SourceCred is a system for assigning 'cred' to contributors, in proportion to the value they've contributed to the project.
SourceCred does this by creating a 'Contribution Graph', a graph containing all of the contributions and contributors to a project, as well as how they are inter-related. It then assigns cred to every node in the graph, with PageRank-esque semantics such that cred flows along edges, accumulating at nodes that are connected to other high-cred nodes. Intuitively, contributions are important if they are depended on by other important contributions, and users are important if they are connected to important contributions.
The intention of this document is to rigorously describe the SourceCred algorithm, so that we can use it as a basis for discussing improvements and open issues with the system. This is a living document, and we intend to keep it up-to-date so as to describe the present operation of SourceCred's algorithm.
Pull requests welcome!
The core data structure in SourceCred is the contribution graph. Strictly speaking, it's actually a quiver as the edges are directed, multiple edges are allowed between a given pair of nodes, and loop edges are permitted.
Every contribution or contributor to a project is represented by a node in a graph. For example, GitHub pull requests, user accounts, comments, and repositories are all represented as nodes. Every node is assigned cred based on how it's connected to other nodes.
It can be counter-intuitive that users get the same "kind" of cred as issues and pull requests, but handling things this way makes the algorithm nicely simple and consistent.
Every node is given a unique address, which is represented by an array of strings. The graph does not store any metadata with nodes. Because addresses are unique, the addresses can be used as an index into other databases.
Every relationship between contributions or contributors is represented by an edge in the graph. For example, a GitHub pull request may have some of the following edges:
- an authored by edge, connecting it with a user that authored the pull
- a has child edge, connecting it with its comments or reviews
- a has parent edge, connecting it with the repository that contains it
- a references edge, connecting it with an issue or pull it textually references
By convention, we name edges with verb phrases, so that the
src is the
subject and the
dst is the object of the verb phrase. Thus, an edge
connecting a GitHub user with a post they wrote would be an authors edge.
In practice, we often want to flow cred along both directions of an edge. For example, if a user authors a post, that user should earn some cred from the post (cred flows along the authored by edge). However, we may also want cred to flow from the user to the post, with the intuition that posts authored by important contributors are likely more important than posts authored by someone who has no history in the project. Then, cred should flow along the authors edge.
These situations come up pretty often, so by default, we treat every edge as
potentially bidirectional. When we assign weights to edges, we give every edge
toWeight configures how much cred flows
src to the
dst, and the
froWeight configures how much cred flows
dst to the
src. This way, we don't need to add backwards edges to
the graph to enable these flows. If an edge really should be uni-directional,
we can just set one of the weights to 0.
A core goal for SourceCred is configurability and extensibility. That extensibility starts with the graph. The SourceCred core doesn't create any graphs itself; it just defines the semantics for graphs. Graphs are actually created by plugins. SourceCred currently includes two initial plugins: one for Git, another for GitHub. In the future, we imagine having plugins for other tools, like Twitter, StackOverflow, Google Docs, npm, and so forth.
As mentioned above, we require that node and edge addresses be unique within a graph. That means that they need to be unique across plugins. To ensure this property, plugins are expected to define their own namespaces within address space, by making all of their node and edge addresses start with a unique two-part plugin prefix. The first element of the prefix should be the name of the owner of the plugin, and the second part should be the name of the plugin itself.
For example, every node and edge address created by SourceCred's GitHub plugin
starts with the prefix
Node and Edge Types
Nodes and edges organized into meaningful "types" or "kinds" which share common semantics. For example, some kinds of nodes include issues, pull requests, and user accounts; kinds of edges include authors, references, and merges.
To support this, SourceCred has a concept of types. Type membership is determined by a shared address prefix.
For example, every node whose address starts with
["sourcecred", "git", "commit"] is considered a member of the commit type defined by SourceCred's
A node or edge may be a member of multiple types (and every node and edge is considered a member of the empty type).
SourceCred allows the assignment of weights to nodes and edges. A weight is a
non-negative floating point number, i.e. in the range
[0, Infinity). Nodes
have a single weight, and edges have two weights, a
At present, weights may only be set at the type level, but our intention is to
allow arbitrarily customized weights that are modified by user provided
Attribution is the process by which we assign a score ('cred') to every node in a contribution graph. It uses a PageRank-based algorithm, although in contrast to canonical PageRank, there is no random teleportation vector. (Due to the lack of a teleportation vector, it's possible that the chain is not ergodic, and that the algorithm will fail to converge. There's no particular reason we don't have a teleportation vector, and may choose to add one in the future.)
Creating the Markov Chain
We convert the contribution graph into a Markov Chain. For every node in the graph, we create a self-loop connection with a small 'synthetic loop weight' (usually 1E-3). This ensures that every node has at least some outbound connections (in lieu of a teleportation vector). Without such a loop, the Graph might not correspond to a valid Markov Chain.
We then add connections corresponding to edges in the graph. Specifically, for
every edge, we add a connection from its
dst with a weight
edge.toWeight * dst.weight, and we create a connection from its
src with weight
edge.froWeight * src.weight.
Finally, we normalize the weights so that the outbound connections for each node sum to 1 (i.e. it's a valid Markov Chain).
Assigning Raw Cred
Once the Markov chain is created, we are ready to assign cred. We start with a
uniform distribution over over every node in the graph. Then, we successively
update that distribution with Markov chain actions, until we find a stationary
distribution. We measure how well converged a distribution is by looking at the
infinity-norm difference between that distribution and the distribution with
one more Markov action applied. Concretely, if
A is the transition matrix
corresponding to the chain, then:
convergenceDelta(x: Distribution) = infinityNorm(x - A * x)
We keep iterating until the convergence delta is sufficiently small, or until a max number of iterations is reached.
Normalizing Cred Scores
The raw cred scores generalized by this algorithm form a probability distribution. This means that most users will have tiny scores like 0.00363, which is aesthetically unappealing for human consumption, and hard to compare across projects. To make the scores easier for humans to remember and interpret, we re-normalize the scores so that all GitHub user accounts, in aggregate, share 1000 cred. The normalization algorithm is linear, i.e. every score is multiplied by the same constant.