This post continues the theme begun in the last post about the nature of networked intelligence, and the role of connection strengths and context. This one reveals more of the theory that has inspired the “Trusted Sharing” app now being tested by a few friends and colleagues.
Networks are nature’s best embodiments of collective intelligence.
Networks are intelligent and adaptive, which means that they grow in intelligence as they adapt to events to fulfill needs.
The networks that are most important to us are:
- Networks of living organisms, plants and animals that feed and protect us;
- Networks of cells that make up our immune systems;
- Networks of neurons that make up our brains;
- Personal and social networks that make up our friendship and support networks, and health, transportation, economic, learning, and communication networks.
Our technology is now more than ever creating, using, and maintaining networks.
All networks are basically made of nodes and connections. The intelligence of the nodes is important, but the real intelligence of networks is in the connections.
There are two super-attributes of connections that are especially important in all types of networks: Connection Strength and Context.
In order for a social network application to be most intelligent and useful it has to recognize, ‘understand’ and make use of these two attributes of connections – strength and context.
Current generations of social networks, like Facebook, LinkedIn, Twitter, and now Google+, are just now, and barely, beginning to recognize and make use of the strength and context of connections. “Just beginning” means that there is much more valuable evolution that has yet to take place.
All people of course pay close attention to the subtle nuances of their network connections. But it is definitely a challenge to design a computer-augmented social network application that can more fully ‘understand’ and make use of these nuances, which include the strength and context of connections.
To be successful, social network applications have to miraculously turn complexities and fine distinctions into features that are easy and intuitive to use, useful, and, most of all, that don’t get in the way.
Strength and Context of Connections
Here are some of the distinctions that exist in our ‘real-world’ understanding of social network connections and behaviors.
Context of connection is about what is shared.
Networks form within and around what people share:
- Friendship, love, family ties (and hostility, aversions, common enemies)
- Group and organization membership – based on commonalities such as shared interest, perspectives, goals, employer, profession or industry.
- Exchange-driven contexts – e.g., buyer-seller, client-consultant, teacher-student.
More abstractly what we share are:
– Exchanges and collaborations
Strength: How strong is the connection?
People easily understand that their connections vary in strength. We intuitively measure strength in terms of how much the connection is:
– High in priority (for response or action)
There is another important attribute of connections that is important to understand and make use of: Reciprocity – also known as mutuality.
In social network theory reciprocity is understood in terms of the “direction” of the connections between two nodes: A connection can extend from A to B, or from B to A, or it can be bi-directional, i.e., reciprocal.
If two people share the same interest (or goal or set of values or group membership) but don’t know each other, then they are each connected by the same context, e.g. interest (or goal or set of values or group membership), but they are not personally connected.
Yet even if not connected personally, they have a mutual connection to the same context; and that mutual connection can be an important incentive for reciprocity: sharing information and ideas, responding to news & requests, providing help when needed, etc.
It is also possible that even if two people share the same context and don’t know each other personally, one person can “follow” the content that the other person makes available, in a tweet, blog post, article, book, video, etc.
This type of non-reciprocal relationship is Twitter’s sweet spot. In Twitter if you follow someone’s posts they don’t have to follow you for the system to work. And yet, following someone is often a way that a reciprocal relationship can start – by following someone back, or even by actually reading and commenting on what the other person posts, or by starting a direct conversation.
Trusted Sharing’s Approach
Trusted Sharing’s first public release will focus on developing a core layer for automatically determining and using both strength and context of connections to sort and filter incoming feeds and to target outgoing messages and feeds. We already have much of that core functionality completed for use of strength of connections — enough to begin testing and refining with a limited number of test users. The public release, when complete, will also include simple but useful methods for filtering and tagging content by topic (context). However, automatically tagging and matching user-generated content by topic isn’t our focus for this first version. Our preference is to partner with other developers who are already well down the road with automated topic recognition in socially-shared content.
If you would like to be notified when the Trusted Sharing beta is ready, leave a comment here, or contact me at email@example.com.