Dataset Information

Identification of influencers through the wisdom of crowds.

ABSTRACT: Identifying individuals who are influential in diffusing information, ideas or products in a population remains a challenging problem. Most extant work can be abstracted by a process in which researchers first decide which features describe an influencer and then identify them as the individuals with the highest values of these features. This makes the identification dependent on the relevance of the selected features and it still remains uncertain if triggering the identified influencers leads to a behavioral change in others. Furthermore, most work was developed for cross-sectional or time-aggregated datasets, where the time-evolution of influence processes cannot be observed. We show that mapping the influencer identification to a wisdom of crowds problem overcomes these limitations. We present a framework in which the individuals in a social group repeatedly evaluate the contribution of other members according to what they perceive as valuable and not according to predefined features. We propose a method to aggregate the behavioral reactions of the members of the social group into a collective judgment that considers the temporal variation of influence processes. Using data from three large news providers, we show that the members of the group surprisingly agree on who are the influential individuals. The aggregation method addresses different sources of heterogeneity encountered in social systems and leads to results that are easily interpretable and comparable within and across systems. The approach we propose is computationally scalable and can be applied to any social systems where behavioral reactions are observable.

SUBMITTER: Tanase R

PROVIDER: S-EPMC6047770 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identification of influencers through the wisdom of crowds.

Tanase Radu R Tessone Claudio J CJ Algesheimer René R

PloS one 20180716 7

Identifying individuals who are influential in diffusing information, ideas or products in a population remains a challenging problem. Most extant work can be abstracted by a process in which researchers first decide which features describe an influencer and then identify them as the individuals with the highest values of these features. This makes the identification dependent on the relevance of the selected features and it still remains uncertain if triggering the identified influencers leads ...[more]

PMID: 30011290

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Project description:BACKGROUND:In drug design, an efficient structure-based optimization of a ligand needs the precise knowledge of the protein-ligand interactions. In the absence of experimental information, docking programs are necessary for ligand positioning, and the choice of a reliable program is essential for the success of such an optimization. The performances of four popular docking programs, Gold, Glide, Surflex and FlexX, were investigated using 100 crystal structures of complexes taken from the Directory of Useful Decoys-Enhanced database. RESULTS:The ligand conformational sampling was rather efficient, with a correct pose found for a maximum of 84 complexes, obtained by Surflex. However, the ranking of the correct poses was not as efficient, with a maximum of 68 top-rank or 75 top-4 rank correct poses given by Glidescore. No relationship was found between either the sampling or the scoring performance of the four programs and the properties of either the targets or the small molecules, except for the number of ligand rotatable bonds. As well, no exploitable relationship was found between each program performance in docking and in virtual screening; a wrong top-rank pose may obtain a good score that allows it to be ranked among the most active compounds and vice versa. Also, to improve the results of docking, the strengths of the programs were combined either by using a rescoring procedure or the United Subset Consensus (USC). Oddly, positioning with Surflex and rescoring with Glidescore did not improve the results. However, USC based on docking allowed us to obtain a correct pose in the top-4 rank for 87 complexes. Finally, nine complexes were scrutinized, because a correct pose was found by at least one program but poorly ranked by all four programs. Contrarily to what was expected, except for one case, this was not due to weaknesses of the scoring functions. CONCLUSIONS:We conclude that the scoring functions should be improved to detect the correct poses, but sometimes their failure may be due to other varied considerations. To increase the chances of success, we recommend to use several programs and combine their results. Graphical abstract Summary of the results obtained by semi-rigid docking of crystallographic ligands. The docking was done on 100 protein-ligand X-ray structures, taken from the DUD-E database, and using four programs, Glide, Gold, Surflex and FlexX. Based on the docking results, we applied our United Subset Consensus method (USC), for which only the top4-rank poses are relevant. The number of complexes for which the best pose is correct, is represented by the gray boxes, the blue and red boxes correspond to the number of complexes with a correct pose ranked as the top 1 or within the top 4. A pose is considered correct when its root-mean-square deviation from the crystal structure is less than 2 Å.

Dataset Information

Identification of influencers through the wisdom of crowds.

Publications

Identification of influencers through the wisdom of crowds.

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