BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence

The emerging prosperity of cryptocurrencies, such as Bitcoin, has come into the spotlight during the past few years. Cryptocurrency exchanges, which act as the gateway to this world, now play a dominant role in the circulation of Bitcoin. Thus, delving into the analysis of the transaction patterns of exchanges can shed light on the evolution and trends in the Bitcoin market, and participants can gain hints for identifying credible exchanges as well. Not only Bitcoin practitioners but also researchers in the financial domains are interested in the business intelligence behind the curtain. However, the task of multiple exchanges exploration and comparisons has been limited owing to the lack of efficient tools. Previous methods of visualizing Bitcoin data have mainly concentrated on tracking suspicious transaction logs, but it is cumbersome to analyze exchanges and their relationships with existing tools and methods. In this paper, we present BitExTract, an interactive visual analytics system, which, to the best of our knowledge, is the first attempt to explore the evolutionary transaction patterns of Bitcoin exchanges from two perspectives, namely, exchange versus exchange and exchange versus client. In particular, BitExTract summarizes the evolution of the Bitcoin market by observing the transactions between exchanges over time via a massive sequence view. A node-link diagram with ego-centered views depicts the trading network of exchanges and their temporal transaction distribution. Moreover, BitExTract embeds multiple parallel bars on a timeline to examine and compare the evolution patterns of transactions between different exchanges. Three case studies with novel insights demonstrate the effectiveness and usability of our system.

[1]  Jean-Daniel Fekete,et al.  Exploring Entity Behavior on the Bitcoin Blockchain , 2017 .

[2]  Young Bin Kim,et al.  Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies , 2016, PloS one.

[3]  Ilya A. Strebulaev,et al.  A Market-Based Study of the Cost of Default , 2012 .

[4]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[5]  Jing Yang,et al.  VAET: A Visual Analytics Approach for E-Transactions Time-Series , 2014, IEEE Transactions on Visualization and Computer Graphics.

[6]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[7]  Hannes Hartenstein,et al.  Could Network Information Facilitate Address Clustering in Bitcoin? , 2017, Financial Cryptography Workshops.

[8]  Björn Scheuermann,et al.  Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies , 2016, IEEE Communications Surveys & Tutorials.

[9]  Niklas Elmqvist,et al.  TimeMatrix: Analyzing Temporal Social Networks Using Interactive Matrix-Based Visualizations , 2010, Int. J. Hum. Comput. Interact..

[10]  Steven Lee,et al.  Anomaly Detection in the Bitcoin System - A Network Perspective , 2016, ArXiv.

[11]  Michael Burch,et al.  A Taxonomy and Survey of Dynamic Graph Visualization , 2017, Comput. Graph. Forum.

[12]  Fabian Beck,et al.  Visualizing Dynamic Hierarchies in Graph Sequences , 2016, IEEE Transactions on Visualization and Computer Graphics.

[13]  Carmel McNaught,et al.  Using Wordle as a Supplementary Research Tool , 2010 .

[14]  Bitcoin Risk Analysis , 2014 .

[15]  Tamara Munzner,et al.  Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context , 2008, IEEE Transactions on Visualization and Computer Graphics.

[16]  Ted E. Senator,et al.  The NASD Regulation Advanced-Detection System (ADS) , 1998, AI Mag..

[17]  Dieter W. Fellner,et al.  Visual analysis of contagion in networks , 2015, Inf. Vis..

[18]  Jeremy Clark,et al.  SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies , 2015, 2015 IEEE Symposium on Security and Privacy.

[19]  Kwan-Liu Ma,et al.  Temporal Multivariate Networks , 2013, Multivariate Network Visualization.

[20]  K.V. Nesbitt,et al.  Finding trading patterns in stock market data , 2004, IEEE Computer Graphics and Applications.

[21]  Li Lin,et al.  The Fish-eye Visualization of Foreign Currency Exchange Data Streams , 2005, APVIS.

[22]  Malte Möser,et al.  An inquiry into money laundering tools in the Bitcoin ecosystem , 2013, 2013 APWG eCrime Researchers Summit.

[23]  I G JPC,et al.  Weighted Average , 1858 .

[24]  Fergal Reid,et al.  An Analysis of Anonymity in the Bitcoin System , 2011, PASSAT 2011.

[25]  Yike Guo,et al.  Visualizing Dynamic Bitcoin Transaction Patterns , 2016, Big Data.

[26]  Neil Gandal,et al.  Competition in the Cryptocurrency Market , 2014, SSRN Electronic Journal.

[27]  Nagiza F. Samatova,et al.  Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph , 2017, Financial Cryptography Workshops.

[28]  Ulrik Brandes,et al.  Asymmetric Relations in Longitudinal Social Networks , 2011, IEEE Transactions on Visualization and Computer Graphics.

[29]  William Wright,et al.  Louvain Clustering for Big Data Graph Visual Analytics , 2013 .

[30]  Sooyong Park,et al.  Where Is Current Research on Blockchain Technology?—A Systematic Review , 2016, PloS one.

[31]  David S. Ebert,et al.  A Survey on Visual Analysis Approaches for Financial Data , 2016, Comput. Graph. Forum.

[32]  John T. Stasko,et al.  SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data , 2009, IEEE Transactions on Visualization and Computer Graphics.

[33]  Andrew B. Whinston,et al.  Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence , 2016, MIS Q..

[34]  S A R A H M E I K L E J O H N,et al.  A Fistful of Bitcoins Characterizing Payments Among Men with No Names , 2013 .

[35]  Jean-Daniel Fekete,et al.  BitConduite: Visualizing and Analyzing Activity on the Bitcoin Network , 2017, EuroVis.

[36]  Tyler Moore,et al.  Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk , 2013, Financial Cryptography.

[37]  William Ribarsky,et al.  WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions , 2007, 2007 IEEE Symposium on Visual Analytics Science and Technology.

[38]  Michael Burch,et al.  The State of the Art in Visualizing Dynamic Graphs , 2014, EuroVis.

[39]  Roberto Tamassia,et al.  Bitconeview: visualization of flows in the bitcoin transaction graph , 2015, 2015 IEEE Symposium on Visualization for Cyber Security (VizSec).