for estuarine food webs with seven services, we find that food web and service robustness are highly correlated, but that robustness varies across services depending on their trophic level and redundancy. Further, we find that species providing services do not play a critical role in stabilizing food webs – whereas species playing supporting roles in services through interactions are critical to the robustness of both food webs and services. Together, our results reveal indirect risks to services through secondary species losses and predictable differences in vulnerability across services.

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Section: Supporting Speciesmentioning

confidence: 99%

An ecological network approach to predict ecosystem service vulnerability to species losses

et al. 2021

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“…Note that the upper bound of the restart probability 𝛼 in Theorem 4.3 (which is approximately 0.243) is not restrictive, as 𝛼 is usually set to 0.15 or 0.2 [10,49,58,88,89,92].…”

Section: Pdist Definitionmentioning

confidence: 99%

“…Lemma 5.4 indicates that the error bound 𝜖 depends on DPPR itself, where more important node pairs (i.e., with larger DPPR) require a tighter error bound. Further, the premier objective turns to approximate level-ℓ DPPR, which can be solved by extending the PPR approximation methods [5,16,28,40,43,49,50,58,59,74,76,[88][89][90][91][92]94].…”

Section: Efficiency Challengesmentioning

confidence: 99%

“…Based on Lemma 6.1, the following theorem establishes the correctness of FORA-TP. In Table 1, we compare Tau-Push with FORA and FORA-TP in terms of index space, and time complexity for a random selected supernode S. For easy presentation, we set 𝑝 𝑓 = 1/𝑛 [74,[88][89][90] and 𝛿 = 𝑂 (1/𝑘) [74]. Besides, we assume the graph is scale-free, in which case 𝑚/𝑛 = 𝑂 (log 𝑛) and V 𝑖 ∈S 𝑑 ( V 𝑖 ) |𝐹 ( V 𝑖 ) | = 𝑂 (𝑘 log 𝑛).…”

Section: Comparing Tau-push With Alternativesmentioning

confidence: 99%

“…PPR computation. The efficient computation of PPR has been extensively studied [5,16,28,40,43,49,50,58,59,74,76,[88][89][90][91][92][93][94]. Among these works, BEAR [76] and BePI [50] improve the power iteration method [40] and achieve high efficiency by indexing several large matrices.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

PPRviz: Effective and Efficient Graph Visualization based on Personalized PageRank

Zhang¹,

Yang²,

Xiao³

et al. 2021

Preprint

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Graph visualization is an important problem that finds applications in various domains, e.g., social network analysis, traffic planning, and bioinformatics. Existing solutions for graph visualization, however, fail to scale to large graphs with millions of nodes, as they either provide inferior visualization results or incur significant computational cost. To address the deficiencies of prior works, we propose PPRviz, a multi-level visualization method for large graphs. Lying in the core of PPRviz is a new measure of graph node distance, PDist, that is specifically designed for visualization. In particular, PDist is formulated based on personalized PageRank, and it provides non-trivial theoretical guarantees for two well-adopted aesthetic measures. We present efficient algorithms for estimating PDist with provable accuracy and time complexity, while incurring small preprocessing costs. Extensive experiments show that PPRviz significantly outperforms 13 state-of-the-art competitors on 12 realworld graphs in terms of both effectiveness and efficiency, and that PPRviz provides interactive visualizations within one second on billion-edge graphs.

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Inferring Interaction Networks from Transcriptomic Data: Methods and Applications

Singh,

Singh

2024

Methods in Molecular Biology

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Personalized PageRank to a Target Node, Revisited

Cited by 43 publications

References 52 publications

An ecological network approach to predict ecosystem service vulnerability to species losses

An ecological network approach to predict ecosystem service vulnerability to species losses

PPRviz: Effective and Efficient Graph Visualization based on Personalized PageRank

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications

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