(upbeat music)
- As we have observed the impact of the pandemic
on the way people work, we've seen a massive shift
of people working from home and also working
in hybrid work situations.
And as we observed this, we've been able
to use digital telemetry to actually measure
an organization's health by looking
at the network collaboration patterns between individuals.
Now, I should note that when we do this,
we always do this in a way that is an aggregate.
And we do this in a way that's privacy preserving.
My name is Jonathan Larson and I'm a data scientist
and a network machine learning expert at Microsoft Research.
I lead a couple of projects on societal resilience.
And today I'm excited to tell you about our latest research
on how we use network machine learning
and apply it to organizational science,
which then ultimately can help better inform how society
at large can also be resilient.
One of the first steps in understanding
how an organizational network works is to look
at the underlying data structures that support it.
And the data structure that we use
to understand organization is called a graph or a network.
Now, to understand a network it's first important
to take a look at how they are constructed.
On this slide here, what we can actually see
on left-hand side is a list of what are called edges.
This gives you a source node and a target node
and denotes that the two are connected.
On the right-hand side of the slide
we can actually see a visual depiction
of this edge list that represents
then the corresponding graph that is output from this.
To show you the power of how a graph might be useful,
let us consider the use case of looking
at co-authorship networks in a scientific conference.
So in this dashboard here, I'm going to go ahead
and select a particular author and look at other authors
who he has co-authored with at least two times.
And from this, we can now start
to extract a graph or a network of interest.
If we're zooming in on this, we can actually see
that particular author and we can see
all other related authors that are around him.
Now, you may note that in this particular analytic
you'll see nodes of different colors.
That is actually determined here
using network machine learning,
to understand which groups of people are more likely
to collaborate with one another.
So in this particular case, we can see
that this author is likely to collaborate
with several of the different author groups
in addition to the main group that he's collaborating with,
which is highlighted here in red.
Graphs are very important because they help us understand
not only just the data around what's happening,
but also the connections and the depth of those connections
as they cascade across the graph.
So, when you take a look at a graph like this,
it's not just showing you that, "Hey,
this person's connected to one person."
It's letting you dive deeper and look many hops down
to understand how those relationships
and those deeper network collaborations might form
into something more meaningful.
One of the first problems you'll start running
into though, when you start using graphs,
is that the problem that is depicted on this slide.
So, on the left-hand portion of the slide,
we actually have a graph and it's a lot of nodes
and they're just laid out all in one little area.
But the problem here is they're not organized.
On the right-hand side of the slide,
we have the exact same graph except here
we've actually visualized it in a meaningful manner.
We have used neural networks and network machine learning
to lay this graph out, to do the coloration
and then to do the positioning
and the sizing of all these nodes.
And once we do that, we can actually get a much better sense
of which nodes might be more related to one another
than other nodes in the graph.
And so this kind of gives you a sense,
almost like a map would of where the regions
of the graph might be interesting,
and which regions might be co-located
or next to one another.
So now that you understand how important network layouts
can be for helping visually debug the network you're working
with, let me introduce you to a new tool
that we've developed and now have open sourced,
called Graph Drill Down.
In this tool, it actually runs a series
of network machine learning steps,
but first, we have to drag the data on there.
So I'm gonna start first with the vertices.
These are all the actual nodes,
and you can see the individual nodes now
with no edges or links in between any of them.
Kind of looks like a star scape.
Next, I'm gonna drag the edges on.
And when I drag the edges on,
you can immediately get a view
just like the one I showed you on the previous slide.
It's kind of a jumble of nodes, it's a giant hairball,
and you can't make any sense of it.
So, how do we make sense of this?
Well, by clicking that UMAP layout
over here on the right hand side,
what it's actually doing is I've now kicked off a series
of network machine learning processes.
The first process that we are going to run is called
a Leiden Hierarchial Community Detection.
And what this will do is it will look across those nodes
and find notes that are more likely to be connected
to each other than to the rest of the graph.
It will then bundle those up together
and put them into a single cluster
and then start coloring the entire graph by cluster.
The next thing it's going to do is
to run a Neural Network Based Graph Embedding Algorithm.
Using those embeddings, we can then actually do
some down projections using UMAP,
and we can use some layout procedures
to actually generate a layout
that you now see on the screen.
So, now on this version of the graph,
we can actually start to see, there are clusters
of activity with similar color sets.
For example, there's an orange cluster over here
on the left, there's some green clusters down here,
and we can get a sense for how the clusters are connected
as well as the interconnections within those clusters.
So now that we've seen how we can actually build
network layouts and understand the collaboration patterns
around a particular network, let's move to one
of the next most common tasks
that we encounter when we're looking at graphs.
And that is the task of ranking.
Let's say you have a graph as shown here on the left.
And in this particular graph, you have the challenge
of trying to find another person
who has a similar communication pattern to another person.
So you're trying to make a recommendation
that maybe this person A should be talking to person B.
Now the task of this is very difficult
if you just take a look at the graph by itself,
because if you're looking at this graph
and you're measuring distance trivially, say for example,
by the number of hops it takes to get
to the other person, it turns out
that the entire graph is about two or three hops away.
And can't really differentiate between nodes
that are the same number of hops away.
So if you say Jim is being recommended to Bob
and Bob is two hops away, but Jim is also being recommended
to Marsha and Marsha's two hops away,
you have no way to differentiate the distance between them.
And this is where graph embeddings really come in.
Graph embeddings allow you to look at the full latent space
of the graph, because what it does is it takes each
of those vertices and it creates a representation
of that Vertex into what is called
a high dimensional tensor.
Now using these high dimensional tensors,
we can actually represent each of those nodes quantitatively
in a high dimensional space and in this case here,
I'm going to go ahead and down project it into a 3D space.
This just goes to show that we can actually calculate
measurements between any pair of vertices
and that original high dimensional space,
which we can then use to perform the ranking.
So now that we've taken a look
at how we can use graph embedding
to perform ranking operations,
let me introduce you to a few new metrics
that we've developed specifically to better help understand
organizational health and analytics.
The first metric I'm going to introduce you
to here today is called Freedom.
Freedom is a metric that lets us understand
and compare the formal organizational hierarchy
as derived from human resources and compare that
to the actual observed hierarchies and work groups
that are observed from the informal communication structures
of people just working.
So to give you a quick example,
if you have a group that has a very, very high Freedom,
that is a group that is probably working across the company.
So you might have an engineering group
that's working with marketing, sales, finance.
That group would probably have a lot of Freedom
because those people are working with other people
that are not necessarily in their hierarchy.
On the contrary, if a group has very low Freedom,
that means that they're really only working
within the confines of that HR defined organization
and they're not really worth collaborating with people
outside of that group that is very close to them there.
The next metric I'd like to introduce you
to here today is called Fluidity.
Now Fluidity is called Omni, if you take a look
at the Graspologic library that is backing this,
but what Fluidity helps you measure is
how much a person's network changes over time.
So for example, if you have a developer that's working
inside of a core team that is not experiencing much change
month over month, and they're just talking
to the same people at the same volume every month,
they would have a very low Fluidity score.
If you have someone, for example,
who is re-orged into a completely different organization,
and now they're talking to completely new people,
that person would have a very, very high Fluidity.
And so this allows us to actually measure this change
over time, based on the network collaboration patterns
that are observed.
So with these metrics we've been looking at how has COVID
actually impacted the way people work,
how have their organizational networks changed?
So for that, let me show you this image here
that actually shows a definitive change
that we actually observed across COVID.
This is looking at about 4,000 different organizations
and looking at the siloing score behavior,
which is actually a function of a company's modularity
and looking at the modularity before COVID and after COVID.
And what we see is that once the companies shift to work
from home posture, they invariably shift
to a much more siloed posture,
as you can see here on this chart.
And so there's a very noticeable statistical effect
that we can observe from the effects of COVID.
So now that we've seen how COVID has affected
these network analytics and these measures
of organizational health, let's put it
into practice and show you how we've actually
deployed these capabilities into products.
In this next demonstration here, I'm going
to show you Viva insights, the Azure templates
organizational network analytics feature.
In this tool here, we've loaded up a fake company
where all the nodes in here are completely fake,
and there's no personal information in here.
And we're gonna look at some of these network metrics
to analyze the health of this company.
So for this company, the first metric we're going
to look at is called Work Group Stability.
This helps us understand the work groups
and how much those work groups change over time.
So if you have a high stability, that can be good,
but if it's too stable, that might also give you indication
that you might want to have those groups collaborating
more with others.
Now we can also click on this and when we click on it,
it'll actually color each of the nodes by their fluidity,
which gives you another sense of stability.
So you can get a sense for understanding how much each of
those nodes are changing over time
in their network communication patterns.
The next tab that we're gonna look
at here is called network silo behavior.
And what this measures is the modularity
and the siloing nature of each of these groups.
So when I select this, it will start showing you
which groups are perhaps at risk
of breaking away and becoming too siloed.
And you might want to get them connected
with other groups to help remediate that.
The last metric we have is called Employee Cohesion.
And this metric helps you understand
within a two hop neighborhood, what's the connectivity
that we're observing.
So for example, if I'm talking to Jim and I'm talking
to Marcia, what are the chances that Jim
and Marsha are also connected?
If they're connected, that leads
to a higher Employee Cohesion.
And if they're not, that leads to a lower employee cohesion.
Now that I've shown you how we can use these metrics
to better understand organizational analytics,
let's turn our attention to how we can use these analytics
to actually understand what's going on in the world.
And for that, I'm gonna use a snapshot of a chart
that we had built for the work trends index
for the 2021 release.
And in this, we can actually again see
that siloing behavior across thousands of organizations,
as skyrockets, as COVID strikes, and then starts to wane,
but never quite returns to the pre pandemic normal.
So with that, let me leave you with a few resources
that I think should be very helpful.
The first is the Graspologic GitHub toolkit.
This is a library that helps you perform a lot
of the graph embeddings and network machine learning shown
throughout this demonstration.
The next is Viva Insights.
Viva Insights provides a turnkey capability to deploy a lot
of these capabilities directly on top of organizations to
better understand what's on going within your organization.
The third resource I wanna provide
to you is the Societal Resilience website,
which provides you a very good overview of a lot
of the other efforts that we're trying
to tackle using these types of machine learning techniques.
We hope that you've enjoyed this content
on organizational analytics and resilience.
And thank you for watching.
- We have to actually rethink the entire AI stack.
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Let's look at an interactive demo.
- All our code is now open source.
(uplifting music)