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Centrality

centrality

The future looks bright for the world of blockchain. According to , the industry will generate an annual business value of over $175 billion by 2025, reaching $3 trillion by 2030. However, while these figures are impressive, there’s much more to the story. Gartner As businesses continue to leverage the functions of decentralized applications (DApps), obstacles remain for those building the underlying infrastructure. In reality, launching a blockchain project is often a daunting task, and the stakes are higher than ever as the hype cycle winds down. According to , investment in blockchain ventures is expected to hit $1.6 billion across 454 deals this year, a steep drop from the $4.1 billion invested in 2018. CB Insights But what does all this mean? In short, incumbent tech companies, blockchain-centric and otherwise, continue to capture and generate the majority of ecosystem value. All the while, newcomers face an uphill battle. For these new market entrants, fierce competition, long-standing incumbent relationships, and big budgets remain a hindrance - but there is a path forward. In response to the obstacles facing new DApp ventures, platforms like Centrality are establishing start-up communities that aim to bolster the success of new DApp ventures. BUIDLing a Community By sharing resources; tech, human, or otherwise, DApp projects on the Centrality platform naturally increase their chances of success - a welcome boost in light of market data. According to a 2015 report from , the average app loses 77% of its daily active users (DAUs) within three days of the initial install. Within 30 days, the app loses 90% of DAUs, and within 90 days, it’s over 95%. In essence, most apps lose all of their DAUs within three months of launching. Quettra When considering the implications of this trend across the decentralized ecosystem, the drop-off could be even more profound in the absence of a vast user base. As such, the Centrality platform offers immense value to community members looking for a safe place to experiment, innovate, and collaborate. Rebooting the Startup Lifecycle The industry’s recent experience with initial coin offerings (ICOs) highlights the shortcomings of existing start-up models. Once the engine of DApp funding, direct venture capital has eclipsed ICO funding for the . But what does this tell us? last four quarters In general, it speaks to the risks associated with overzealous DApp development in the absence of proven concepts and adequate resources. On the Centrality platform, these risks are mitigated through a human-centric approach to each stage of the start-up life cycle. By allowing users to interact with a range of DApps within the same ecosystem, every project benefits from the consolidation of resources - a welcome change from prominent industry fragmentation today. By leveraging this collective scale, DApp developers using the Centrality platform are more likely to overcome many of the incumbent startup hurdles. In turn, the trajectory of the existing startup lifecycle can be amended. When considering the primary obstacles to blockchain adoption, Centrality functionality is further galvanized. The Hype Cycle As mentioned, many DApp projects have over-promised and under-delivered. And while blockchain remains a transformative technology, there are limitations to what it can do. In a survey conducted by the , respondents expected a 24% return on investment (ROI) on their new blockchain projects. However, the reality is much lower at an average return of 10%. World Economic Forum Although the still refers to blockchain market segments, it's apparent that the initial hype has passed. The Centrality platform responds to this trend by providing an environment where projects work on value-added projects rooted in reality. 2019 Gartner Hype Cycle for Emerging Tech Stakeholder Buy-In Moving a blockchain project from proof of concept to production is impossible without stakeholder buy-in. The Centrality platform helps reduce the friction associated with this process by offering a more affordable, risk-mitigated approach to project development. Instead of making massive capital investments to start from scratch, projects can start using pre-built components out of the gate. Capturing Ecosystem Value At this point in blockchain market maturation, it's apparent that working with others is critical for successful blockchain implementation. To ensure consistent standards see application across the ecosystem, organizations must work collaboratively to overcome challenges. This approach reflects the intent of Centrality's platform, where DApp projects work together with a shared purpose. Complex Legacy Systems In many situations, tech companies begin exploring DApp development as part of a digital transformation project. However, this process can be expensive, especially when considering the investments made in legacy technology. Under these circumstances, Centrality’s catalogue of pre-built tools is particularly valuable, eliminating the cost of starting from scratch. Uncertainty In the WEF mentioned above, 59% of respondents said they had no confidence that the blockchain project would deliver a positive ROI before project commencement. As an emerging technology, this figure isn't particularly surprising, even in the face of limitations and scalability issues. The centrality platform helps allay fears of project success by dramatically improving scalability and lessening the risk of poor outcomes. survey BUIDLing in the Centrality Ecosystem Taking on incumbent market players with competing tech isn’t enough. As large organizations extend their advantage through , start-ups must leverage the values arising from collaborative processes. In this vein, the Centrality platform runs a consortium hybrid chain within the Ethereum network and uses several built-in improvements to performance and security. machine learning This infrastructure has been built to better-serve community members by: reducing latency and congestion on the platform; eliminating network fees when creating transactions; and circumventing stress on the public (Ethereum) network resulting from large transaction volumes. The Centrality ecosystem is made up of several underlying components, each interacting to provide a truly integrated development stack. This infrastructure underpins the primary building blocks of the Centrality platform. Source: The Centrality White Paper Sylo The majority of DApp projects migrating to the Centrality ecosystem choose to build within Sylo, a decentralized communication app and associated user interface. Comparable to WeChat, Sylo operates as "an app within an app." This super-app has a user base of over 80,000 and the functionality to support thousands of future "mini-apps." When users download Sylo, they gain immediate access to all Sylo-based apps within one platform. CENNZnet For those companies that wish to create a standalone application, the public CENNZnet blockchain supports the development of consumer-facing DApps. Under this model, projects deploy their smart contracts, but also gain access to the Centrality Developer Portal - a resource containing all the necessary app-building tools. If a resulting DApp proves particularly valuable to the broader ecosystem, the community can upvote it to a protocol with additional privileges, such as SingleSource and Sylo. PL^G Although CENNZnet underpins the majority of business in the Centrality ecosystem, some projects may require the flexibility of a fully customized blockchain application. The Centrality PL^G toolkit enables the seamless development of custom blockchain applications. Whether private or public, companies can create a blockchain with ease, avoiding the hassle of starting from scratch. For those projects requiring highly specific functionality, PL^G provides ultimate flexibility. Who’s Using Centrality? When Centrality’s native CENZZ tokens were issued via an ICO in early 2018, all tokens on offer - netting the company $80 million and placing it among the top ten biggest ICOs ever conducted. With a subsequent market capitalization falling in the (36th at the time of writing), ongoing performance speaks for itself. sold out within minutes top 50 Today, the Centrality platform is home to a diverse range of DApp projects, reinforcing initial expectations of robust adoption. Centrality apps have generated millions in revenue and have hundreds of thousands of users around the world - the Skoot app alone generates close to $100K a month. Based on the numbers, it's apparent that the ecosystem is gaining traction, validating its community-centric approach to DApp development. Source: Centrality Ecosystem DApps BUIDLing the Future of Blockchain As the competition for data, talent, and viable use cases heats up, the need for collaboration among start-ups will be essential to avoiding further industry consolidation. Facing this challenge head-on, Centrality offers a platform of services and a library of applications that act as DApp building blocks. Using this component-based model, startups are provided with ultimate flexibility over their projects while maintaining a cohesive feel across all ecosystem DApps. In an industry ripe with impressively polished user interfaces, this standardization facilitates the delivery of comparable user experiences across the Centrality ecosystem. There's no denying the future of blockchain is bright, and the same is true of those start-ups that choose to BUIDL with Centrality.

How One Blockchain Company BUIDLed Their Way to the Top 50

One of the most important steps of any data-related project is Exploratory Data Analysis (EDA). It is crucial to explore the distribution of the data to understand it and efficiently decide on the next steps. A straightforward way to explore data distribution is to study its central tendencies through measures of centrality. In this article, we’ll explore the three primary measures of centrality: Mean, Median, and Mode. We’ll discuss their strengths and weaknesses, along with practical examples using SQL and Python. The Mean: The Average Joe The mean, often referred to as the average, is calculated by summing all the values in a dataset and dividing by the number of values. It’s a straightforward way to find a central value. Let’s say we have a table named Sales with a column Revenue. The following query will give us the mean revenue per sale: SELECT AVG(Revenue) AS MeanRevenue
FROM Sales; When using Python, we can obtain the same result by importing Pandas and running the following code: import pandas as pd
# import your data into a pandas dataframe
mean_revenue = df['Revenue'].mean() Strengths and Weaknesses The mean leaves no man behind, meaning every data point is taken into consideration. Although this allows it to give us a holistic view of the data, it makes it highly susceptible to outliers. The Median: The Middle Ground The median is the middle value when the data is sorted, meaning there is an equal number of data points before and after it. Note: If there’s an even number of observations, the median is obtained by averaging the two middle values. We can obtain the median price using the following SQL query: SELECT 
DISTINCT PERCENTILE_CONT(0.5) 
    WITHIN GROUP (ORDER BY Revenue) OVER() AS MedianRevenue
FROM Sales; Using Python, however, obtaining the median is much more straightforward: median_revenue = df['Revenue'].median() Strengths and Weaknesses Since the median only considers the order of data points, it is not affected by outliers, making it a great indicator of the center when the data is not symmetrically distributed. However, this strength is also its weakness—it fails to capture the whole dataset. The Mode: The Most Popular The mode is the value that appears most frequently in a dataset. It’s particularly useful for categorical data where we want to know the most common category. To find the mode in SQL, we can execute the following query: SELECT Revenue, COUNT(*) AS Frequency
FROM Sales
GROUP BY Revenue
ORDER BY Frequency DESC
LIMIT 1; And for the Pythonistas out there, it’s even simpler: mode_revenue = df['Revenue'].mode() Strengths and Weaknesses The mode is useful for providing categorical insights, making it ideal for identifying the most common category or categories (bimodal or multimodal) in non-numeric datasets. However, it has limitations: if all values are unique, there may be no mode at all, and it tends to be less relevant for continuous data compared to the mean or median. Avengers Assemble Link to this heading The most efficient way to understand a dataset’s central tendencies is by considering all three measures of centrality. Each provides unique insights into the data distribution, allowing us to build a more comprehensive understanding of its shape. If mean ≈ median ≈ mode, the data is likely symmetrically distributed (think bell-shaped curve).
If mean > median, it suggests a right-skewed (positively skewed) distribution, where higher values pull the mean upwards.
If mean < median, it indicates a left-skewed (negatively skewed) distribution, meaning lower values pull the mean down. By observing all three measures, we get a clearer understanding of the data. If there’s a large difference between the mean and median, that could be a red flag for outliers or a skewed distribution, which would require further analysis or potential data transformations. Conclusion: The Bigger Picture Exploring central tendencies is a critical part of exploratory data analysis. The mean, median, and mode each serve different purposes, and understanding when and how to use them is critical for interpreting data correctly. Whether you’re working with continuous or categorical data, applying these measures helps uncover patterns and insights that guide the upcoming steps—whether it’s cleaning, transforming, or modeling. One of the most important steps of any data-related project is Exploratory Data Analysis (EDA). It is crucial to explore the distribution of the data to understand it and efficiently decide on the next steps. A straightforward way to explore data distribution is to study its central tendencies through measures of centrality. In this article, we’ll explore the three primary measures of centrality: Mean, Median, and Mode . We’ll discuss their strengths and weaknesses, along with practical examples using SQL and Python. Exploratory Data Analysis central tendencies Mean, Median, and Mode The Mean: The Average Joe The mean , often referred to as the average, is calculated by summing all the values in a dataset and dividing by the number of values. It’s a straightforward way to find a central value. mean Let’s say we have a table named Sales with a column Revenue . The following query will give us the mean revenue per sale: Sales Revenue SELECT AVG(Revenue) AS MeanRevenue
FROM Sales; SELECT AVG(Revenue) AS MeanRevenue
FROM Sales; When using Python, we can obtain the same result by importing Pandas and running the following code: import pandas as pd
# import your data into a pandas dataframe
mean_revenue = df['Revenue'].mean() import pandas as pd
# import your data into a pandas dataframe
mean_revenue = df['Revenue'].mean() Strengths and Weaknesses The mean leaves no man behind, meaning every data point is taken into consideration. Although this allows it to give us a holistic view of the data, it makes it highly susceptible to outliers . holistic view susceptible to outliers The Median: The Middle Ground The median is the middle value when the data is sorted, meaning there is an equal number of data points before and after it. median Note: If there’s an even number of observations, the median is obtained by averaging the two middle values. Note: If there’s an even number of observations, the median is obtained by averaging the two middle values. We can obtain the median price using the following SQL query: SELECT 
DISTINCT PERCENTILE_CONT(0.5) 
    WITHIN GROUP (ORDER BY Revenue) OVER() AS MedianRevenue
FROM Sales; SELECT 
DISTINCT PERCENTILE_CONT(0.5) 
    WITHIN GROUP (ORDER BY Revenue) OVER() AS MedianRevenue
FROM Sales; Using Python, however, obtaining the median is much more straightforward: median_revenue = df['Revenue'].median() median_revenue = df['Revenue'].median() Strengths and Weaknesses Since the median only considers the order of data points, it is not affected by outliers , making it a great indicator of the center when the data is not symmetrically distributed. However, this strength is also its weakness—it fails to capture the whole dataset . not affected by outliers capture the whole dataset The Mode: The Most Popular The mode is the value that appears most frequently in a dataset. It’s particularly useful for categorical data where we want to know the most common category. mode categorical data To find the mode in SQL, we can execute the following query: SELECT Revenue, COUNT(*) AS Frequency
FROM Sales
GROUP BY Revenue
ORDER BY Frequency DESC
LIMIT 1; SELECT Revenue, COUNT(*) AS Frequency
FROM Sales
GROUP BY Revenue
ORDER BY Frequency DESC
LIMIT 1; And for the Pythonistas out there, it’s even simpler: mode_revenue = df['Revenue'].mode() mode_revenue = df['Revenue'].mode() Strengths and Weaknesses The mode is useful for providing categorical insights , making it ideal for identifying the most common category or categories (bimodal or multimodal) in non-numeric datasets. However, it has limitations: if all values are unique, there may be no mode at all , and it tends to be less relevant for continuous data compared to the mean or median. categorical insights no mode at all Avengers Assemble Link to this heading Link to this heading The most efficient way to understand a dataset’s central tendencies is by considering all three measures of centrality . Each provides unique insights into the data distribution, allowing us to build a more comprehensive understanding of its shape. all three measures of centrality If mean ≈ median ≈ mode, the data is likely symmetrically distributed (think bell-shaped curve). If mean > median, it suggests a right-skewed (positively skewed) distribution, where higher values pull the mean upwards. If mean < median, it indicates a left-skewed (negatively skewed) distribution, meaning lower values pull the mean down. If mean ≈ median ≈ mode , the data is likely symmetrically distributed (think bell-shaped curve). mean ≈ median ≈ mode If mean > median , it suggests a right-skewed (positively skewed) distribution, where higher values pull the mean upwards. mean > median right-skewed If mean < median , it indicates a left-skewed (negatively skewed) distribution, meaning lower values pull the mean down. mean < median left-skewed By observing all three measures, we get a clearer understanding of the data. If there’s a large difference between the mean and median, that could be a red flag for outliers or a skewed distribution , which would require further analysis or potential data transformations. outliers skewed distribution Conclusion: The Bigger Picture Exploring central tendencies is a critical part of exploratory data analysis . The mean , median , and mode each serve different purposes, and understanding when and how to use them is critical for interpreting data correctly. Whether you’re working with continuous or categorical data, applying these measures helps uncover patterns and insights that guide the upcoming steps—whether it’s cleaning , transforming , or modeling . exploratory data analysis mean median mode cleaning transforming modeling

Understanding Measures of Centrality: A Deep Dive into Mean, Median, and Mode

Ethereum Is More Than Crypto. It’s a Machine for Making Worlds

What Makes a Scene Important? This AI Knows

Authors:
(1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg;
(2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Table of Links Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 4. Scene Saliency Classification Model Using the set of generated silver-standard labels for scene saliency, we train a neural network-based classification model to predict scene saliency. We formulate this task as a sequence labeling task where the model takes a sequence of scenes M = {S1, S2, ..., SN} as input and predicts a sequence of binary labels Y = {y1, y2, ..., yN } denoting whether a scene is salient. The model consists of two components, as shown in Figure 1. The first component is a scene encoder which computes scene representations by concatenating the sentences in the scene and encodes them using a pre-trained language model. Next, to learn contextual scene representation across the whole movie, we further encode the scene embeddings generated by the scene encoder using a transformer (Vaswani et al., 2017) block (L layers stacked), with unmasked self-attention initialized with random weights (Liu and Lapata, 2019). To preserve the sequence of the scenes, we add positional encodings to scene representations obtained from the first component. The final contextualized representation of the scenes is then used to classify whether scenes are salient or not. The model is trained for binary sequence labeling using the binary cross-entropy loss. 4.1 Dataset To train the saliency model, we used the ScriptBase corpus (Gorinski and Lapata, 2015) that contains preprocessed scripts of movies with Wikipedia summaries. We removed the movies used in our gold-standard MENSA dataset from Scriptbase from the training set. This resulted in a training set containing 824 movie scripts, for which we generated silver-standard scene saliency labels using the model of Mirza et al. (2021), as previously discussed. We randomly split our gold-standard scene saliency dataset of 100 movies, using half of it for validation and the other half for testing. 4.2 Baselines Majority Class: We used predicting the majority class as a simple baseline for classification. The dataset is highly imbalanced, with non-salient being the majority class. Supervised Bi-LSTM: For a supervised baseline, we used a bi-directional LSTM (Bi-LSTM) to learn contextual representation for the classification of scene saliency. Again, we computed scene representations by concatenating the sentences and encoding them using a pre-trained language model. Note that the alignment model of Mirza et al. (2021) cannot be used as a baseline for saliency classification: it requires summaries to align to movie scripts at test time. In a summarization scenario, no summaries are available at test time. 4.3 Implementation Details Our Scene Saliency Model and baseline models employed RoBERTa-large as the pre-trained scene encoder. Representation of a scene is computed using the first token’s last hidden state of the model. The movie encoder transformer block has 10 layers with 16 heads and a feedforward hidden size of 2048. As the binary scene labels in the dataset are highly imbalanced, we used weighted binary cross entropy. We employed AdamW with β1 = 0.9, β2 = 0.999 as our optimizer. The learning rate was fixed at 5e-5. For the baseline TextRank model, we performed a grid search for hyperparameters and used λ1 = 0.7, λ2 = 0.3, and K = 15% of move length. For Bi-LSTM, we used hidden dimension of size 512 followed by a fully connected layer. 4.4 Results The results of our saliency classification model and the baselines are summarized in Table 4. We report macro-averaged precision (P), recall (R), and F1 score for each model, as the labels are highly imbalanced given that only a limited number of scenes in each movie are salient. Our model outperforms the baselines and achieves 68.38, 68.13, and 68.01 on precision, recall, and F1. The results show that the majority baseline performance is equivalent to random guessing (macro-average). The unsupervised TextRank model has higher precision, recall, and F1 than the majority baseline, which indicates that it is able to correctly predict some scenes as salient based on the centrality score. Also, the high value of λ1 (see Section 4.3) signifies that the backward-looking context is more important than forward-looking context for computing scene importance. The transformer-based scene saliency model achieves better performance than other baselines, indicating the effectiveness of transformer layers in learning the context across scene representations, which is helpful in classifying scene saliency. We also found that a higher number of layers worked better for the transformer, which indicates that more layers help in capturing complex relationships in the input. See Appendix C for k-fold cross-validation on the test set. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg; (2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Authors: Authors: (1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg; (2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Table of Links Part 1 Part 1 Part 2 Part 2 Part 3 Part 3 Part 4 Part 4 Part 5 Part 5 Part 6 Part 6 4. Scene Saliency Classification Model Using the set of generated silver-standard labels for scene saliency, we train a neural network-based classification model to predict scene saliency. We formulate this task as a sequence labeling task where the model takes a sequence of scenes M = {S1, S2, ..., SN} as input and predicts a sequence of binary labels Y = {y1, y2, ..., yN } denoting whether a scene is salient. The model consists of two components, as shown in Figure 1. The first component is a scene encoder which computes scene representations by concatenating the sentences in the scene and encodes them using a pre-trained language model. Next, to learn contextual scene representation across the whole movie, we further encode the scene embeddings generated by the scene encoder using a transformer (Vaswani et al., 2017) block (L layers stacked), with unmasked self-attention initialized with random weights (Liu and Lapata, 2019). To preserve the sequence of the scenes, we add positional encodings to scene representations obtained from the first component. The final contextualized representation of the scenes is then used to classify whether scenes are salient or not. The model is trained for binary sequence labeling using the binary cross-entropy loss. 4.1 Dataset To train the saliency model, we used the ScriptBase corpus (Gorinski and Lapata, 2015) that contains preprocessed scripts of movies with Wikipedia summaries. We removed the movies used in our gold-standard MENSA dataset from Scriptbase from the training set. This resulted in a training set containing 824 movie scripts, for which we generated silver-standard scene saliency labels using the model of Mirza et al. (2021), as previously discussed. We randomly split our gold-standard scene saliency dataset of 100 movies, using half of it for validation and the other half for testing. 4.2 Baselines Majority Class: We used predicting the majority class as a simple baseline for classification. The dataset is highly imbalanced, with non-salient being the majority class. Majority Class: Supervised Bi-LSTM: For a supervised baseline, we used a bi-directional LSTM (Bi-LSTM) to learn contextual representation for the classification of scene saliency. Again, we computed scene representations by concatenating the sentences and encoding them using a pre-trained language model. Supervised Bi-LSTM: Note that the alignment model of Mirza et al. (2021) cannot be used as a baseline for saliency classification: it requires summaries to align to movie scripts at test time. In a summarization scenario, no summaries are available at test time. 4.3 Implementation Details Our Scene Saliency Model and baseline models employed RoBERTa-large as the pre-trained scene encoder. Representation of a scene is computed using the first token’s last hidden state of the model. The movie encoder transformer block has 10 layers with 16 heads and a feedforward hidden size of 2048. As the binary scene labels in the dataset are highly imbalanced, we used weighted binary cross entropy. We employed AdamW with β1 = 0.9, β2 = 0.999 as our optimizer. The learning rate was fixed at 5e-5. For the baseline TextRank model, we performed a grid search for hyperparameters and used λ1 = 0.7, λ2 = 0.3, and K = 15% of move length. For Bi-LSTM, we used hidden dimension of size 512 followed by a fully connected layer. 4.4 Results The results of our saliency classification model and the baselines are summarized in Table 4. We report macro-averaged precision (P), recall (R), and F1 score for each model, as the labels are highly imbalanced given that only a limited number of scenes in each movie are salient. Our model outperforms the baselines and achieves 68.38, 68.13, and 68.01 on precision, recall, and F1. The results show that the majority baseline performance is equivalent to random guessing (macro-average). The unsupervised TextRank model has higher precision, recall, and F1 than the majority baseline, which indicates that it is able to correctly predict some scenes as salient based on the centrality score. Also, the high value of λ1 (see Section 4.3) signifies that the backward-looking context is more important than forward-looking context for computing scene importance. The transformer-based scene saliency model achieves better performance than other baselines, indicating the effectiveness of transformer layers in learning the context across scene representations, which is helpful in classifying scene saliency. We also found that a higher number of layers worked better for the transformer, which indicates that more layers help in capturing complex relationships in the input. See Appendix C for k-fold cross-validation on the test set. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Smart Summaries: Teaching AI to Pick Key Scenes from Scripts

Authors:
(1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland;
(2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain;
(3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland;
(4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland;
(5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Table of Links 1 Introduction 2 State of Art 3 Projects Selection and Data Collection 3.1 Blockchain data 3.2 Twitter data 4 Data Analysis and Results 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.3 Hashtags 4.4 The role of the social network community 5 Conclusions and References 5 CONCLUSIONS The NFT phenomenon is a topic that became mainstream during 2021 and has continued to receive increasing attention from early 2022 to the present. This blockchain-based technology has also come to the forefront thanks to projects such as BAYCs whose images have been purchased by celebrities and traded at very high prices as reported by several media. In this paper, we analyzed the phenomenon of NFTs, focusing on the class related to PFPs. We chose 18 projects based on our knowledge in the field and based on their importance in terms of volumes traded, users, and social activity. It was illustrated through analysis of social activity how the concept of social community is essential to ensure liquidity and value of PFP projects, i.e., used as social icons. The centrality and influence of the community related to BAYCs was demonstrated, that despite being second to CryptoPunks in terms of volumes traded, social activity is much higher and central to every other project in the industry. This work aimed to lay the groundwork for more in-depth future studies on understanding whether patterns and causality conditions may exist between elements such as the number of tweets, average price of a collection, Twitter users, and Ethereum wallet. REFERENCES [1] Oluwaseun Ajao, Jun Hong, and Weiru Liu. 2015. A survey of location inference techniques on Twitter. Journal of Information Science 41, 6 (2015), 855–864. https://doi.org/10.1177/0165551515602847 [2] Dune Analytics. 2022. rchen8: OpenSea Dashboard. Retrieved June 6, 2022 from https://dune.xyz/rchen8/opensea [3] Joseph Bonneau, Jonathan Anderson, Ross Anderson, and Frank Stajano. 2009. Eight friends are enough: social graph approximation via public listings. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems. 13–18. https://doi.org/10.1145/1578002.1578005 [4] Evan Brinckman, Andrey Kuehlkamp, Jarek Nabrzyski, and Ian J. Taylor. 2019. Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data. In 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 717–722. https: //doi.org/10.1109/ICTC46691.2019.8939746 [5] Pinar Caglayan and Zehra Ozkan. 2021. NFTs and copyright: challenges and opportunities. Journal of Intellectual Property Law& Practice 16, 10 (06 2021). https://doi.org/10.1093/jiplp/jpab104 [6] Umit Can and Bilal Alatas. 2019. A new direction in social network analysis: Online social network analysis problems and applications. Physica A: Statistical Mechanics and its Applications 535 (2019), 122372. https://doi.org/10.1016/j.physa.2019.122372 [7] Simone Casale-Brunet, Paolo Ribeca, Patrick Doyle, and Marco Mattavelli. 2021. Networks of Ethereum Non-Fungible Tokens: A Graph-Based Analysis of the ERC-721 Ecosystem. In IEEE Blockchain 2021, 06 - 08 December. https://doi.org/10.1109/Blockchain53845.2021.00033 [8] Joseph Coelho, Dawson D’almeida, Scott Coyne, Nathan Gilkerson, Katelyn Mills, and Praveen Madiraju. 2019. Social Media and Forecasting Stock Price Change. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 2. 195–200. https://doi.org/10.1109/ COMPSAC.2019.10206 [9] CoinMarketCap. 2022. Ethereum historical data. Retrieved June 6, 2022 from https://coinmarketcap.com/currencies/ethereum/historical-data/ [10] Iain J Cruickshank and Kathleen M Carley. 2020. Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering. Applied Network Science 5, 1 (2020), 1–40. https://doi.org/10.1007/s41109-020-00317-8 [11] Roberta De Michele, Stefano Ferretti, and Marco Furini. 2019. On helping broadcasters to promote tv-shows through hashtags. Multimedia Tools and Applications 78, 3 (2019), 3279–3296. https://doi.org/10.1007/s11042-018-6510-7 [12] Decrypt.co. 2022. Hot Ethereum NFT Platform LooksRare Is Rife With Wash Trading - And OK With It. Retrieved June 6, 2022 from https: //decrypt.co/90317/ethereum-nft-market-looksrare-wash-trading [13] Joshua Fair!eld. 2021. Tokenized: The Law of Non-Fungible Tokens and Unique Digital Property. Indiana Law Journal, Forthcoming (04 2021). [14] Stefano Ferretti, Marco Furini, and Manuela Montangero. 2019. Diabetes: What are italian twitter users talking about?. In 2019 28th International Conference on Computer Communication and Networks (ICCCN). IEEE, 1–6. https://doi.org/10.1109/ICCCN.2019.8847039 [15] Anastasia Giachanou and Fabio Crestani. 2016. Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR) 49, 2 (2016), 1–41. https://doi.org/10.1145/2938640 [16] Google. 2022. BigQuery. Retrieved June 6, 2022 from https://cloud.google.com/bigquery/ [17] Shrey Jain, Camille Bruckmann, and Chase McDougall. 2022. NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks. arXiv preprint arXiv:2204.12932 (2022). [18] Arnav Kapoor, Dipanwita Guhathakurta, Mehul Mathur, Rupanshu Yadav, Manish Gupta, and Ponnurungam Kumaraguru. 2022. Tweetboost: Influence of social media on nft valuation. arXiv preprint arXiv:2201.08373 (2022). [19] OpenSea. 2022. OpenSea, the largest NFT marketplace. Retrieved June 6, 2022 from https://opensea.io/ [20] Daniel M Romero, Brendan Meeder, and Jon Kleinberg. 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web. 695–704. https: //doi.org/10.1145/1963405.1963503 [21] İlker Türker and Eyüb Ekmel Sulak. 2018. A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links. International Journal of Modern Physics B 32, 04 (2018), 1850029. https://doi.org/10.1142/S0217979218500297 [22] Twitter. 2022. Search Tweets API. Retrieved June 6, 2022 from https://developer.twitter.com/en/docs/twitter-api/tweets/search/introduction [23] Kishore Vasan, Milán Janosov, and Albert-László Barabási. 2022. Quantifying NFT-driven networks in crypto art. Scienti!c reports 12, 1 (2022), 1–11. [24] Shermin Voshmgir. 2020. Token Economy: How the Web3 reinvents the Internet. Vol. 2. Token Kitchen. [25] Andi Wiliam, Sasmoko Sasmoko, Wibowo Kosasih, and Yasinta Indrianti. 2020. Sentiment analysis of social media engagement to purchasing intention. In Understanding Digital Industry. Routledge, 362–365. https://doi.org/10.1201/9780367814557-86 [26] Nick Yee, Jeremy N. Bailenson, and Nicolas Ducheneaut. 2009. The Proteus Effect: Implications of Transformed Digital Self Representation on Online and O&ine Behavior. Communication Research 36, 2 (2009), 285–312. https://doi.org/10.1177/0093650208330254 arXiv:https://doi.org/10.1177/0093650208330254 This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland; (2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain; (3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland; (4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland; (5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Authors: Authors: (1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland; (2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain; (3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland; (4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland; (5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Table of Links 1 Introduction 1 Introduction 2 State of Art 2 State of Art 3 Projects Selection and Data Collection 3 Projects Selection and Data Collection 3.1 Blockchain data 3.1 Blockchain data 3.2 Twitter data 3.2 Twitter data 4 Data Analysis and Results 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.3 Hashtags 4.3 Hashtags 4.4 The role of the social network community 4.4 The role of the social network community 5 Conclusions and References 5 Conclusions and References 5 CONCLUSIONS The NFT phenomenon is a topic that became mainstream during 2021 and has continued to receive increasing attention from early 2022 to the present. This blockchain-based technology has also come to the forefront thanks to projects such as BAYCs whose images have been purchased by celebrities and traded at very high prices as reported by several media. In this paper, we analyzed the phenomenon of NFTs, focusing on the class related to PFPs. We chose 18 projects based on our knowledge in the field and based on their importance in terms of volumes traded, users, and social activity. It was illustrated through analysis of social activity how the concept of social community is essential to ensure liquidity and value of PFP projects, i.e., used as social icons. The centrality and influence of the community related to BAYCs was demonstrated, that despite being second to CryptoPunks in terms of volumes traded, social activity is much higher and central to every other project in the industry. This work aimed to lay the groundwork for more in-depth future studies on understanding whether patterns and causality conditions may exist between elements such as the number of tweets, average price of a collection, Twitter users, and Ethereum wallet. REFERENCES [1] Oluwaseun Ajao, Jun Hong, and Weiru Liu. 2015. A survey of location inference techniques on Twitter. Journal of Information Science 41, 6 (2015), 855–864. https://doi.org/10.1177/0165551515602847 [2] Dune Analytics. 2022. rchen8: OpenSea Dashboard. Retrieved June 6, 2022 from https://dune.xyz/rchen8/opensea [3] Joseph Bonneau, Jonathan Anderson, Ross Anderson, and Frank Stajano. 2009. Eight friends are enough: social graph approximation via public listings. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems. 13–18. https://doi.org/10.1145/1578002.1578005 [4] Evan Brinckman, Andrey Kuehlkamp, Jarek Nabrzyski, and Ian J. Taylor. 2019. Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data. In 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 717–722. https: //doi.org/10.1109/ICTC46691.2019.8939746 [5] Pinar Caglayan and Zehra Ozkan. 2021. NFTs and copyright: challenges and opportunities. Journal of Intellectual Property Law& Practice 16, 10 (06 2021). https://doi.org/10.1093/jiplp/jpab104 [6] Umit Can and Bilal Alatas. 2019. A new direction in social network analysis: Online social network analysis problems and applications. Physica A: Statistical Mechanics and its Applications 535 (2019), 122372. https://doi.org/10.1016/j.physa.2019.122372 [7] Simone Casale-Brunet, Paolo Ribeca, Patrick Doyle, and Marco Mattavelli. 2021. Networks of Ethereum Non-Fungible Tokens: A Graph-Based Analysis of the ERC-721 Ecosystem. In IEEE Blockchain 2021, 06 - 08 December. https://doi.org/10.1109/Blockchain53845.2021.00033 [8] Joseph Coelho, Dawson D’almeida, Scott Coyne, Nathan Gilkerson, Katelyn Mills, and Praveen Madiraju. 2019. Social Media and Forecasting Stock Price Change. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 2. 195–200. https://doi.org/10.1109/ COMPSAC.2019.10206 [9] CoinMarketCap. 2022. Ethereum historical data. Retrieved June 6, 2022 from https://coinmarketcap.com/currencies/ethereum/historical-data/ [10] Iain J Cruickshank and Kathleen M Carley. 2020. Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering. Applied Network Science 5, 1 (2020), 1–40. https://doi.org/10.1007/s41109-020-00317-8 [11] Roberta De Michele, Stefano Ferretti, and Marco Furini. 2019. On helping broadcasters to promote tv-shows through hashtags. Multimedia Tools and Applications 78, 3 (2019), 3279–3296. https://doi.org/10.1007/s11042-018-6510-7 [12] Decrypt.co. 2022. Hot Ethereum NFT Platform LooksRare Is Rife With Wash Trading - And OK With It. Retrieved June 6, 2022 from https: //decrypt.co/90317/ethereum-nft-market-looksrare-wash-trading [13] Joshua Fair!eld. 2021. Tokenized: The Law of Non-Fungible Tokens and Unique Digital Property. Indiana Law Journal, Forthcoming (04 2021). [14] Stefano Ferretti, Marco Furini, and Manuela Montangero. 2019. Diabetes: What are italian twitter users talking about?. In 2019 28th International Conference on Computer Communication and Networks (ICCCN). IEEE, 1–6. https://doi.org/10.1109/ICCCN.2019.8847039 [15] Anastasia Giachanou and Fabio Crestani. 2016. Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR) 49, 2 (2016), 1–41. https://doi.org/10.1145/2938640 [16] Google. 2022. BigQuery. Retrieved June 6, 2022 from https://cloud.google.com/bigquery/ [17] Shrey Jain, Camille Bruckmann, and Chase McDougall. 2022. NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks. arXiv preprint arXiv:2204.12932 (2022). [18] Arnav Kapoor, Dipanwita Guhathakurta, Mehul Mathur, Rupanshu Yadav, Manish Gupta, and Ponnurungam Kumaraguru. 2022. Tweetboost: Influence of social media on nft valuation. arXiv preprint arXiv:2201.08373 (2022). [19] OpenSea. 2022. OpenSea, the largest NFT marketplace. Retrieved June 6, 2022 from https://opensea.io/ [20] Daniel M Romero, Brendan Meeder, and Jon Kleinberg. 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web. 695–704. https: //doi.org/10.1145/1963405.1963503 [21] İlker Türker and Eyüb Ekmel Sulak. 2018. A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links. International Journal of Modern Physics B 32, 04 (2018), 1850029. https://doi.org/10.1142/S0217979218500297 [22] Twitter. 2022. Search Tweets API. Retrieved June 6, 2022 from https://developer.twitter.com/en/docs/twitter-api/tweets/search/introduction [23] Kishore Vasan, Milán Janosov, and Albert-László Barabási. 2022. Quantifying NFT-driven networks in crypto art. Scienti!c reports 12, 1 (2022), 1–11. [24] Shermin Voshmgir. 2020. Token Economy: How the Web3 reinvents the Internet. Vol. 2. Token Kitchen. [25] Andi Wiliam, Sasmoko Sasmoko, Wibowo Kosasih, and Yasinta Indrianti. 2020. Sentiment analysis of social media engagement to purchasing intention. In Understanding Digital Industry. Routledge, 362–365. https://doi.org/10.1201/9780367814557-86 [26] Nick Yee, Jeremy N. Bailenson, and Nicolas Ducheneaut. 2009. The Proteus Effect: Implications of Transformed Digital Self Representation on Online and O&ine Behavior. Communication Research 36, 2 (2009), 285–312. https://doi.org/10.1177/0093650208330254 arXiv:https://doi.org/10.1177/0093650208330254 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How Social Engagement Drove NFT Market Success in 2022

Knowledge Graphs May Be the Missing Link Businesses Need for an AI that Works

Графиките на знанието може да са липсващата връзка, от която фирмите се нуждаят за AI, който работи

Vidensgrafer kan være det manglende led, som virksomheder har brug for for en AI, der virker

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Graf Konesans yo ka lyen ki manke biznis yo bezwen pou yon AI ki travay

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Žinių diagramos gali būti trūkstama nuoroda, kurios įmonėms reikia norint sukurti veikiančią AI

Мэдлэгийн графикууд нь ажилладаг хиймэл оюун ухаанд хэрэгтэй бизнесүүдэд дутагдаж буй холбоос байж магадгүй юм

දැනුම ප්‍රස්ථාර යනු ව්‍යාපාරවලට ක්‍රියාත්මක වන AI එකක් සඳහා අවශ්‍ය නැතිවූ සබැඳිය විය හැකිය.

Znalostné grafy môžu byť tým chýbajúcim článkom, ktorý firmy potrebujú na umelú inteligenciu, ktorá funguje

نالج گرافس گمشدہ لنک ہو سکتے ہیں کاروباروں کو کام کرنے والے AI کی ضرورت ہے۔

Bilim grafiklari korxonalarda ishlaydigan sun'iy intellekt uchun zarur bo'lgan etishmayotgan havola bo'lishi mumkin

Biểu đồ tri thức có thể là liên kết còn thiếu mà doanh nghiệp cần cho một AI hoạt động

Now, every published HackerNoon story is automatically shared across multiple platforms, including Pinterest, Threads, X/Twitter, Bluesky, Mastodon, FlipBoard, and via RSS (free, full text). Distribution FTW! This builds on some of our existing methods to amplify the publish button, like also auto-publishing all new stories on the Arweave blockchain, the terminal reader, and the lite version of story (without javascript). Hitting the publish button amplifies your content, giving it massive exposure across a wide range of networks. It’s a powerful way to boost your reach and maximize visibility with minimal effort! Threads Each post includes your story's headline, link and uses the first tag as a hashtag, and, if provided, tags your social handle. 1. Digital Transformations Methodology - When Agile can fall short in Cloud Procurement. #cloud 2. This article explores how to implement a permission system in Langflow workflows using Permit.io’s ABAC capabilities. #llms 3. 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The TechBeat: Towards Glory: Lumoz OG NFT Event is Now Live! (11/20/2024) 3. The TechBeat: Towards Glory: Lumoz OG NFT Event is Now Live! (11/20/2024) 3. The TechBeat: Towards Glory: Lumoz OG NFT Event is Now Live! (11/20/2024) BONUS: The HackerNoon Newsletter The HackerNoon Newsletter HackerNoon’s daily tech injection delivers a curated selection of 5 must-read stories, handpicked by our editors from hundreds of weekly submissions. This does not include all stories, just the 5 very best stories every day. Subscribe to The HackerNoon Newsletter here. Subscribe to The HackerNoon Newsletter here. Subscribe to The HackerNoon Newsletter here. HackerNoon's Tech Brief HackerNoon's Tech Brief All HackerNoon stories are assigned a category by writers and editors. Every story published on HackerNoon enters a queue to publish on a category-specific tech brief. 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Live action NFTs, Character IP, and Alien Conjecture with Paul Wight (The Big Show), Neil Stevenson-Moore & David Smooke 2. Discussing Crypto, NFT, and Web3 Scams with HackerNoon 2. Discussing Crypto, NFT, and Web3 Scams with HackerNoon 2. Discussing Crypto, NFT, and Web3 Scams with HackerNoon 3. Gathering Threat Intelligence to Strengthen Security 3. Gathering Threat Intelligence to Strengthen Security 3. Gathering Threat Intelligence to Strengthen Security More Story Distribution HackerNoon offers several other ways to increase the distribution and readership of your stories, making it easy to amplify your reach. In addition to automatic sharing across multiple platforms, HackerNoon also manually boosts native updates to top stories and provides enhanced visibility through our business blogging package, ensuring high-quality content gets the attention it deserves. boosts native updates to top stories For even greater exposure, we offer additional ad-hoc options. 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