Detecting malicious accounts in permissionless blockchains using temporal graph properties

Overview of attention for article published in Applied Network Science, February 2021

Altmetric Badge

About this Attention Score

Good Attention Score compared to outputs of the same age (67th percentile)
Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

So far, Altmetric has seen 7 X posts from 7 X users, with an upper bound of 10,236 followers.

RT @networkspapers: Appl. Netw. Sci.: Detecting malicious accounts in permissionless blockchains using temporal graph properties https://t.…

RT @networkspapers: Appl. Netw. Sci.: Detecting malicious accounts in permissionless blockchains using temporal graph properties https://t.…

Appl. Netw. Sci.: Detecting malicious accounts in permissionless blockchains using temporal graph properties https://t.co/92FFY1qNAC

RT @ragarwa2: Our paper (with @Awesme_Indians and @sandeepkshukla) on detecting malicious accounts in #Blockchain using concepts from #NetS…

RT @ragarwa2: Our paper (with @Awesme_Indians and @sandeepkshukla) on detecting malicious accounts in #Blockchain using concepts from #NetS…

Our paper (with @Awesme_Indians and @sandeepkshukla) on detecting malicious accounts in #Blockchain using concepts from #NetSci and #MachineLearning is out https://t.co/CWfSg1dBZq. Thanks to @appliednetsci

Detecting malicious accounts in permissionless blockchains using temporal graph properties https://t.co/DWONn23J1X