PageRank centrality for temporal networks

Lv, Laishui; Zhang, Kun; Zhang, Ting; Bardou, Dalal; Zhang, Jiahui; Cai, Ying

doi:10.1016/j.physleta.2019.01.041

Cited by 33 publications

(15 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A network-based ranking of metabolites was achieved via calculating the PageRank (PR) centrality. PR is a link analysis algorithm that measures the importance of a vertex by considering three factors: (1) the number of edges it receives; (2) the edge propensity of the connected vertices; (3) the centrality of the connected vertices [25]. The higher the PR value, the more important the metabolite is in the disease module.…”

Section: Network-based Ranking Of Metabolitesmentioning

confidence: 99%

Metabolic network-based identification of plasma markers for non-small cell lung cancer

Guo

Zhao

et al. 2021

Anal Bioanal Chem

View full text Add to dashboard Cite

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis.

show abstract

Section: Network-based Ranking Of Metabolitesmentioning

confidence: 99%

Metabolic network-based identification of plasma markers for non-small cell lung cancer

Guo

Zhao

et al. 2021

Anal Bioanal Chem

View full text Add to dashboard Cite

show abstract

“…Similarly, BC and CC are not applicable in large networks because of their high computation time complexity. PR is based on global information, so it works very well in directed networks but is not suitable for undirected ones [26]. Similarly, the K-shell algorithm takes into account the core or periphery position of the nodes in the networks to investigate influential nodes.…”

Section: Introductionmentioning

confidence: 99%

Identification of Influential Nodes via Effective Distance‐based Centrality Mechanism in Complex Networks

et al. 2021

View full text Add to dashboard Cite

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.

show abstract

“…36,37 PageRank is also used as a reference model to compare rankings that use new techniques. 18 PageRank has also recently been used to analyze opinion formation, 28 for networks that change with time while preserving their nodes, 42 to analyze risk in financial networks, 62 to include the concept of trust on weighted social networks, 15 to study the vulnerability of gas and electricity networks, 55 to rank citation networks, 44 and to rank products following multiple criteria in dynamic markets. 54 There are also new numerical methods for the PageRank algorithm (see Ref.…”

Section: Introductionmentioning

confidence: 99%

Parametric controllability of the personalized PageRank: Classic model vs biplex approach

Flores

Garcı́a

Pedroche

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

Measures of centrality in networks defined by means of matrix algebra, like PageRank-type centralities, have been used for over 70 years. Recently, new extensions of PageRank have been formulated and may include a personalization (or teleportation) vector. It is accepted that one of the key issues for any centrality measure formulation is to what extent someone can control its variability. In this paper, we compare the limits of variability of two centrality measures for complex networks that we call classic PageRank (PR) and biplex approach PageRank (BPR). Both centrality measures depend on the so-called damping parameter α that controls the quantity of teleportation. Our first result is that the intersection of the intervals of variation of both centrality measures is always a nonempty set. Our second result is that when α is lower that 0.48 (and, therefore, the ranking is highly affected by teleportation effects) then the upper limits of PR are more controllable than the upper limits of BPR; on the contrary, when α is greater than 0.5 (and we recall that the usual PageRank algorithm uses the value 0.85), then the upper limits of PR are less controllable than the upper limits of BPR, provided certain mild assumptions on the local structure of the graph. Regarding the lower limits of variability, we give a result for small values of α. We illustrate the results with some analytical networks and also with a real Facebook network.

show abstract

PageRank centrality for temporal networks

Cited by 33 publications

References 19 publications

Metabolic network-based identification of plasma markers for non-small cell lung cancer

Metabolic network-based identification of plasma markers for non-small cell lung cancer

Identification of Influential Nodes via Effective Distance‐based Centrality Mechanism in Complex Networks

Parametric controllability of the personalized PageRank: Classic model vs biplex approach

Contact Info

Product

Resources

About