I am thrilled to announce that I joined the global Ashoka fellowship network for social entrepreneurship. Ashoka is the world’s first and largest platform for social entrepreneurs with 3500 fellows from 93 countries around the world. This engagement puts our work with Graph Commons on a whole new level as it will enable our team to focus more on our system changing strategies and priorities.

When it comes to technology, we usually engage with the question of how. In my most recent talk at the European Changemaker Summit in Barcelona, I presented the question of why; why work on a project like Graph Commons.

Today, it is news to no user of digital technologies that we are being tracked and mapped whether we are using them for our work, or pleasure; with or without consent. The monopolies of such tech platforms create thick data maps of all our activities and preferences. However, we have no access to those cartographies created by the data we provide; all we get at the end of the day are some petty personalized recommendations. The massive data accumulation in the hands of the few, in other words, serves surveillance capitalism instead of smart use of technologies for good. As users, therefore, we find ourselves in a severe data asymmetry; while these platforms know everything about us, we know very little about how they work, what their real interests are, what they collect, or how they use the accumulated data. This situation has consequences not only in manipulation of our everyday personal behavior, but also in our collective decisions as societies, hence the election frauds we see around the world.

Efforts in creating alternatives run up against the inertia of these infrastructures; an inertia that relies on network effects, which lock us in and consolidate power in a single platform or product. By now, we know that withdrawal does not work, negation does not work, acceleration does not work. As McKenzie Wark writes, “we need to build new infrastructures within the ruins of the old.” [1]

What if, instead of capturing a social graph like Facebook, a knowledge graph like Google, a transportation graph like Uber, or a music graph like Spotify, we enable building graph commons, an equitable network of graphs owned and controlled fully and collectively by those who create it. This was and still is the idea we started to build on Graph Commons more than 5 years ago.

Over 30,000 members on 5 continents use Graph Commons to transform their own data into interactive network maps and untangle complex relations that impact them and their communities. Global Climate Change Deniers Network, The Jeffrey Epstein Network, Code of Acquisitions, Syrian Refugees Support Network, Decentralized Governance Network are some recent public trending graphs created collaboratively. There are many other use cases such as ecosystem mapping, impact mapping, organizational analysis, content curation, network analysis of blockchain activities, business strategies, investments, industry-specific knowledge graphs, and mapping of policies. These graphs help communities or teams to be on the same page about a complex issue by providing a holistic picture while also allowing exploration of specific paths and patterns between entities. Network maps enable us to discover hidden opportunities, and to see what issues are more central and urgent, which ones are in the periphery. The cluster analysis provides insight about the organic groups that are not visible otherwise. The bridges between those clusters reveal possibilities of solidarity and partnerships among groups who work on seemingly separate issues.

In 2020, we will make the Graph Commons system more robust, develop the most requested features (such as synchronized data integration with productivity tools), and create deep and strong partnerships with communities we serve. We also work on a brand new infrastructure to enable equitable interoperability among separate graph data by different initiatives.

The value offered by Graph Commons and its social impact increase together with the community and we are grateful to you for that! We hope 2020 to be the year of sustainable growth, solidarity, and transforming impact. I wish you a happy and peaceful new year.

[1] The Vectoralist Class, Part Two. McKenzie Wark, 2016

Exciting News from Graph Commons and Happy 2020! was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article investigates the dynamics of membership growth and voting in MolochDAO, a decentralized autonomous organization for collectively coordinating funds to support Ethereum development. Examining the voting activity in MolochDAO, we identified a tactical voting scheme that leaves it susceptible to manipulation. Because voter turnout will likely never be 100% and voting dynamics may deviate from protocol incentives, we should study empirical data wherever available and use those learnings to guide our protocol designs.

The level of participation in collective decision making is a dynamic metric that affects the security of plutocratic governance systems, where voting is not “one person, one vote”, but weighted by the token holdings of each account. When voting turnout is low, the powerful accounts — token holders with an outsized proportion of total supply — can significantly swing the numbers. This article examines this risk, particularly in MolochDAO, and suggests potential remedies.

DAO Basics

A decentralized autonomous organization (DAO) is an organization whose decision-making processes are transparent and enforced programmatically by a blockchain rather than a central authority.

After the tragic failure of the first DAO on Ethereum in 2016, several new DAO projects emerged. DAOs are typically used for collective fund coordination to support projects within the blockchain ecosystem, but the potential for DAOs to serve as new social coordination tools goes far beyond finance (Kei Kreutler’s talk “Autonomous ecologies at a distance” presents a good summary). Today, to start a DAO for your community you can design and implement the smart contract logic directly using best practices on the blockchain of choice, fork and redeploy an existing one such as MolochDAO, which is considered a minimum viable DAO because of its simplicity, or you can use a service such as Aragon or DAO Stack.

MolochDAO Membership Growth

MolochDAO works because it has off-chain reputation within Ethereum community. — Ameen Soleimani

The MolochDAO smart contract on Ethereum was deployed on Feb 14th 2019 and 22 founding members each donated 100 ETH for a total of 2,200 ETH (330K USD at the time). As of August 12th 2019, MolochDAO has 71 members. To become a member you have to know an existing member who can submit a membership application on your behalf, tribute some amount of ETH to the MolochDAO funding pool, and then have the members vote you in.

While these are early data points, we have analyzed the MolochDAO proposal activity. To join MolochDAO, an existing member (Proposer) creates a proposal to add an Ethereum account as a member (Beneficiary). The above graph is built based on all the passed proposals using the Proposer -> Proposal -> Beneficiary relationship. So far we observe that the membership growth is centralized, that is, a single account has proposed most new members. Yet from the periphery of the beneficiary graph above we see new member proposals are emerging. In the long run, we may observe a preferential attachment pattern in the growth of the membership network, where most new members are proposed by the most connected nodes, which further reinforces their influence.

From the plot of membership shares above we see Vitalik Buterin and Joseph Lubin each have 1000 shares, ten times the voting power of most members. One account (Jez San from FunFair) has 250 shares, 52 accounts have 100 shares, and few accounts have around 10 shares.

MolochDAO Voting Turnout

The voting activity in MolochDAO is concentrated around the MolochDAO founders, Ethereum Foundation, Consensys, and Fun Fair, meaning they are the most active participants in proposal voting so far.

The voter turnout rate affects the relative power distribution and security risk. In theory, if Buterin and Lubin both vote NO on a proposal, a minimum of 20 accounts, each with 100 shares voting YES, will be required to counter it. However, given the current turnout rates (max 30%), it is unlikely the remaining members will be able to gather support to oppose a Buterin + Lubin voting block.

Adaptive Quorum Biasing for the low voter turnout problem

Like MolochDAO, Polkadot uses a token-weighted voting system. However, to counter the low voter turnout problem, Polkadot proposes the Adaptive Quorum Biasing scheme, in which the supermajority required for a referendum to pass changes as a function of percent voter turnout.

A positive turnout bias requires a heavy supermajority of aye votes to carry at low turnouts, but as turnout increases towards 100%, it becomes a simple majority-carries as above. We call this a “positive” turnout bias because the required margin of ayes increases as turnout increases.

A negative turnout bias requires a heavy supermajority of nay votes to reject at low turnouts, but as turnout increases towards 100%, it becomes a simple majority-carries as above. We call this a “negative” turnout bias because the required margin of nays increases as turnout increases.

If MolochDAO had an adaptive quorum schema in its voting system, then the low turnout rates around 30% would require higher majority to pass a proposal.

Time-lock voting for the token-weighted voting problem

To limit the problem of large holders having outsized influence, a common problem in all share-based governance systems, Polkadot introduces the notion of time-lock voting, an approach that allows token holders to increase the weight of their vote by locking their shares for a certain amount of time after voting has ended, trading opportunity cost for increased voting power.

With this mechanism, a voter with five tokens willing to lock them up only for the minimum allowed duration would have the same number of votes as a voter with only one token happy to have it locked for the maximum amount of time.

If MolochDAO had time-lock voting, then, even with a low voter turnout, opposing a Vitalik + Lubin voting block (assuming they lock their tokens for the minimum allowed duration) would take the remaining members to lock their shares for the maximum amount of time.

Conclusion

Although these solutions to the problems of low voter turnout and weighted voting are game-theoretically sound, there are no guarantees in reality. As the number of participants grow and the system increases in complexity, information asymmetry can impede members making sense of, and coordinating around proposals.

Reducing information asymmetry for members can be achieved by tracking critical metrics, including explanations and visualizations of voting data. Furthermore, network topology measurements of on-chain activity could be natively supported in blockchains and integrated into governance mechanisms. In the end, learning from our experience and continuing to improve our social coordination tools in ways that enfranchise and enable a broad community is how we progress toward more equitable governance in decentralized networks.

Data modeling, visualization, and analysis are done using Graph Commons. Data sourced from TheGraph.

Thanks to Sam Hart for conversations and editing which contributed to this post.

Accountability in decentralized networks: The MolochDAO Case was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

We are organizing a Substrate workshop with Parity Technologies at New Lab in New York on Sunday May 12th, 2019.

Substrate is an open-source modular framework for building blockchains, and is the backbone of the upcoming Polkadot network. Substrate provides the means for building high-performance specialized blockchains that can connect to Polkadot, a multi-chain platform that addresses blockchain governance, interoperability, and scalability issues. With Substrate you can develop blockchains without rewriting the fundamentals and benefit from a collection of modules.

In this workshop, you will learn how to build your first custom blockchain using Substrate. More particularly, you will learn how to build a custom Crypto Collectables “DAppchain” during this live working session. We will walk you through the end-to-end process for setting up your working environment, going through the basics of the run-time development, and finally building a game from the ground up.

By the end of this workshop, you will have all the tools and knowledge needed to build a custom blockchain using Substrate!

Workshop is free, and seats are limited, RSVP here:
https://www.meetup.com/meetup-group-eEMURxBh/events/261105483/

The workshop will be followed with a Polkadot Meetup, where Polkadot founders and other teams building with Substrate will present.
https://www.meetup.com/meetup-group-eEMURxBh/events/261105680/

Workshop: Build Your Own Blockchain with Substrate was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

Blockchain teknolojisinin temellerini anlamak ve açtığı yeni fırsatları keşfetmek için bir günlük workshop 5 Mayıs Cumartesi ATÖLYE İstanbul’da.

Graph Commons ve Project Interactions ekipleri tarafından geliştirilen “Design Thinking Blockchains” metodunun kullanılacağı bir tam günlük workshop 5 Mayıs Cumartesi günü ATÖLYE ev sahipliğinde gerçekleştirilecek.

Workshop Başvurusu (yer sınırlıdır):
http://bit.ly/design-thinking-blockchains-workshop-tr

Herkes Bitcoin, Ethereum, kriptoparalar, tokenlar ve ICO (“Initial Coin Offering”) ile fonlanmakta olan projeler hakkında konuşuyor. Peki tüm bu projelerin gerçekleşmesini sağlayan blokzincirleri aslında nasıl çalışıyor? Akıllı sözleşmeler, dijital para alışverişinin ötesinde yeni nesil uygulamaları nasıl sağlıyor? Merkezi sistemlerin hakimiyetini kaybedeceği bir dünyada yeni fırsatları keşfetmek için nasıl yaratıcı düşünebilir ve yeni kripto-projeleri geliştirebiliriz?

Workshop 3 bölümden olıuşuyor:

1. Kavrayış — Temel blokzinciri kavramları ve işleyişi görsel anlatımlar, gerçek hayattan örnekler, örnek uygulamalar, ve interaktif araçlar ile adım adım ders olarak konu uzmanları tarafından anlatılacak.

2. Uygulama— Katılımcılar gruplar halinde istedikleri bir sektörü yakından inceleyecek, temel aktörleri haritalayarak içgörüler çıkartacak ve gayrimerkeziyet fırsatlarını belirleyecek. Bu aşamada tasarım düşüncesi yöntemleri, paydaş analizi, ağ modelleme, deneyim haritası yöntemlerini birleştiren “Design Thinking Blockchains” metodunu öğrenilecek ve kullanılacak.

3. Sunumlar & Tartışma — Her grup kendi çalışmasını sunacak, atölye yürütüclerinden ve katılımcılardan geri bildirimler ile tartışma yapılacak.

Çalışmanın konuları:

• Gayrimerkeziyetin prensipleri
• Diijtal imzalar
• Kriptografik hash fonksiyonları
• Dağıtık Hesap Defteri Teknolojisi
• “Proof of Work” ve mutabakat mekanizmaları
• Mining ve Validation
• Teşvik modelleri
• Ağ etkileri
• Akıllı sözleşmeler

Kimler katılmalı

• Girişimciler ya da yeni bir girişim düşünenler. Trendlerin ötesinde temel yapıları anlamak ve kendi projelerinde blokzinciri kullanmak isteyenler.
• Yatırımcılar ya da yatırımcı olmak isteyenler. Kirpto-varlıkları borsadaki pazar/fiyat dinamiklerinin ötesinde anlamak ve değerlendirmek isteyenler.
• Yaratıcı düşünen tasarımcı, programcı, ya da iş geliştirenler. Blokzincirini temelden anlayarak kolları sıvamak isteyenler.

*Blok zinciri ya da programlama konusunda deneyim şart değildir.

Neler öğreneceğiz

Çalışma katılımcılara blokcinzicir alanında güçlü bir temel, yaratıcı bir vizyon, ve pratik beceriler kazandırmak amacıyla tasarlanmıştır. Bu atölyeden sonra katılımcılar şunlara sahip olacaktır:

• Blokzinciri teknolojisinin nasıl çalıştığını kavramak.
• Kendi alanında gayri-merkeziyetin etkilerini anlamak ve tespit edebilmek.
• Design thinking yöntemlerini blokzinciri ile uygulayarak gayri-merkezi sistemler modellemek ve analiz etmek.
• Yeni bir kripto-varlık ya da blokzinciri uygulaması kurgulamayı adım adım yapabilmek.
• Gelecekte blokzinciri ile geliştirilen yeni uygulamaları gürültünün arasından görebilmek, anlamak ve konumlandırabilmek.

Yer, Tarih, Süre ve Kayıt

5 Mayıs Cumartesi 10:00–18:00

ATÖLYE

Adres: Tarihi Bomonti Bira Fabrikası Silahşör Cad. Birahane Sok. No: 1 Şişli, İstanbul

Workshop ücreti kişi başı 350 TL, öğrenciler için 150 TL (KDV dahil)

*Katılımcı kontenjanı 60 kişi ile sınırlı olup, öncelikli işlem esasına göre kontenjan imkânı verilecektir.

*Blok zinciri ya da programlama konusunda deneyim şart değildir.

*ATÖLYE’de çay, kahve ve atıştırma servisi olacaktır.

*Workshop dili Türkçe olacaktır.

Workshop başvurusu:
http://bit.ly/design-thinking-blockchains-workshop-tr

Design Thinking Blockchains Workshop, Istanbul was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

Understand the fundamentals of blockchain technology and explore its radical potential for new opportunities. Sunday, March 4, New Lab, Brooklyn, NY

One-day workshop, co-hosted by Graph Commons and Project Interactions, on creative and critical use of blockchain technology and what it means for entrepreneurial minds, curious investors, and creators. How do blockchains actually work to achieve decentralization? How do smart contracts enable new generation of applications beyond digital money exchange? How do we think and build creatively to explore new opportunities in a world after decentralization?

Register for the workshop here (limited seats):
http://bit.ly/blockchain-design-thinking-workshop

The workshop will be conducted in three parts:

1. Understand — We will start with a lecture introducing the core blockchain ideas through step-by-step visual explanations and real-world examples. We will provide interactive tools for participants to reinforce their understanding of complex concepts.
2. Create — We will divide into smaller groups and facilitate a rapid deep-dive into selected domains to extract insights and identify decentralization opportunities. We will utilize hands-on design thinking methods including user/stakeholder analysis, network modelling, journey mapping, and brainstorming.
3. Present & Discuss — We will conclude with each group presenting their work, feedback from facilitators and discussion with all participants.

Topics include:

• Principles of Decentralization
• Digital Signatures
• Cryptographic Hash Functions
• Distributed Ledger Technology
• Proof of Work
• Mining / Validation
• Consensus Mechanisms
• Incentive Models
• Network Effects
• Smart Contracts

* No programming or prior blockchain experience required.
* Morning coffee and lunch will be available.

Who should attend

• Entrepreneurial minds, who think beyond the hype and are interested in building a systemic perspective to start projects or organizations in their domains.
• Curious investors, who would like to build a foundational understanding of crypto assets beyond stock market dynamics to inform their investment aspirations.
• Creative technologists, designers, developers, and hackers who are looking for a well-grounded starting point to get their hands dirty.

What will you learn

We designed the workshop to help participants establish a strong foundation, a creative perspective, and practical methods. After this workshop, you will be able to:

• Identify the implications of decentralization that are most relevant to you.
• Hypothesize the impact of blockchains on your life, expertise, and business domain in an informed way.
• Apply hands-on design thinking methods to model and analyze decentralized systems.
• See through the hype/noise around blockchains and learn to process upcoming innovations.
• Walk through the steps for envisioning a new crypto asset / blockchain application.

Venue

The workshop will take place in New Lab, a creative technology hub in Brooklyn Navy Yard, whose residents include entrepreneurs in AI, connected devices, nanotechnology, robotics, urban tech, and more.

Design Thinking for Blockchains Workshop
Sunday, March 4th, 2018
New Lab, Brooklyn, NY

Register for the workshop here (limited seats):
http://bit.ly/blockchain-design-thinking-workshop

We’d love to hear your feedback, let us know if you have any questions or comments at workshop@graphcommons.com.

Design Thinking for Blockchains Workshop was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

Each day Graph Commons members map data-networks about a variety of topics. This activity is highly creative and fundamentally different than traditional network science use cases (e.g., fraud analysis, security intelligence, social networks). We’ve been curating our homepage to highlight some of these inspiring data projects ranging from the mapping of Trump’s cabinet, to partnerships on energy corridors, to dependencies of software packages, to power relations in the art world. However, this activity has been more intense than we can keep up with.

Today, we are excited to announce the feed, displaying recent activities on Graph Commons. Within the feed, you can easily engage with graphs or stories that spark your interest: explore and share nodes, start a conversation by adding a comment, or simply click ‘recommend’ to express your support for creators. As you scroll down the feed, you will be inspired by the creative possibilities of network data mapping.

In most social media, activity feeds are arranged to capture a larger portion of your attention real-estate in order to maximize advertising revenue. The algorithms control what you see, creating filter bubbles. On Graph Commons, the feed displays all public activities ordered chronologically, for everyone in the same order. There is no black-box algorithm behind the scenes optimized for your attention. It’s one activity feed for everyone.

For paid plans, the activity around your private graphs will be visible only to you and your collaborators.

We hope that you will spend some time browsing these graphs and creating new ones. Feel free to share anything you create or find.

In addition to the activity feed, we’ve released the following new features.

Secondary node labels

You can display a key node property along with the node name to make your graph more informative at first glance. For example, if your nodes have “year” property, you can display them along with the node name.

Custom edge names

You can distinguish the edges from one another by displaying a custom property of the edge. For example, your edge type could be labeled as “AFFILIATED” while it may also have “role” properties as “Board member”, “Advisor”, “Investor”. You can choose it as the rendered display name.

Node hover card

While browsing the nodes on a graph, you may want to get more information about a node at a glance. So, when you wait on a node for a few seconds, a hover card displays the image and description of the node.

Follow @graphcommons on Twitter, read our journal, join our Slack chat channel for discussions. We’d love to hear your feedback at contact@graphcommons.com.

Photo: 1963… lady, bubble, river — Melvin Sokolovsky by James Vaughan. Reproduced under a Creative Commons (CC) License.

One activity feed for everyone was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

Typically, a force-directed layout algorithm organizes a network map, makes patterns visually comprehensible, but it cannot identify and mark the clusters. Furthermore, in large network maps, the high level of detail overwhelms our senses. To be able to precisely examine its patterns, we need quantitative views of the data contained in the network. While there are a variety of data clustering methods in machine learning, the Louvain Modularity algorithm works well particularly for large data-networks. It detects tightly knit groups characterized by a relatively high density of ties. Beyond the visual realm, you can use a Louvain clustering algorithm to partition a many million-node online social network onto different machines.

Once the network clusters are detected, the identified groups of nodes can be given distinct color and names, so they are clearly differentiated and together provide a summary of the larger network. We can label a cluster based on the commonalities of its nodes or the most central nodes found in the grouping.

In Graph Commons, you can use clustering on your data-networks using the Analysis bar. You first click on the “Run Clustering” button, then set the resolution of how much granular clusters the algorithm should identify. Once the clusters are found, they are automatically labelled based on the most connected node in the cluster. However, we strongly recommend that you to rename these communities yourself to highlight what these communities specify in your context. Finally, you can view the list of all the nodes that belongs to a certain cluster and download it as a CSV file.

Cluster labels on the network map

In Graph Commons, you’ll notice the cluster labels are also placed on the map visually. You can move them around and change their size in order to make the network more readable.

When you mouse over a cluster label, it will be highlighted, this way you can clearly see its boundaries and where it is located the larger picture. Cluster labels on the map provide an overview for a complex network that is otherwise hard to grasp visually.

Bridges between clusters

Within the clusters of a complex network, we often see few nodes making connections to other clusters besides their neighbouring nodes, whose connections are only local, within their immediate cluster. Those nodes that are bridging connections among multiple clusters have high betweenness centrality. Such bridging nodes between two or more clusters become distinctly visible with the help of the network layout algorithms.

If we are analyzing a social network, these bridging people are well-positioned to be information brokers, since they have access to information flowing in other clusters. They are the ones who carry the gossip from one group of people to another. They are in a position to combine variety of knowledge and ideas found in multiple groups. On the other hand, bridging nodes have more likelihood of being a single point of failure. If a bridge person disappears, those formerly connected communities would disconnect.

Telling stories with network clusters

A network map is a powerful medium to visualize and analyze relations at scale. It doesn’t present a single story, but a world with multiple possible narratives. At first glance, a giant hair-ball of a network can be, for the non-expert, confusing, intimidating, or just illegible. Not everyone has the facility to read a network map and use algorithmic interfaces to filter and navigate. So we created the Stories feature on Graph Commons, to allow you to tell simple interactive narratives from complex data maps.

You can use cluster views in your stories to narrate interesting patterns in your data, through a visual interface that mimics the movements of a camera across your graph. Beginning with the familiar narrative flow of a story, you can reach more people, and introduce them to the tools that will allow them to engage with your data.

Sharing your cluster analysis

The goal of Graph Commons is to support quality data publishing, in addition to intuitive mapping and analysis of data networks, so we developed features to annotate and share your network maps. You can add visual annotations to any public graph, deep-link to your findings, and share them as rich social media posts. When you share, people come and provide feedback and generate new discussions around your work.

Sharing cluster-view of your network map often generates the most feedback, because your audience may not be familiar with the particular data points you have, but when they see the cluster labels, it makes sense and they start engaging with your work.

Clustering is a powerful method for analysis and story telling with your data. You can use Graph Commons to apply clustering in your data-networks and make sense of the complex issues that matters to you and to your communities.

We welcome feedback on your experience on using Graph Commons. Please send your thoughts to contact@graphcommons.com. Follow @graphcommons, read our journal, join our Slack chat channel.

Finding organic clusters in complex data-networks was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

Saturday Oct 21, 2017 4pm–7pm
Radical Networks
315 Ten Eyck St, Brooklyn, NY

Workshop tickets: Full price $25 Students $17.50
https://www.eventbrite.com/e/radical-networks-tickets-38618435781

We are organizing a network mapping workshop at the Radical Networks conference on Oct 21st. Radical Networks is a community conference & arts festival for creative, critical, and alternative applications of Internet technology.

This workshop focuses on the use of complex networks through mapping and analysis in order to expand your thinking about the network as a creative and critical medium. You will start from hands-on drawing exercises and gradually build complex compositions using the Graph Commons platform for collaborative mapping, analyzing, and publishing data-networks. The workshop asks how to extract models from complex systems, how to read those networks with methods such as centrality and cluster analysis, and also includes practice-based work sketching diagrams, editing graph data, and more. As a workshop participant, you will gain creative skills for modeling and analyzing networks, raise new questions and explore answers with your complex data, which would then inform your decisions.

Please bring your laptop!

This workshop is organized by Burak Arikan & Zeyno Ustun.

View the Workshop Archive for past participants’ work.

View the Radical Networks conference program.

About Graph Commons

Graph Commons is a collaborative platform for mapping, analyzing and publishing data-networks. It empowers people and organizations to transform their data into interactive maps and untangle complex relations that impact them and their communities. Graph Commons members have been using the platform for data research, investigative journalism, strategizing, organizational analysis, civic activism, archival exploration, and art curating.

For any questions or comments contact@graphcommons.com. Follow @graphcommons on Twitter, Graph Commons on Medium, join our chat channel on Slack.

Workshop: Creative and Critical Use of Complex Networks was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

We are excited to announce a new feature, Stories on Graph Commons.

A network map, or a graph, is a powerful medium to visualize and analyze relations at scale. It doesn’t present a single story, but multiple possible narratives. At first glance, a giant hair-ball of a graph can be, for the non-expert, confusing, intimidating, or just illegible. Not everyone has the facility to read a network map and use algorithmic interfaces to filter and navigate. So we created the Stories feature, to allow you to tell simple narratives from complex data maps.

As of today, you can start creating interactive slideshows to tell dynamic stories from your graphs. You can narrate interesting patterns, actors, paths and clusters in your data, through a visual interface that mimics the movements of a camera across your graph. Beginning with the familiar narrative flow of a story, you can reach more people, and introduce them to the tools that will allow them to engage with your graphs. It looks great on mobile browsers too!

Explore the stories below and try the interactive tutorial to begin making your own stories.

NGO responses to the climate change policies in Europe

Who is opposing or supporting the EU climate change policies.
8-slide story by SIMPOL Project, Financial Systems Simulation & Policy Modelling at University of Zurich.

ATÖLYE’s multidisciplinary community curation

Sector-based curation that enhances creative production amongst our community. 9-slide story by ATÖLYE, Transdisciplinary Innovation Platform in Istanbul.

The power broker of the Erdogan family

Erdogan’s brother-in-law ties the family to the Azeri SOCAR conglomerate. 11-slide story by L’Espresso Magazine, Italy’s prominent weekly and Italian partner of ICIJ.

Major ISIS attacks in Turkey

This story tracks the major attacks committed by ISIS from among the 88 attacks by 9 different terror organizations since 2011. 5-slide story by 140journos, new media journalism from Turkey.

We welcome feedback on your experience. Please send your thoughts to contact@graphcommons.com. Follow @graphcommons, read our journal, join our Slack chat channel for discussions.

Introducing Stories for Graphs was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.

Graph Commons is a collaborative platform for mapping, analyzing and publishing data networks. It empowers people and organizations to transform their data into interactive maps and untangle complex relations that impact them and their communities. Whether you use data mapping for investigative journalism, archival exploration, or content curating you’d want to have your audience engage with your published data. The goal of Graph Commons is to support quality data publishing, in addition to intuitive mapping and analysis of data networks, so we hope that this new sharing feature flourishes meaningful discussions around your work.

As of today, you can add visual annotations to any public graph, deep-link to your findings from rich social media posts, and make high resolution prints.

Comment with a view

Select an area on the graph and add your thoughts. Give feedback, share links, and generate new discussions.

Tip: In a comment, use # to reference nodes and @ to mention members.

Graph comments, listed on the right bar, are commonly used for giving feedback, sharing links, and flourishing new discussions. Comments with captured images are useful for annotating the graph. Again, you can mention (@ sign) other users and provide linked references (with # sign) to nodes on the graph.

High resolution prints

Download your selection or the entire graph as a PDF file to make beautiful posters. It enables you to print high resolution images, so a giant graph that does not fit to your LCD screen can be printed on a large canvas.

Tip: Open the PDF file in a design tool to modify for your needs.

Deep-linking to nodes

Select a node and share its link. Your audience will directly land on the shared node highlighted on the graph.

Tip: Shift+drag to select a group of nodes.

Deep-linking allows you directly reference any node in a graph using its permalink, which is displayed on your browser address bar when you click on a node.

Rich social media posts

Instantly share a compelling argument supported by graph data, automatically displayed as a rich social media card on Twitter and Facebook.

Tip: Embed an interactive graph on Medium by pasting its URL into your post.

We hope the new sharing experience generates meaningful discussions around your data maps. We welcome feedback on your experience, it will help us refine the platform further for everybody. Please send all suggestions, questions and comments to contact@graphcommons.com we’d love to hear from you.

Follow @graphcommons on Twitter for a daily dose of network arts and sciences, read our publication on Medium, join our Slack chat channel for discussions.

Sharing graphs has never been easier was originally published in Graph Commons on Medium, where people are continuing the conversation by highlighting and responding to this story.