Social gravity, a new definition for the social web.Comments

Current suggestions for measuring the impact we can have on the social web are considered inadequate and there is an ongoing backlash against them. How can we, therefore, better define our ability to influence others?


As we have already seen, the science behind influence measurement services is actually pretty sound but even the best algorithms are only as good as the data they are run against.

Influence measurement is not going away and is rapidly becoming a vital mechanism in understanding impact on the social web but, building on the concepts in network science we should be looking at other ways to interpret our "influence".

Social gravity can help.

Social Gravity is an existing term with a number of different interpretations but none of which seem to fit the context of social influence; existing definitions include:

  • the forces which hold a person in the same societal sphere and may prevent them from being socially mobile (able to better their position) irrespective of merit
  • the ability of a business to keep customers "in orbit" around its brand to facilitate easy purchase (orbit strategies)

I would like to propose a new definition:

Social gravity is the ability of individuals and groups on the social web to both draw others to them and keep them close - it is a social "glue".

But it goes much deeper than this and we need to examine other areas for comparisons to better understand how it will operate within social networks.

Gravity models

The first law of geography according to Waldo Tobler is "Everything is related to everything else, but near things are more related than distant things."

"All animals are equal" anyone?

Newton's law of gravity states that bodies will attract each other with a force proportional to their masses (and inversely proportional to distance). It causes dispersed matter to coalesce, and coalesced matter to remain intact (a direct equivalent to the ability of social gravity proposed above)

A social parallel to Newton's law would be the size of a network cluster determining the relative pull that it may have on non-members but this is not sufficient to explain how groups form and grow on the social web.

Gravity models are used in social sciences to describe behaviour and generally contain elements of mass and distance, which lends them perfectly to the metaphor of physical gravity - a couple of examples are as follows:

  • trip distribution - more distant points will have less traffic between them than closer ones, common sense which can equally be applied to nodes within a social graph. Distance is generally inversely proportional to influence.

The term "friction of distance" goes on to explain that as distance increases so the effort required to overcome that distance also increases. In a social context, as the distance along a path between users increases so the amount of re-shares required for an item to be seen goes up thus reducing the likelihood that the person at the end of path will be exposed to that item.

  • migration model - used to estimate traffic flow and migration between areas as well as the sphere of influence of each. In a social context this would equate to the traffic between clusters within a network but sheer size and distance of those clusters is not sufficient for determining their pull.

At the risk of sounding crude, size is important but it's what you do with it that counts - a cluster that is.

Further considerations

Along with size and distance social gravity, regardless of if we are talking about an individual or a group, is linked to the following :

  • reputation,
  • relevance, and
  • value

As previously noted, there are times when members of social networks might establish connections beyond the influence of social gravity: they might seek out popular individuals or influencers to kick-start their experience rather than be drawn organically; others will gravitate towards influencers because it is fashionable - free-floating users looking for the next big thing to attach themselves to whilst it is in vogue before drifting away to something else.

At all other times it is social gravity which draws us together but, just as in science, there are additional forces that must be overcome.

Inertia and entropy

InertiaInertia is the resistance of an object to changes in its state of motion or rest, or the tendency for an object to resist such changes.

People, just like objects, will have a sense of inertia - we are creatures of habit and without external influence will often becoming stuck in a rut and resistant to social change. We may need to give individuals a nudge to get them moving. The goal is to overcome the inertia and extend social gravity wider to encourage engagement from more remote individuals

Social entropy is the measure of decay within a social system such as the breakdown of its structure.

Without interaction a group will naturally drift apart so much of the communication between members of a cluster will just serve to hold that cluster together even before any useful transfer of content is achieved. Engagement is essential for maintaining social bonds, members should also ideally have a sense of ownership (having their voices heard) to encourage them to stay within the group.

Social entropy increases as satisfaction with the status quo decreases for various reasons:

  • relationships with other members of the cluster
  • decline in engagement
  • loss of value possibly due to change of topic or focus
  • changes in the social platform which can lead to dissatisfaction with the user experience
  • external factors which impact behaviour and decrease engagement

We are, therefore, in a constant battle to maintain our existing connections. This may involve increased sharing, altering of social habits or even attempting to introduce new relationships to ignite new discussion.

What attracts us to Clusters?

Regardless of the structure of a group or cluster relevance and value really come in to their own. Just as an offline model will look at the shops and facilities available within an area so a model of social gravity will examine criteria such as group members, the shared content available and the depth of discussion.


A social group must be accessible in order to maintain a sense of gravity, and this doesn't just mean public. If a group spends its time sharing private jokes between members then, while the bonds may be strong within it, the group will not be an attractive proposition to outsiders.

Users are not going to associate with clusters that have no relevance. Additionally, a small cluster might have higher relative value when compared to a large cluster (even if relevance is equal) due to the content offered by that cluster.

The notion that larger clusters should afford more opportunities due to a larger population would hold true all things being equal but this does not account for the actions of its members. Indeed, size might even be counter-productive due to noise and network congestion.

Individual gravity

Just as with groups or clusters, individuals will be relying on the same principles to increase their own social gravity. We are constantly reminded that we must be open, honest, authentic and share quality content. Individuals will increase their relative gravity by their associations with others; to maintain relevance they must align themselves with like-minded individuals (as it is easier to preach to the converted) and share material in keeping with the interests of the target group to ensure a sense of value.

Engagement is key as - unless the individual is already a recognised leader in their field - others will not respond well to a one-way, broadcast relationship. Interaction is vital to cement those relationships with others which cause them to become advocates as without it we are unable to establish a positive reputation.

The bottom line

Social gravity, just as with influence itself, is not just about the level of activity maintained on social networks but the respect and trust that can be instilled in others by establishing reputation, relevance and value. The amount of social activity (between individuals or within a cluster) is secondary to its quality.

Social gravity is a core property of identity, the ability to extend the sphere of influence and is formed by the actions and affiliations of an individual or group. A group's gravity is also affected by its cohesion so it is imperative that stable relationships within the group are maintained to encourage new membership.

The mechanics of social gravity are complex and further illustrate the need to look beyond influence scoring.

Images by Rubin 110, bluekdesign and daynoir

Social gravity, a new definition for the social web.

Network science, influence and the social web.Comments

"At any moment in time, we are driven by and are an integral part of many interconnected, dynamically changing networks" - Katy Börner, Soma Sanyal and Alessandro Vespignani

NetworkMy original 3 R's of Influence proposal was compared to the principles of network science due to the nature of data and relationships required in order to ascertain a full picture of our influence over others. Network science was not something I was overly familiar with at the time so I have decided to take another look at how social networks and influence fit together based on these principles.

As a reminder, I proposed that social influence should be comprised of: reach, reputation and relevance.

We must also guard against the difference between perceived and actual influence where perceived influence is little more than garnering attention with single click, throwaway behaviour such as Likes or +1s.

Actual influence is encouraging someone to do something meaningful: a comment that adds value or writing their own post, changing their opinion or maybe even influencing a wider decision such as a purchase.

So, what is network science?

One definition of network science is "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena"; it is essentially the study of any kind of network, its formation, the behaviour of its members and the analysis thereof.

If you have spent any amount of time discussing the operation of social networks then you will already know more about the field than you might expect from the terminology involved to some of the core, common sense concepts:

  • A network (or graph) is composed of nodes (members) connected by edges (the links between them) - edges can be bilateral or unilateral and determines the type of network we are dealing with.
  • Bilateral links will create the formation of undirected graphs (or just graphs) which, from a social media perspective, we will recognise as having a "friending model" - nodes will enter a reciprocal relationship.
  • Unilateral links will promote the creation of directed graphs (or digraphs) which we will recognise as the "following model" - just because Person A follows Person B it does not imply that the connection goes both ways.

Directed and undirected graphs are often examined in isolation but, in reality, our networks comprise a mixture of bilateral and unilateral links and exist within the context of wider connections. We are frequently active on more than one social network so really need to investigate "multigraphs" as our connections often exist in more than one network and we have multiple edges between the same individuals depending on the means of connection.

We might have a bilateral edge between users on Facebook (friends) but only a unilateral edge on another service such as Twitter or Google+ (a one way following).

It is the combined impact (and not necessarily the sum) of these edges that, ultimately, determines the influence exerted by one node on another - we may not enjoy the same relationship on different networks.


Social influence measurement systems such as Klout and Kred attempt to operate in a multigraph environment but, as has been stated previously, often only have access to limited public data which cannot accurately determine the level of influence.

A data host, such as Facebook or Google, has direct access to both public and private data and also access to an extended set of "actions" so is far better placed to determine influence between nodes within the confines of its own graph but this acts in isolation and any potential ranking is independent of influence within any other graph unless the data host has sufficient access to other data sources - Google may have access to extended data via its search engine for example but certain private data will still be missing.

Random NetworkFriends and weighted graphs

As well as the number of edges (roughly translating to the number of friends) we may have, strength and frequency of interactions between nodes have an impact on the graph - and therefore the degree of influence exerted by those nodes.

Network science defines a node with a direct connection as a Neighbour - within the social web we will refer to this as a friend or follower based on the nature of the connection.

Connections between people are known as paths and if we are able to go from one node to another along a path - even if it means going via intermediate nodes - then this node is within our reach. Still all familiar terms.

Reach is perhaps an impersonal term so we tend to feel more comfortable with the likes of Facebook's "friend of a friend" but it still means exactly the same thing: someone connected via a path and, therefore, within our reach and potential influence.

The length of the path (how far removed a friend actually is) will usually determine the extend of influence but the influence afforded by specific events may override what is expected. Person A may not normally influence Person B very highly due to the social distance between them but a specific share may prompt action beyond the normal response for that path. This may be an isolated event, in which case it will have little impact on overall influence, but it may also cause Person B to create an edge (friend or follow) with Person A thus directly increasing the base level of influence between them.

As I have previously mentioned with regards to implicit and explicit graphs, the frequency of interaction can increase the degree of influence, especially for secondary/tertiary connections etc. The more frequently someone is exposed to a friend of a friend the more likely they are to be both influenced by them and to create a closer connection - just like the move from a repeated implicit graph to an explicit one.

The actions of our friends and followers (neighbours) can be seen to be very important in the determination of strength of connections and also the way our clusters are formed.

GravitySocial gravity

The concept of "graph density" is quite obvious: if the actual number of edges between nodes is close to the maximum possible number of edges then it is a dense graph. If a group of nodes within a larger graph is particularly dense it can be refered to as a cluster. A typical example is a group of friends on Facebook who all know each other.

The denser the graph the greater its influence due to increased communication between the nodes. A denser graph can be thought of as having its own "social gravity" - the more connections and interactions we experience within a group the more included we feel and, therefore, more likely to remain a member. A sparse graph is likely to have less regular interaction between members on its periphery and they are more likely to disconnect in search of a more fulfilling experience.

We can argue that influence is not only the ability to cause others to perform actions but to also maintain graph/network integrity even without necessarily prompting action; this can be the cumulative effect of the actions of members within the group rather than specific individuals.

Influence is not just for people.

Degrees and herd mentality

The number of connections (edges) that a node may have is known as its degree. The higher a nodes' degree, combined with the degree of those nodes to which it is attached, the more likely that it is "central" and, therefore, an influencer - all well and good we would assume.

The "Barabási–Albert (BA) model" for network generation states that as a network grows so those members with a high degree are more likely to receive new connections and we see this in action all the time within our social networks.

As individuals, our own social gravity will determine our ability to attract new followers but an element of herd mentality can also be at play. For every follower that connects due to a genuine interest there will be more who connect as it is the fashionable thing to do as that person is deemed an influencer.

New users of a social network will frequently seek out the popular, the influencers in an attempt to increase engagement during their first weeks or months on the service.

Social gravity can, therefore increase artificially rather than organically; the graph will experience an element of distortion especially when the network publishes a suggested user list based on the perceived popularity of its members.

It is not just the networks themselves that can cause potential issues when recommending other users. The practice of circle sharing on Google+, or list sharing on Facebook, are examples where fashion may override interest and, as I wrote in my post Do shared circles aid growth or hinder engagement? blindly adding shared circles or lists and not interacting with their members can skew the graph and also have a negative impact on influence.

The ratio of interactions to edges is important; as we have seen above, whilst having a high degree may seem like a node is influential it is the weighting of its edges that is the true mark of influence. A high number of edges all with very low weighting demonstrate that the central node does not actually exert that much influence as the strength of the connections (and consequently the ability to drive actions) is weak.

ConnectionsDegree correlation

Although it may be more prevalent for relatively unconnected users to seek out popular members we can also experience instances where nodes with a high degree (influencers) may prefer connecting with other influencers. This process is known as degree correlation and a frequent example is subject experts preferring to collaborate with other experts rather than the lay person.

The social web, however, is a great leveler allowing so degree correlation can be viewed as nothing more than social snobbery with influencers merely "broadcasting" to their followers but not interacting. This might work for some as their followers might just be willing to consume; for others, however, it can lead to a sense of resentment with the influencer seen as elitist.

Our social networks

When dealing with social networks we are almost always talking about clusters when we refer to our social graphs - small subsets of the network population. Some may only interact within a single cluster (a specific, contained group of friends) and others will have connections to many based on varied interests introducing a complexity that cannot be easily modeled.

Facebook is seemingly singular amongst social networks in that its functionality is obviously highly derived from network theory. Edgerank is a direction interpretation of the effects of edge weighting to determine influence between nodes. What annoys some users, however, is the impression that Edgerank assumes that users are ONLY interested in what influences them most and that, over time, this ranking becomes artificial as they have filtered items forced upon them and smart lists auto-populated with those deemed close friends or just acquaintances.

Twitter is all or nothing; we see everything from someone or nothing (unless it is shared by a mutual connection). At its core Google+ is all or nothing - we add someone to a circle or we don't - we do, however, have the ability to manually weight those circles thus increasing or reducing their visibility within our primary stream. By tuning our circles we are specifying who we are (or want to be) influenced by. We are manually adopting the principles of network science without knowing it by altering the edge weighting ourselves rather than having it automated.

Both Facebook and Google+ provide the means to circumvent edge weighting by showing "Most Recent Stories" or viewing individual circles respectively but this relies on the user; many will not toggle the view of their Facebook feed or view individual circles.

What of the 3 R's?

Network science adequately covers the concept of reach as we seen above and begins to touch on the idea of reputation when we start addressing social gravity and the weight of our connections.

While a number of models exist for the formation and growth of networks the one thing they cannot accurately represent is relevance. The human factor involved in our interactions is unique and, as has been written before, what is relevant will change based on context including:

  • trust
  • interest
  • location
  • platform

The level of trust placed in an individual might be indicated by the number, strength and duration of connections but all too often we will let our social circles stagnate and not remove those with whom we no longer interact.

Our interests will vary according to our circumstances so the workings of any social group will be hard to predict once we factor in variables such as location and platform - be it mobile or desktop, different operating system or applications or different social networks.

Influence scoring

All this aside, we recognise base elements of the organisation and behaviour of our the social web in the principles of network science and this can serve as some degree of comfort to our time spent online - a validation that there is, perhaps, more to it than just meaningless status updates. We can also see these principles at work in the methods employed by the influence measurement systems as they seek to evaluate the impact of our connections across the range of social graphs.

Opinion on the efficacy of influence measurement services is widely contradictory. The science behind the likes of Klout and Kred, however, is actually quite sound but calculations based on a flawed data set are always going to be problematic.

Social influence measurement in its current form must not be used in isolation but can be treated as a base on which to add the more human elements of online interaction.

Images by futureshape, OpenBioMedicalRubin 110 and bupowski

Network science, influence and the social web.