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Indepth blockchain network analysis with AI and Big Data

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This report aims at studying the RLC token and the behavior of actors within its network.

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Indepth blockchain network analysis with AI and Big Data

  1. 1. iExec's RLC token case study Indepth blockchain network analysis with AI and Big Data Supported by
  2. 2. 2 Context iExec is a blockchain-based decentralized cloud computing platform. The company has conducted an ICO in April 2017. Its token RLC is to be used through the smart contract associated to iExec platform. This report aims at studying the RLC token and the behavior of actors within its network from April 2017 (ICO) to October 2018: • Analysis of the token usage through several Key Performance Indicators; • Evolution of token distribution within the community; • Characterization of actor’s behavior all along the project development. Our theoretical approach invites us to interpret the transactional data-structure as directed graphs: • Wallets public keys are considered as vertices; • Edges describe interactions between these wallets: depending on our objectives, their weights can account for number of interaction, volume of exchanged cryptocurrency, modalities of exchanges between wallets, etc. After having produced all the relevant statistics to describe these structures and their evolution over time, we have used more sophisticated mathematical methods to analyze these graphs and get insights from them. We implement classical approaches as well as the state of the art in machine learning for graph analysis: • We generate multiple relevant metrics for any vertex or edge that describe wallets activity and networks interaction. Wallets related to exchange platforms have been identified this way. • We construct high-quality embeddings of our nodes and use this representation to cluster wallets according to their behavior, allowing us to study groups of coherent nodes and how these groups interacts with each other.
  3. 3. 3 Preliminary step: Global network modelization Considering the network structure as a directed graph allows us to obtain a visual representation of the overall network structuration. In addition to other algorithms such as centrality analysis and outlier detection, we managed to identify wallets with specific behavior like exchange platform’s wallet or wallets used by iExec team (heat points on the graph). We perform this as a preliminary step to exclude a few wallets (around 30 in this case) from our data set to focus on users and individual investors. This is very important in order to improve the accuracy of all the others methods we use to characterize the network activity. Then, this study focuses on network activity from individual actors. Future studies will focus on exchange platform’s activity. Representation of iExec token network with oriented edges: blue on the sender side and red on the receiver side Wallet’s closeness centrality rating (number of wallets per rate)
  4. 4. 4 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000 2017-16 2017-18 2017-20 2017-22 2017-24 2017-26 2017-28 2017-30 2017-32 2017-34 2017-36 2017-38 2017-40 2017-42 2017-44 2017-46 2017-48 2017-50 2017-52 2018-02 2018-04 2018-06 2018-08 2018-10 2018-12 2018-14 2018-16 2018-18 2018-20 2018-22 2018-24 2018-26 2018-28 2018-30 2018-32 2018-34 2018-36 2018-38 2018-40 2018-42 2018-44 New wallets and transactions per period Number of new wallets Number of transactions Introduction: General statistics about iExec network activity The iExec network activity can be split into several time periods: 1. ICO and first exchange listing (weeks 16 to 26, 2017) 2. Calm period during bull market (weeks 26 to 46, 2017) 3. Crypto-hype and Binance listing (weeks 46, 2017 to weeks 14, 2018) 4. Project development on bear market (weeks 14 to 44, 2018) On these different periods, different key events on the project development roadmap have generated several activity peaks: 1 2 3 4 5 Bittrex listing Release v1 + Airdrop Binance listing Ubisoft partnership announcement 6 1st worker drop Intel partnership announcement at Consensus 2018 1 2 3 5 6 4 Nb_new_wallets Nb_transactions The number of new wallets and transactions highlight the activity’s intensity of iExec network. Over the period considered, these number varied by a factor of seven between periods of lowest activity and highest ones. These metrics mainly highlight the newcomers. On the other hand, some wallets become inactive, while holding some tokens or not.
  5. 5. 5 0 2 000 000 4 000 000 6 000 000 8 000 000 10 000 000 12 000 000 14 000 000 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 2017-21 2017-23 2017-25 2017-27 2017-29 2017-31 2017-33 2017-35 2017-37 2017-39 2017-41 2017-43 2017-45 2017-47 2017-49 2017-51 2018-01 2018-03 2018-05 2018-07 2018-09 2018-11 2018-13 2018-15 2018-17 2018-19 2018-21 2018-23 2018-25 2018-27 2018-29 2018-31 2018-33 2018-35 2018-37 2018-39 2018-41 2018-43 Average and total volume of transactions Average number of tokens per transactions Total number of tokens transferred 0 50 000 000 100 000 000 150 000 000 200 000 000 250 000 000 0 10 000 20 000 30 000 40 000 50 000 60 000 70 000 80 000 2017-16 2017-18 2017-20 2017-22 2017-24 2017-26 2017-28 2017-30 2017-32 2017-34 2017-36 2017-38 2017-40 2017-42 2017-44 2017-46 2017-48 2017-50 2017-52 2018-02 2018-04 2018-06 2018-08 2018-10 2018-12 2018-14 2018-16 2018-18 2018-20 2018-22 2018-24 2018-26 2018-28 2018-30 2018-32 2018-34 2018-36 2018-38 2018-40 2018-42 2018-44 Average and total volume of transactions Average number of tokens per transactions Total number of tokens transferred Introduction: General statistics about iExec network activity Looking to the average and total volume of transactions on the RLC network, it is clear that ICO has been the most intense period for the network. If we focus on the network’s activity without considering the ICO-period, we note a high fluctuation between 2.000 and 12.000 RLC for the average volume of transactions, and 2M and 12M RLC for the total volume. At the same time, these variations are not that much correlated to the activity related to new wallets and transactions. This means hype periods don’t generate as much volume than they attract new users. This is confirmed by the fact that average and total volumes have different behaviors. During highly speculative periods, the average volume remains low whereas the total volume reaches a peak. During these periods, there is a surge in small individuals attracted by hype around the project. The average volume in these hype periods isn’t different from other periods, while total volume is superior due to a large number of active wallets. 1 1 2 2 3 3 5 5 6 6 4 4 Average_volume Volume_total
  6. 6. 6 -250 250 750 1 250 1 750 2 250 -250 250 750 1 250 1 750 2 250 2017-16 2017-18 2017-20 2017-22 2017-24 2017-26 2017-28 2017-30 2017-32 2017-34 2017-36 2017-38 2017-40 2017-42 2017-44 2017-46 2017-48 2017-50 2017-52 2018-02 2018-04 2018-06 2018-08 2018-10 2018-12 2018-14 2018-16 2018-18 2018-20 2018-22 2018-24 2018-26 2018-28 2018-30 2018-32 2018-34 2018-36 2018-38 2018-40 2018-42 2018-44 Unique senders and receivers per period Number of unique senders Number of unique receivers Introduction: General statistics about iExec network activity Looking at the number of senders and receivers for the RLC token, we understand that there is almost all the time more receivers than senders, which means a constant expansion for the iExec network. This is computed for each period on active wallets without considering inactive ones. Thus, we formalize an expansion ratio to highlight this point: • When the ratio is inferior to 1, the network is being expanded; • When the ratio is superior to 1, the network is being contracted. In this way, this expansion indicator doesn’t insure that active wallets remain active. It is more about knowing if there are more people holding the tokens at the end of the period than at the beginning. 1 2 3 5 64 Nb_senders Nb_receivers 0,00 0,20 0,40 0,60 0,80 1,00 1,20 2017-16 2017-18 2017-20 2017-22 2017-24 2017-26 2017-28 2017-30 2017-32 2017-34 2017-36 2017-38 2017-40 2017-42 2017-44 2017-46 2017-48 2017-50 2017-52 2018-02 2018-04 2018-06 2018-08 2018-10 2018-12 2018-14 2018-16 2018-18 2018-20 2018-22 2018-24 2018-26 2018-28 2018-30 2018-32 2018-34 2018-36 2018-38 2018-40 2018-42 2018-44 Decentralization ratio : unique senders / unique receivers
  7. 7. 7 Introduction: General statistics about iExec network activity We can also look at the general distribution of RLC token within the community (exchange platforms’ wallets excluded). We also don’t consider wallets with a balance inferior to 10 RLC. We note here that around 90% of wallets currently hold less than 10,000 tokens, while the large majority owns a quantity between 10 and 1,000 RLC. The distribution of wallets’ weight in each category is inversely proportional to the distribution of wallet’s number. Around 90% of the network value is in the hand of the 10% who hold more than 10,000 tokens. This representation also highlights the impact of December’s hype and Binance listing. The beginning of 2018 is characterized by a sudden surge in the number of wallets holding the RLC tokens, with mainly small balances. This evolution has nearly no impact on the weight’s distribution. On the other hand, the weight’s distribution has been constantly evolving since April to December 2017 with a decrease for wallets with a balance superior to 1,000,000 RLC. *The number of wallets represented here is related to wallets with a positive balance at the end of each month. Some active wallets don’t match this criteria. 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 Number of wallets per category 10 - 100 RLC 100 - 1,000 RLC 1,000 - 10,000 RLC 10,000 - 100,000 RLC 100,000 - 1,000,000 RLC More than 1,000,000 RLC 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Weight of wallets in each category 10 - 100 RLC 100 - 1,000 RLC 1,000 - 10,000 RLC 10,000 - 100,000 RLC 100,000 - 1,000,000 RLC More than 1,000,000 RLC
  8. 8. 8 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 2017-04 2017-05 2017-06 2017-07 2017-08 2017-09 2017-10 2017-11 2017-12 2018-01 2018-02 2018-03 2018-04 2018-05 2018-06 2018-07 2018-08 2018-09 2018-10 Number of wallets regarding different types of behaviors Micro incoming investor Little incoming investor Medium incoming investor Big incoming investor Micro trader Little trader Medium trader Big trader Micro outgoing investor Little outgoing investor Medium outgoing investor Big outgoing investor Micro holder Little holder Medium holder Big holder Part 1: Behavior analysis (macro view) To go further in the understanding of RLC’s network activity, we have used advanced machine learning algorithms to batch wallets following different kinds of behaviors*. This has been computed for each month, and we have simplified the representation with four types of behaviors (incoming investor, trader, outgoing investor, and holder) weighted with four different levels of activity (detailed in the next slide). We saw that activity peaks are mainly attracting incoming investors and traders, while creating holders over the next period. Outgoing investors are cashing out more intensely during and after these peaks, while trading activity is quickly decreasing after each peak. For the last few months, the number of active wallets has been low and most of the network is composed of holders. There are currently almost 7,000 wallets active on the iExec network, while a total of 27,000 have been used since ICO. *Top exchange wallet’s have been excluded before. 1 2 3 5 64 Nb_wallets_per_category
  9. 9. 9 Additional details on behavior analysis computation The used algorithms enable us to obtain very precise batches of wallets who have a similar behavior. For each cluster, we gather activity details (number of transactions, total volume involved, balance) to precisely characterize them each month. We obtain a variable number of clusters per period. We have built several indicators to interpret the type / size / intensity of wallet’s activity while creating different labels to determine their main orientation. Here are the main rules used on each period: • We consider wallets as incoming / outgoing investors for an increase / decrease superior to 25% of their wallet’s initial balance, with more than 100 tokens added / removed. • When the balance remains stable (-25% to +25%), we note wallets as holders when their trading volume is inferior to 10% of their balance, with a balance superior to 100 tokens. • Traders are so the one with a stable balance and a trading volume superior to 10% of their balance. • We have also batched remaining clusters in two categories related to inactive wallets or wallets with residual activity (balance < 100 tokens and volume < 250 tokens). They won't be represented in the previous and following graphs as they have very little impact on the global behavior of the network. We have settled a second labelling system to characterize clusters in their size / intensity. For each type of behavior, rules don’t involve the same indicator for us to better interpret the different labels while remaining in the same order of magnitude. We use the trading volume to characterize traders, the balance for holders, and the number of tokens added / removed for investors: • Micro: between 100 and 1,000 tokens; • Little: between 1,000 and 10,000 tokens; • Medium: between 10,000 and 100,000 tokens; • Big: superior to 100,000 tokens; We are also currently working on a user interface for actors to determine their own rules while interacting with the different graphs.
  10. 10. 10 0 10 000 000 20 000 000 30 000 000 40 000 000 50 000 000 60 000 000 70 000 000 2017-04 2017-05 2017-06 2017-07 2017-08 2017-09 2017-10 2017-11 2017-12 2018-01 2018-02 2018-03 2018-04 2018-05 2018-06 2018-07 2018-08 2018-09 2018-10 Weight of wallet's balance within different type of behavior's categories Micro incoming investor Little incoming investor Medium incoming investor Big incoming investor Micro trader Little trader Medium trader Big trader Micro outgoing investor Little outgoing investor Medium outgoing investor Big outgoing investor Micro holder Little holder Medium holder Big holder Part 1: Behavior analysis (macro view) Looking at the same behavior categories while counting the total balance in number of tokens reveal that big holders continuously own a large majority of tokens. The network activity seems to have reached an all-time low on the last few months, with very few active actors. We also note a decreasing number of tokens in the hand of the community, which means more and more tokens owned on a few top exchange wallets (around 30 excluded). *Top exchange wallet’s have been excluded 1 2 3 5 64 Total_balance_per_category
  11. 11. 11 0 20 000 000 40 000 000 60 000 000 80 000 000 100 000 000 120 000 000 140 000 000 160 000 000 180 000 000 2017-04 2017-05 2017-06 2017-07 2017-08 2017-09 2017-10 2017-11 2017-12 2018-01 2018-02 2018-03 2018-04 2018-05 2018-06 2018-07 2018-08 2018-09 2018-10 Volume exchanged by wallets within different type of behavior's categories Micro incoming investor Little incoming investor Medium incoming investor Big incoming investor Micro trader Little trader Medium trader Big trader Micro outgoing investor Little outgoing investor Medium outgoing investor Big outgoing investor Micro holder Little holder Medium holder Big holder Part 1: Behavior analysis (macro view) Finally, the volume exchanged by wallets in our different behavior categories shows the differences between major activity period. The volume considered here is the sum of inbound and outbound volumes for each category. This means there is double counting in these figures. The three first months have been the most active for the overall iExec network, around the ICO and the first exchange platform listing. The December hype and Binance listing didn’t succeed in making volume higher than in these first three months. Intel’s partnership announcement is also clearly visible on May 2018. Since several months, the network remains stable with a low exchanged volume activity. *Top exchange wallet’s have been excluded 1 2 3 5 64 Total_volume_per_category
  12. 12. 12 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 2017-04 2017-05 2017-06 2017-07 2017-08 2017-09 2017-10 2017-11 2017-12 Weight of wallets within different type of behavior's categories Micro incoming investor Little incoming investor Medium incoming investor Big incoming investor Micro trader Little trader Medium trader Big trader Micro outgoing investor Little outgoing investor Medium outgoing investor Big outgoing investor Micro holder Little holder Medium holder Big holder Part 1: Behavior analysis (focus on 2017) Looking more precisely to the evolution of the community during 2017, we saw a constant activity for new incoming investors which is fueling the growth of holders. Outgoing investors have been quite constant too, and trading activity high has occurred during the first exchange listing. After ICO, most of the coin were owned by large investors, who have moved after first exchange listing. They enjoy the price’s peak to cash out some of their position. After this, the network has been stable for a few months. Nb_wallets_per_category Total_balance_per_category 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 2017-04 2017-05 2017-06 2017-07 2017-08 2017-09 2017-10 2017-11 2017-12 Number of wallets regarding different types of behaviors Micro incoming investor Little incoming investor Medium incoming investor Big incoming investor Micro trader Little trader Medium trader Big trader Micro outgoing investor Little outgoing investor Medium outgoing investor Big outgoing investor Micro holder Little holder Medium holder Big holder
  13. 13. 13 0 10000000 20000000 30000000 40000000 50000000 60000000 2018-01 2018-02 2018-03 2018-04 2018-05 2018-06 2018-07 2018-08 2018-09 2018-10 Weight of wallets within different type of behavior's categories Micro incoming investor Little incoming investor Medium incoming investor Big incoming investor Micro trader Little trader Medium trader Big trader Micro outgoing investor Little outgoing investor Medium outgoing investor Big outgoing investor Micro holder Little holder Medium holder Big holder Part 1: Behavior analysis (focus on 2018) The beginning of 2018 is characterized with a high number of incoming investors. When the network activity is high due to speculation, little investors are coming creating later a large number of little holders. Trading activity has been fueled by a few major announcements (Ubisoft and Intel partnerships). This enable the network to remain active until the beginning of the summer. After this, active actors have been constantly decreasing. The weight of holder has remained largely predominant all along 2018. Nb_wallets_per_category Total_balance_per_category 0 1000 2000 3000 4000 5000 6000 7000 8000 2018-01 2018-02 2018-03 2018-04 2018-05 2018-06 2018-07 2018-08 2018-09 2018-10 Number of wallets regarding different types of behaviors Micro incoming investor Little incoming investor Medium incoming investor Big incoming investor Micro trader Little trader Medium trader Big trader Micro outgoing investor Little outgoing investor Medium outgoing investor Big outgoing investor Micro holder Little holder Medium holder Big holder
  14. 14. 14 Part 2: Behavior’s paths cross (macro view) While representing different metrics related to the different batches (number of wallets, balance or volume), we have put aside the relation between these categories. With the behavior’s paths cross view, we represent the way each category is fueling the other ones on the next period. In addition to our previous categories, we find there three new categories (in black, grey and white): the one associated to inactive wallets (no balance / no volume), the one for residual activity (linked to our labelling rules), and the one for new wallets which are to be active on the next period (NOT_CREATED). This last label is used to represent the behavior distribution of new wallets on each next period. With this representation, we can analyze the consistency of each category upon time, and their mutual relations from one period to the next one. Representation of behavior’s paths cross M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M1 M17 M18 M19
  15. 15. 15 Part 2: Behavior’s paths cross (focus on the hype period) We can focus on a specific period in the behavior’s paths cross representation. Here, we represent the Binance listing period (M11) with the adjacent ones to understand what kind of actors have become active after this event. We can see that the flow of newscomer is divided in three part: - Half of new active wallets (which are considered as “NOT_CREATED” on the precedent period [M10]) are joining the “Residual activity” clusters. - For the other half, some are becoming traders, and others are categorized as incoming investors. On the next month, all these new wallets are holders or inactive. These analysis can be performed on every period of interest. We highlight here the most significant period regarding the number of new wallets becoming active on the iExec network. M9 M10 M11 M12 M13
  16. 16. 16 Part 2: Behavior’s transfer matrix Then, we use the transfer matrix to analyze the behavior's path cross representation. For each wallet, we consider each couple of consecutive months and note its categories: we have a set of ordered pairs where every element represents a category transition of one wallet from a month to the next one. On the transfer matrix, we represent the distribution of pairs - lines account for the start month and column for the second one. This representation is normalized line-wise. The transfer matrix gives us more insights on the way batches are fueling each other: - Incoming investors are mainly fueling holder category, with a few becoming directly outgoing investors; - Traders and outgoing investors are largely becoming inactive; - Holders are largely remaining holders; - Inactive wallets are largely remaining inactive. We also note that 40% of new wallets (labeled as NOT_CREATED) are going directly to the residual activity category. A lot of wallets are dealing with a balance inferior to 100 tokens and a volume inferior to 250 tokens. This transfer matrix sums up all the behavior evolution during our period of interest. Using it as a network performance indicator enables us to analyze the impact of main news regarding the project development. Transfer matrix
  17. 17. 17 Conclusion To conclude on this study, our developments for analytic tools that can precisely describe and interpret a global token network activity are highly successful. We have demonstrated in this report that, with a rigorous and methodologic process, transactional flows reveal all their meaning with the adequate data science approach. About iExec, its token is clearly suffering the bear market. But this is not specific to RLC - it seems to be the case for all the other tokens as well. For now, developers are mainly accessing iExec’s marketplace through testnets (Kovan or Rinkeby). Knowing this, we did not focus on the utility smart contract in this report. However, when the market will be more mature, it will be definitely interesting to monitor the evolution of real usage compared to all financial transactions. From now, we are interested in performing advanced comparisons on different token networks, as well as following the evolution of other promising projects to measure and analyze their adoption rate. Then, we are looking for other tokens to study, to deliver different kind of insights and to support the overall blockchain ecosystem in its maturing journey. If you are interested to get your own token study, feel free to contact us for more details on our pricing modalities. All the data highlighted in this report comes from Nyctale’s own algorithms, fueled with Ethereum blockchain data. During the process to produce this report, we have defined different hypothesis for several important analytic steps: • To exclude top exchange’s wallets; • To cluster all the wallets based on similar behaviors; • To characterize and label behavior’s categories. Other methods could deliver different outcomes, regarding the hypothesis used.
  18. 18. Contact us contact@nyctale.io

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