Designing A/B tests in a collaboration network



On this article, we talk about an strategy to the design of experiments in a community. Specifically, we describe a technique to forestall potential contamination (or inconsistent therapy publicity) of samples as a consequence of community results. We current information from Google Cloud Platform (GCP) for example of how we use A/B testing when customers are related. Our methodology will be prolonged to different areas the place the community is noticed and when avoiding contamination is of main concern in experiment design. We first describe the distinctive challenges in designing experiments on builders engaged on GCP. We then use simulation to indicate how correct choice of the randomization unit can keep away from estimation bias. This simulation relies on the precise person community of GCP.

Experimentation on networks

A/B testing is a regular methodology of measuring the impact of adjustments by randomizing samples into completely different therapy teams. Randomization is important to A/B testing as a result of it removes choice bias in addition to the potential for confounding elements in assessing therapy results.

At Google, A/B testing performs a key function in higher understanding our customers and merchandise. With A/B testing, we will validate numerous hypotheses and measure the impression of our product adjustments, permitting us to make higher choices. After all, A/B testing will not be one thing new in our subject, because it has been adopted by many tech corporations. However because of the giant scale and complexity of knowledge, every firm tends to develop its personal A/B take a look at resolution to unravel its distinctive challenges. One specific space includes experiments in marketplaces or social networks the place customers (or randomized samples) are related and therapy project of 1 person might affect one other person’s conduct.

Typical A/B experiments assume that the response or conduct of a person pattern relies upon solely by itself project to a therapy group. This is named the Steady Unit Remedy Worth Assumption (SUTVA). Nevertheless, this assumption not holds when samples work together with one another, comparable to in a community. An instance of that is when the consequences of uncovered customers can spill over to their friends. That is the case for experiments on the Google Cloud Platform.

Google Cloud Platform (GCP) provides a collection of merchandise that allow builders to work on their tasks within the cloud. GCP additionally offers nice flexibility to builders in sharing their assets and tasks, with instruments to guard and management their safety and privateness. We discover that customers in GCP naturally type collaboration networks to work on shared tasks and this in flip improves effectivity in managing their assets. Our aim is to leverage this community construction in designing and analyzing experiments to enhance the GCP product.

One salient requirement we impose on experiment design is that the expertise of all customers who collaborate with one another be constant. That is essential to our extremely collaborative product. For instance, think about a scenario the place two customers collaborate on a shared venture. One person sees a brand new function to allow a firewall, however the different person doesn’t see the identical choice accessible. This might create confusion. Such an undesired impact will not be solely unhealthy for person expertise, it additionally hinders us measuring the true common therapy impact of the brand new firewall function. Thus, the requirement is to offer a constant person expertise for the next two forms of therapy publicity:

  • Direct publicity: Each person should have a constant expertise throughout all GCP tasks that she or he owns, manages, or collaborates in. 
  • Oblique publicity: Any two customers who collaborate on a venture should have the identical expertise.

The graph of person collaboration will be separated into distinct related parts (hereafter known as “parts”). In an effort to fulfill these consistency necessities, we select to make use of the element as our randomization unit in experiment design.

Whereas the collaboration networks in Google Cloud Platform bear similarities to parts of social networks (e.g. Fb, Twitter, LinkedIn, Google+), there are important variations. Two elementary variations are described beneath:

  • A couple of giant related networks versus many related parts: Customers in social networks are linked to one another via their frequent pals. Methodologies for experimenting on customers in social networks give attention to methods to partition the general graph into subgraphs, after which to run randomized experiments on these subgraphs. Severing edges is important as a result of the most important related parts of the graph are usually very giant. In GCP, nonetheless, we observe many small related parts as a result of our clients wish to handle their very own privateness and safety of their tasks, and don’t wish to share entry with third events.
  • Spillover results versus contamination: Experiments in social networks should care about “spillover” or affect results from friends. These spillover results are a elementary side of person conduct in a social community. Thus, the impact comes from each direct publicity of every handled particular person, and oblique publicity from his or her friends. Such spillover results additionally exist within the GCP person collaboration community however they’re of secondary significance. In our case, avoiding confusion is extra vital than estimating oblique therapy results. For instance, think about the confusion ensuing from two customers who work on a shared venture however see two completely different variations. We have to keep away from these results relatively than estimate them.

Construction of the person collaboration community

As talked about earlier, customers in GCP collaborate with different builders by way of shared tasks. Initiatives are linked to a Google Cloud billing account for correct useful resource administration and billing. Since a venture will be linked to at most one billing account, the project-to-billing account relationship is nested. Nevertheless, a person can work on a number of tasks.The user-to-project relationship will not be essentially nested. Quite, customers can have membership in a number of tasks. Subsequently the connection amongst billing account, venture and person is complicated. Determine 1 illustrates these three entities.

Determine 1. Related parts: a complicated account with a number of billing accounts managing separate units of tasks (left), a growing account with a number of tasks (center), and a brand new account (proper)

Since customers will be related by way of shared tasks, we additionally want to trace one other entity, the element of the graph to which any person belongs. A person will be related to precisely one element. Determine 2 reveals that the person collaboration graph has three distinct parts.

Determine 2. Hierarchy: element → account → venture → person

Designing experiments on the collaboration community

The hierarchical construction of the collaboration community makes it clear that we should use element because the unit of randomization in our experiments. That is mandatory to offer ensures on therapy consistency. Nevertheless, the draw back of utilizing a bigger unit of randomization is that we lose experimental energy. This comes from two elements: fewer experimental models, and higher inherent distinction throughout experimental models.

Determine Three reveals distribution of venture dimension, as measured in # customers per venture, and the distribution of # of venture per person (axes have been eliminated for confidentiality). These contribute to the construction and dimension of parts. We see right here indications of enormous variations in dimension and construction of parts. These variations have a tendency to extend the variance of our estimates and therefore lose us statistical energy.

Determine 3: # of customers (per venture) and # of tasks (per person)

One technique to mitigate this lack of energy is to cluster samples into extra homogeneous strata and pattern proportionately from every stratum. We outline strata based mostly on two options: variety of customers and “utilization”, a measure of the combination person exercise within the community. These two properties had been chosen as a result of they correlate strongly with experiment metrics of best curiosity.

By drawing a set fraction of models from every stratum, we obtain higher steadiness throughout therapy teams, and therefore scale back variance in our estimates. As well as, stratified sampling helps us get hold of consultant samples when the sampling charge is low.

The general process of our methodology for stratified random sampling is described as follows:

  1. Construct person graphs: Discover all of the parts within the present collaboration community.
  2. Stratify graphs by dimension and utilization: Measure the scale of every element by variety of customers and income and stratify graphs in variety of customers and income.
  3. Choose samples and random project: Randomly pattern a fraction of parts in every stratum from Step 2 relying on the scale of a research. Then randomly assign them to a therapy arm. For instance, if we want to run an experiment with a 5% arm for therapy and 5% for management, we first choose a random 10% of parts from every stratum, and subsequently assign them 50-50 to therapy and management teams. Every person, venture and account inherits the experiment group from the element to which they belong.
  4. Run experiment: Steps 2 and three are repeated every day after the graph has been up to date, and new parts correctly additionally randomized.

The outline above assumes that whereas new parts might emerge over the course of the experiment, the topology of current parts will stay unchanged throughout this time. As mentioned later, this isn’t fully the case.  Determine Four is a visible illustration of those steps.

Determine 4. parts and random sampling with stratification

Modeling community results

In an effort to quantify the tradeoffs concerned in experiment design, we’d like a mannequin of community results for use in subsequent simulation research. We now describe a generative mannequin for the way results would possibly propagate via the community. The community topology itself is the precise collaboration community we observe for GCP.

Take into account the case the place experiment metrics are evaluated on the per-user stage. Assume we now have $Okay$ customers. Let $Z_k$ denote the project of the $ok^{th}$ person to an arm of the experiment. Right here $Z_k = 0$ means the person is assigned to regulate and $Z_k = 1$ for therapy. Below Steady Unit 
Remedy Worth Assumption (SUTVA), one can estimate therapy impact as follows:
delta = frac{1}{N_0}sum_k Y_k[Z_k = 1] – frac{1}{N_1}sum_k Y_k [Z_k=0]    tag{1}
$$ the place $N_0$ and $N_1$ are the variety of samples assigned to therapy group and management group, respectively. That is equal to estimating within the following linear mannequin:

y_k sim mu + tau z_ktag{2}
$$ the place $mu$ is an total intercept time period and $tau$ is the impact of therapy.

When customers are related in a community, their therapy assignments can generate community results via their interactions. Our mannequin considers two points of community results:

  • Homophily or similarity inside community: customers collaborating in community are likely to behave equally. For instance, builders engaged on a selected cell app present comparable conduct in utilization. We use hierarchical fashions for this impact.
  • Spillover or contamination results: direct therapy results can spill over via community connections. We conservatively restrict the diploma of spillover results to instant neighbors.

We mannequin community similarity utilizing the absolutely nested hierarchical construction: element → account → venture. Then we use random results to mannequin response from this hierarchy as follows:
y_{i,j,ok} sim c_i + a_{i,j} + p_{i,j,ok}    tag{3}
$$ the place $c_i$ refers back to the response from Part $i$, $a_{i,j}$ from Account $j$ in Part $i$, and $p_{i,j,ok}$ from Mission $ok$ in Account $j$ in Part $i$. The random results

c_i &sim N(0, sigma_c^2)
a_{i,j} &sim N(0, sigma_a^2)
p_{i,j,ok} &sim N(0, sigma_p^2)
finish{align*} can mannequin potential correlation amongst accounts and tasks inside a element.

Spillover results are modeled as an extra element added to to the linear mannequin in (2):
y_k sim mu + tau z_k + gamma tau a_k^T cdot Z tag{4}
$$ the place $Z$ is a vector representing therapy group project of each person, and $a_k$ is the $ok^{th}$ column of adjacency matrix $A$, i.e., $m^{th}$ component of $a_k$ is $1$ if the $ok^{th}$ person and $m^{th}$ person are related. Be aware that we solely mannequin first order spillover results in (4). In different phrases, we don’t contemplate potential results from neighbors’ neighbors. Thus, our mannequin is conservative with respect to spillover results (i.e. it limits their impression). Combining spillover results and similarity inside community, we now have
y_{i,j,ok} sim mu + tau z_k + gamma tau a_k^T cdot Z + (c_i + a_{i,j} + p_{i,j,ok}) tag{5}

Experimental energy and unit of randomization

We will use the mannequin simply outlined to simulate the impact of randomization unit. We contemplate two randomization models: venture and related element. To additional illustrate the consequences of stratification on experimental energy, we pattern parts both uniformly, or by strata. In different phrases, for the three strategies of randomization

  • uniform random element
  • uniform random venture
  • stratified random element

we simulate confidence intervals for A/A assessments, i.e. when there is no such thing as a actual impact.

Determine 5 reveals empirical 95% confidence intervals for every of those sampling strategies. Because the true impact is zero in every case, we anticipate our confidence intervals to incorporate zero 95% of the time. The plot types 1000 empirical confidence intervals by their mid level (gray dot). The vertical line phase corresponding to every interval is inexperienced if it covers zero, crimson in any other case. Thus, the patch of crimson on both facet consists of about 25 circumstances (i.e. 2.5%).

The determine reveals that random sampling by element has the widest confidence interval whereas random sampling by venture has the least. Stratified sampling by element is in between. Thus stratification recovers a few of the experimental energy misplaced when going from sampling by venture to sampling by related element.

Determine 5. A/A take a look at outcomes: Confidence intervals of three strategies: random sampling by tasks, random sampling by element, and stratified sampling by element.

Estimation bias as a consequence of unit of randomization

After all, operating null experiments is hardly the aim of experiment design. The explanation we selected element because the unit of experimentation was that it higher captures spillover results when they aren’t null. As a result of randomized tasks doesn’t take community results under consideration, we’d anticipate to incur bias to the extent there are spillover results.

We generate simulation information utilizing (5) on the precise GCP person community, with the next parameter values for similarity and spillover results:

  • Similarity impact parameters: $sigma_c=2$, $sigma_a=1$ and $sigma_p=0.5$
  • Direct therapy impact dimension: $tau= frac{1}{8}$
  • Spillover impact parameter: $gamma=2^{-m}$, the place $m = 1, 2, 3, 4, 5, 6, 7$ or $8$

We various $m$ to higher perceive the contribution of community results beneath completely different ranges of spillover impact.

For every setting of the parameters, we ran three randomized experiments, as soon as for every of the three sampling strategies. Every experiment ran with 50% in therapy and 50% in management. We repeated this complete course of 1,000 occasions to estimate a distribution of impact dimension estimates.

Determine 6 reveals the distributions of estimated impact dimension for the three experiment designs based mostly on 1,000 simulation information units for mounted values of $tau=1/8$ and $gamma= 2^{-4}$. Whereas the variance of the random venture design is least, it incurs important bias. In distinction, random element and stratified element have greater variance however no observable bias.

Determine 6. Impact dimension estimates for every of the three experiment designs. Dotted line reveals the true impact dimension. Distributions estimated from 1,000 simulations, $tau=1/8$ and $gamma = 2^{-4}$.

The quantity bias in a random venture design depends upon the extent of spillover impact. That is proven for various values of $m$ in Determine 7. The bias of the random venture design is such that its 95% confidence intervals, estimated beneath independence, exclude the true impact even for small spillover results ($m leq 6$).

Determine 7. Diploma of community results and impact dimension estimation. The dotted line with “A” refers back to the true common impact.

Dynamic evolution of person collaboration community

An precise person collaboration community will not be static and evolves over time as customers begin new tasks, end current ones, or change their venture memberships. Because of this, the next 4 adjustments can occur to parts:

  • Create: a brand new element is created.
  • Break up: an current element breaks into sub-components.
  • Take away: a element not exists.
  • Merge: current parts develop into related. 

The primary three circumstances are straightforward: a newly created element is simply randomly assigned to an arm, whereas no motion must be taken for splits and removing. Issue solely arises when related parts merge as proven in Determine 8.

Determine 8. Addition and Merge: new entities (tasks and customers) added are in RED, and parts merged are in BLUE.

At this level, it’s not doable to ensure constant person therapy as outlined earlier. We might talk about nuances of graph evolution in a future publish, however for probably the most half, we’re lucky that this can be a comparatively uncommon occasion in our collaboration community as we speak. One other side of concern is that therapy can in concept have an effect on not simply experiment metrics but additionally the graph topology itself. Thus graph evolution occasions additionally should be tracked over the course of the experiment.


Designing randomized experiments on a community of customers is tougher due to community results. It’s typically inappropriate to disregard these community results, both as a result of it leads to poor (inconsistent) person expertise or as a result of doing so incurs bias in estimating the impact of therapy. Our simulation outcomes based mostly on the precise GCP person community reveal the potential for bias if the construction of the community will not be thought-about when designing experiments.


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