The central source of power in the digital world today is network effects stemming from the control of data. A network effect is defined as something whose value to all participants increases as more people participate in a particular platform or network. There are many examples, from the telephone system, to social media, to marketplaces, where many independent parts (e.g., devices, people, organizations) interact with each other and constitute a large complex system.
When the network effect results from the links within data, it is called "data-network effects". It occurs when a service becomes smarter as it gets more data from its users. Digital platforms record their users' activities, link to one another to build giant data-networks, and compute them with machine learning algorithms. The more data users contribute, the smarter the service becomes as the users' data are computed to make predictions, recommendations, performance improvements, perfecting interfaces etc. Examples range from Google's search result optimizations, to Amazon's product recommendations to Facebook's friend suggestions, to Uber's pooling of taxi riders. Over time, users become increasingly addicted to these services because of the personalizations and improvements that have been made based on their own data. These new means of production through capturing, predicting, monetizing people's behavioral surplus generates exponential growth and monopoly power for these platforms.
The power of monopolies leads to problems ranging from the threat of censorship to algorithmic biases in the curation of content to manipulation of people’s behavior. A recent report from MIT Center for Civic Media says these platforms that host and inform our networked public sphere are unelected, unaccountable, and often impossible to audit or oversee. Needless to say, none of those digital platforms are public, but private digital spaces that are designed to feel like public ones. Furthermore, the report examines alternative platforms which seek to confront these power imbalances. These include open source and federated social media applications such as Diaspora and Mastodon as well as peer-to-peer distributed systems based on blockchain technologies. Hence, the report concludes that there is no straightforward technical solution to the problem of platform monopolies.
The reality is that most people do not want to run their own web servers or social network nodes. They want to engage with the web through friendlier platforms, and these platforms will be constrained by the same forces that drive consolidation today.
Another fundamental issue with platform monopolies is data ownership, when we take the labor point of view. Data ownership is usually discussed in the framework of data interoperability, that the users are locked in these platforms because they cannot take their social network or data traces with them, if they want to migrate to another platform. Although demand for data portability points to an important problem, the value of a user’s data in such platforms often remains opaque to them. The spectacle users create in those platforms (through creating social content and meta-content) is not a byproduct of use, but the product itself, as mentioned in Tiziana Terranova's seminal essay "Free Labor: Producing Culture for the Digital Economy". Moreover, as these platforms expand their reach to everyday life and become part of the surveillance apparatus, this situation can have serious consequences for people’s personal and professional lives. Shoshana Zuboff explains such exploitation of people's behavioral surplus as a parasitic form of profit and calls it "surveillance capitalism".
This is how in our own lifetimes we observe capitalism shifting under our gaze: once profits from products and services, then profits from speculation, and now profits from surveillance.
With the new version of the Internet Protocol (IP), any device in the world can be assigned a unique address for identification and location definition. This technical preparation for the so called Internet of Things  makes increasingly critical the question of who owns and controls data infrastructures. Do you own a self-driving car’s sensor data captured from your neighbourhood? Are you in control of a nanoengineered drug’s data captured from your body? Are you paid rent for the use of sensor data captured from your house? As our behavior is systematically forecasted, we have gradually entered a “society of control” that monitors, simulates and pre-mediates individual identities in relation to their data trails. Data oligarchies holding such power will only continue to grow and the dispossession of our data will increasingly constitute what I call data asymmetries, until we move from connectivity to collectivity, build new purposeful exploitation-free autonomous zones, and reroute our life activities in solidarity with each other.
Graph Commons (graphcommons.com) 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 investigative journalism, creative research, strategizing, organizational analysis, activism, archival exploration, and art curating.
Exploring data projects across a variety of topics
Using Graph Commons, activists in Brazil have mapped public-private partnerships causing ecological damage in the Amazon rainforest. Journalists in Turkey have mapped the network of NGOs aiding Syrian refugees. An art foundation in New York maintains an open graph about their grantee network. A Zurich-based NGO monitors lobbying influences in the Swiss parliament. These are some of the examples of the many data projects, created in a variety of languages, and on a variety of topics by people and organizations around the world using the Graph Commons platform.
Graph Commons is an open platform where you can discover content in variety of ways. You can view featured graphs on the homepage; search people, organizations, and concepts that interest you; view data (node) profiles and explore relations and graphs. Members have profile pages where you can view their published graphs, their work in progress, and what they recommend on the platform.
Organizations with extensive data needs such as art institutions, museums, think tanks, civil society organizations, media journalism groups, or specialized projects use a Hub on Graph Commons. A hub is an organization’s data portal, where you can search and explore their curated graph database.
Mapping your data: Visual Editor, Import, Customization, Collaboration, Analysis
On Graph Commons, you can collectively compile data about the topics you are interested in, define and categorize relations, transform your data into interactive network maps, discover new patterns, and share your insight about complex issues using a simple interface. The platform serves both producers and consumers of graphs by linking entities together in useful ways and thereby creating a whole that is greater than the sum of its parts.
Using the visual graph editor you can easily brainstorm ideas, work in collaboration with your colleagues. Besides the visual graph editor, you can also import your existing data sheets. Once you import your data, you apply network analysis to discover patterns, indirect relations, and organic clusters that are otherwise hidden. As you work with your data, you can create unified data taxonomies, customize color and icons of actors and relations in your data, and develop an effective visual language.
Publishing data: Prints, Embeds, Stories, Comments
Once you’ve prepared your graphs, you publish them on the Internet with a unique permanent link, or permalink. You can share a particular selection from your larger graph, which will also have sharable permalinks with ready-made social media cards. Furthermore, you can embed the interactive graph into your website or online article. Public graphs are licensed to their authors with Creative Commons International 4.0, which applies the license to the author’s graph data structure and its contents (if copyrightable). All graphs have a comment side bar where visitors leave comments, provide feedback, and discuss your work.
Using these platform features, Graph Commons members collectively experiment in the act of network mapping as an ongoing practice: Search across variety of graphs, explore data-networks at scale, invite collaborators to their work and ask others to contribute to theirs. We believe everybody will find a unique way to use Graph Commons in their own connected world.