Blockchain Visualization
2022
- 22Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage22
- Abstract Views22
Artifact Description
Bitcoin has become the center of attention in recent years, the blockchain has experienced exponential growth since its introduction in 2009. Satoshi Nakamoto hoped to create a decentralized system where users do not need to trust a central authority. Ever since then, Bitcoin has been used to buy all sorts of items, ranging from a benign pizza to illicit drugs and services. In this presentation we will explore some novel ways of taint analysis and the challenges associated with our methodology. We introduce two heuristics, one which allows us to more easily explore the blockchain from output to input (as opposed to input to output). We also introduce a heuristic to assign taint to bitcoin addresses we think might be suspicious. We also intend to produce a visualization that we hope can be used to spot patterns associated with illicit activities or any mixing services.
Bibliographic Details
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