Seeing the shape of what your team knows

What you will take away from this lesson

A team that has been writing knowledge pages for a year has a corpus, and nobody can answer the obvious questions about it. Which definitions is everything else leaning on? Which subjects have quietly split into two disconnected halves? Which pages cite a dataset the warehouse no longer has? Reading the pages one at a time will not tell you, because none of those facts live inside any single page.

The graph view answers them from the references the pages already carry. 206 covered how a fact becomes a knowledge page and what a page links to. This lesson is about what those links add up to once there are enough of them.

Learning Objectives

01Say what a node and an edge are in the knowledge graph, and which entities become nodes.
02Explain why the view opens on one node rather than the whole corpus, and what Hops and Whole corpus each do.
03Read node size as the measurement it is: how much of the corpus that entity holds together.
04Use the inspector to walk the corpus without a page load, and trace the shortest chain of references between any two nodes.
05Recognise an unresolved catalog reference as a documentation gap rather than an ordinary node.
06State what the graph leaves out, and where it tells you it has done so.

Where we are in the curriculum

If any term in this lesson feels unfamiliar, the 100 series is one click back. The 200 series assumes that mental model.

100 Series: the foundation

Open index

If a term in this lesson looks unfamiliar, back up to the 100 series. The 200 series assumes that mental model. Every row above is a direct link.

What is drawn: the pages, the things they cite, and the citations

The Knowledge Pages sub-tab offers two layouts of the same corpus, switched with a Cards and Graph toggle. Cards is the browse list you already know. Graph draws the corpus as its reference network, and the first thing to settle is what the marks on that canvas mean, because everything else in this lesson is read off them.

Every node is a thing, every edge is a citation

An edge is one stored reference, drawn as an arrow running from the page to the thing it cites. Nothing is inferred and nothing is guessed at from the text: if an editor attached the reference, the arrow is there, and if they did not, it is not. A node is either end of one of those arrows.

Knowledge pages
The corpus itself. Every page is a node, and pages are always drawn, because the graph is a view of them.
Assets, prompts, collections
The portal entities a page cites: a dashboard the definition governs, the prompt that produces the report, the collection it belongs to.
Connections
The configured systems a page names. A connection has no page of its own in the portal, which is why it is the one node type with nothing to open.
Catalog entities
Datasets, glossary terms, tags, and domains, identified by the URN the catalog generated for them. These are the nodes that reach outside the portal, and the ones that can fail to resolve.
Other knowledge pages
A page citing a page. These edges are what turn a pile of documents into a corpus with a shape worth looking at.

Shape and colour carry the type, so a dataset and a dashboard stay distinguishable at a glance. Size is left free to carry something else, which is the subject of a later section.

What it looks like

The portal Knowledge graph opened on the neighbourhood around one page: twenty of forty nodes drawn as labelled circles, squares, and diamonds joined by dashed arrows, with an Explore and Whole corpus toggle, a Hops selector, node-type filter chips, and an inspector panel on the right reporting the selected page's references out, references in, bridge score, and cluster — The graph as it opens, centred on the corpus's strongest bridge. The summary line above the canvas says exactly what is on screen and what is not: *20 of 40 nodes, 8 clusters*, with the partition's modularity in brackets. The inspector on the right belongs to the selected node and is covered further down.

Why it opens on one node

A force layout of an entire corpus is the picture everyone has seen and nobody has used. It is dense in the middle, it changes every time it settles, and the one question it answers reliably is whether you have a lot of documents, which you already knew.

So the graph starts somewhere specific. It opens on the corpus’s strongest bridge, the node the most shortest paths run through, and draws that node’s neighbourhood. That choice is itself a finding: the entity the view opens on is the one holding the most of your knowledge together, and if it is a page nobody has edited in eighteen months, you have learned something before touching a control.

Two views of the same corpus

Explore: one node and its neighbourhood

Where the view opens

The strongest bridge in the corpus, its immediate neighbours, and nothing else. Hops widens the ring one step at a time, so the picture grows only as fast as you can still read it.

Whole corpus: every node, clusters tinted

What you switch to on purpose

Every node at once, with each substantial cluster tinted as a region behind its members and the layout pulling those members together so the regions actually separate. This is the view for the question does our knowledge have topics at all, and it is a deliberate switch rather than the default.

The whole corpus, when you actually want it

Whole corpus is still worth switching to, for a different question. Not what surrounds this page, but whether the corpus has topics at all, and whether they are the topics you believe your team has.

The same knowledge graph switched to Whole corpus: all forty nodes and sixty-four references drawn at once, with each detected cluster tinted as a shaded region behind the nodes belonging to it, and two isolated nodes sitting outside any region — The whole-corpus overview, with the detected clusters drawn as regions. The two nodes sitting alone on the right are as informative as the dense regions: they are pages nothing else in the corpus cites and that cite nothing themselves.

The layout is computed, not arranged

The difference between this and a diagram somebody drew is that every visual property here is the output of a measurement over the reference network. Size is not emphasis chosen by a designer, and the tinted regions are not groupings somebody decided on. Both are computed from the citations, which is why they can tell you something you did not already believe.

What each visual property is measuring

Node size
How much of the corpus stops connecting if this entity goes away. The biggest marks on screen are the definitions, datasets, and pages holding otherwise separate topics together, which makes them the ones you cannot afford to leave stale.
Computed by: Betweenness centrality: the share of shortest paths through the graph that run through this node.
Cluster regions
Which parts of your knowledge are actually about each other. A subject that has split into two regions with one thin link between them is a subject two teams are documenting separately.
Computed by: Louvain community detection over the reference network.
The summary line
Whether the structure you are looking at is real. It states how many clusters were found and the partition’s modularity, so a corpus with no genuine topic separation reports a low number instead of being read into a picture.
Computed by: Modularity of the partition, printed rather than implied.

Clicking inspects rather than navigates

Selecting a node opens an inspector beside the canvas instead of taking you to a page. That distinction is what makes the view usable for investigation: following a citation, seeing where it goes, and backing out costs nothing, so you can follow twenty of them in the time one page load would take.

Clicking a node inspects it rather than navigating away

References out and referenced by
How many citations run each way, counted separately. A page with many outward references and none inward is a leaf; one with the reverse is something the rest of the corpus depends on.
Bridge score and rank
The measurement behind the node’s size, with its standing among everything in the corpus that bridges anything. Rank is the part worth reading, because a raw path count means nothing on its own.
Cluster
Which of the detected clusters this node landed in, and how many were found. Two nodes you assumed were about the same subject sitting in different clusters is a finding.
Both directions of references, listed
Every citation out and every citation in, each one selectable in place. Selecting one moves the inspector to that node without a page load, which is how you walk the corpus.
The URN, for anything that is not a page
The identifier the node’s label was derived from, and the only string you can search or act on anywhere else. A label is for reading; a URN is for doing something with.

Moving through the corpus

Four actions, one of which leaves the graph

Focus
Re-centres the view on this node and redraws its neighbourhood around it.
Expand
Pulls in this node’s neighbours without moving the centre, so you grow the picture in the direction you are curious about.
Path from
Then click any other node, and the shortest chain of references between the two is highlighted on the canvas and listed hop by hop. This is the answer to “how are these two things related”, stated as the actual citations rather than a guess.
Open
The only action that leaves the graph. It is absent where there is nothing to open: a connection has no per-instance page in the portal, and a reference whose target has been removed has no destination at all.

Hovering lights a node's immediate neighbourhood and dims the rest. Dragging pulls a cluster apart and it stays where you drop it, with Reset layout releasing every pin. The search box focuses matching nodes rather than removing the others, and switching back to Cards keeps both the search text and the tag filter.

Catalog nodes are resolved against the live catalog

Most nodes in this graph are portal entities, and the portal knows everything about them. Catalog nodes are different: they name something that lives in DataHub, and whether that thing is still there is a question only the catalog can answer.

A citation that does not resolve is the interesting one

Selecting a catalog node looks the dataset up in DataHub and shows what is actually there, naming the connection it queried: the description, the domain, the owners, the tags. That is useful on its own. A reader deciding whether to trust a page can see the current state of the data it is written about without leaving the canvas.

The more valuable case is the other one. When the catalog does not have the dataset, the inspector says so plainly instead of drawing the node as though it resolved. A knowledge page citing a dataset that is not in your catalog is a documentation gap: the table was renamed, the pipeline was retired, or the page was written about something that never made it into the warehouse. Any of those is worth knowing, and none of them is visible from reading the page.

What the graph leaves out

A visualization has an unusual capacity to mislead, because a picture reads as the whole of something in a way a list does not. Three rules keep this one honest, and all three are about telling you what is missing.

What the graph leaves out, and where it tells you

Entities you cannot access are absent entirely
Neither node nor edge. Not greyed out, not shown as a placeholder, because a placeholder is itself a disclosure that something exists. This is the same visibility rule the per-page reference list applies, so the graph and the page agree about what you can see.
A very large corpus is capped
And the cap is stated in a notice above the canvas rather than applied quietly. A picture that silently omits half your corpus is worse than no picture, because it reads as complete.
The summary line is always on screen
How many nodes are drawn out of how many exist, how many clusters were found, and the modularity of that partition. Every claim the layout makes is stated in numbers next to it.

Where this leads

The two things worth carrying out of this lesson are practical rather than conceptual. The largest node on screen names the definition your corpus is leaning on hardest, which tells you where review effort is worth spending. An unresolved catalog reference names a page written about data your catalog cannot confirm, which tells you where the documentation and the warehouse have come apart.

Key terms

Eleven terms cover the vocabulary of the graph view. Two of them, bridge score and cluster, are the ones doing the work: they are what turns a drawing of your corpus into a measurement of it.

Key Terms

Node: One entity in the graph: a knowledge page, or anything a page cites. Shape and colour carry its type; size carries its bridge score.
Edge: One stored reference, drawn as an arrow from the page to the thing it cites. Edges come from what editors attached, not from anything inferred out of the prose.
Neighbourhood: A node and everything within a chosen number of hops of it. The graph opens on one, because a force layout of an entire corpus is a hairball that answers nothing.
Hops: The control that widens the neighbourhood one step at a time. Whole corpus is the separate switch that drops back to the overview.
Bridge score: How much of the graph a node holds together, computed as betweenness centrality and drawn as node size. The largest marks on screen are the entities joining otherwise separate topics.
Cluster: A group of nodes more connected to each other than to the rest, found by Louvain community detection and tinted as a region in the whole-corpus overview.
Modularity: The quality of the cluster partition, printed in the summary line. It is what lets you tell a corpus with real topic structure from one a layout has merely spread out.
Path from: The action that traces the shortest chain of references between two nodes, highlighted on the canvas and listed hop by hop. It answers how two things are related with the actual citations.
Unresolved catalog reference: A catalog node whose dataset the connection does not have. The inspector states it rather than drawing the node as if it resolved, because it marks a page written about data your catalog cannot confirm.
URN: The identifier a node's label was derived from, shown for anything that is not a knowledge page. It is the string you can search or act on elsewhere, and the same identifier the catalog surfaces in 211 resolve for you behind a name.
Cap notice: The statement above the canvas when a corpus is too large to draw in full. The cap exists; what matters is that it is announced rather than applied silently.

212 - Seeing the shape of what your team knows