TL;DR

All items and item statements that are instance of of X are considered subgraph of X. Some obvious subgraphs were merged into one.
0.5% (~400) subgraphs form 90% of Wikidata (triples and items). Subgraph size visualization: subgraph stats
List of Top 50 subgraps: #Table_of_top_50_subgraph_information
The most common top predicates in the subgraphs are description, rdf:type, wikibase:rank, and references.
The growth rate of all subgraphs look consistent yet stable. None of them are growing at an unprecedented rate, but growing nonetheless. Growth rate visualization: subgraph_growth_rate
A visualization to show the connectivity between the top 50 subgraphs and how strong the connections are: wikidata_graph
- Almost all subgraphs are connected to most other subgraphs in one way or other
- Most connections are through triples
- Subgraphs that share items are considered more connected (therefore, heavily weighted edges)
- No isolated subgraph group or cluster was found in the top 50 subgraphs
- The larger the subgraph gets, the more connections to other subgraphs it has
A list of the subgraph pairs that have the common items: nonzero_subgraph_pair_detail.csv. While most of the pairs seem reasonable, such as mountain-hill, some pairs seem odd, like village-human. This list can be used as a guide to solve some of the errors in Wikidata.

What are subgraphs?

Wikidata contains all kinds of data from various aspects of knowledge. All of these data are highly inter-connected, but we can find some patterns. We find subgraphs within Wikidata and find out how large these subgraphs are, how connected they are, and finally how much these subgraphs are used (queried).

In order to find subgraphs, the following steps were taken:

Consider all items that are instance of (P31) the same item to be under a subgraph. For example: all items that are instance of Q13442814 are part of one subgraph.
Some subgraphs were merged where it was obvious. For example: all subclasses of astronomical object were considered part of astronomical object as they were all indeed some sort of astronomical object. This method of sublcass merging is not applicable everywhere without manual inspection.
Some large subgraphs were almost completely part of another subgraph. For example: all items under Review Articles are also instance of scholarly article. In such case, review articles was not considered a separate subgraph.

Subgraph sizes

Using only instance of, Wikidata has 82,919 subgraphs. The distribution of the sizes of these subgraphs has a clear long tail, with very few subgraphs incorporating most items in Wikidata. Subgraph size can be calculated in two ways:

The number of items it contains
The number of triples related to the items in a subgraph. This is what we refer as subgraph size from here on.

Takeaways:

Most calculations from here on will take the top 50 subgraphs, which form 85% of Wikidata
340 top subgraphs (0.5% of all subgraphs, after merging some) form 90% of Wikidata (91% of all items and 90% of all triples). These subgraphs have >=10,000 items each.
Rest 99.5% of the subgraphs have <10,000 items each, and together form 10% of Wikidata.

Below is the distribution of the number of items in a subgraphs.

To be more specific,

Subgraph item distribution
	Number of subgraphs		Number of items
There are	54,602	subgraph(s) with more than	1	item(s)
	23,724		10
	6,625		100
	1,712		1,000
	392		10,000
	63		100,000
	10		1,000,000
	1		10,000,000

Below is the subgraph size comparison of top 340 subgraphs in Wikidata (90%).

Below is the subgraph size comparison of top 50 subgraphs in Wikidata (85%).

Here is an interactive graph showing the comparison of subgraph sizes in terms of item count and triple count: subgraph stats.

Here are some subgraph size visualizations in WDQS:

Size as percentage of Wikidata each subgraph occupies: query link
Size as percentage of Wikidata items each subgraph contains: query link

Number of days to recovery Given the current rate of growth, how long would it take wikidata to get back to its original size again if some amount of triples were removed from it? This helps us estimate what to temporarily remove from Wikidata in the siatuation Wikidata backend maxes out. The growth rate of triples is not constant, but considering the growth an approximate straight line, in grafana dashboard, Wikidata grows at a rate of 4.77M triples per day. This rate was calculated from the number of triples at the start and end of a 90-day interval (11/3/21 to 6/6/21). It could be faster or a bit slower than this. This will give us a wide approximation of the number of days we can gain by removing some parts of Wikidata.

Triples

The triples within a subgraph can be of various types. They can be:

truthy triples like wdt
non-wikidata direct triples like rdfs:label, schema:name etc
full statements that hold other information

See more about these statement types here: RDF_Dump_Format#Statement_types.

Table of top 50 subgraph information

Top 50 Subgraphs in Wikidata
Rank	Subgraph	Subgraph Name	Number of items	% of WD items	Number of triples	% of WD Triples	Number of days to recover	%of truthy statements	%of non-wikidata direct statements	%of full statements	Number of unique properties
1	Q13442814	scholarly article	37,362,641	39.75	6,539,020,889	49.73	1370.86	12.62	24.28	63.25	722
2	Q6999	astronomical object	8,412,914	8.95	1,136,682,291	8.64	238.3	10.20	14.24	76.07	578
3	Q5	human	9,315,444	9.91	954,536,943	7.26	200.11	13.31	20.06	60.94	4482
4	Q4167836	Wikimedia category	4,840,195	5.15	753,127,982	5.73	157.89	1.19	86.06	5.19	610
5	Q16521	taxon	3,180,248	3.38	367,926,462	2.8	77.13	10.11	37.22	42.97	963
6	Q101352	family name	481,445	0.51	187,299,892	1.42	39.27	1.59	93.49	6.62	375
7	Q4167410	Wikimedia disambiguation page	1,359,804	1.45	180,124,174	1.37	37.76	0.89	88.32	3.83	796
8	Q7187	gene	1,196,361	1.27	122,421,508	0.93	25.66	14.13	12.51	73.11	218
9	Q11266439	Wikimedia template	845,852	0.9	114,308,711	0.87	23.96	0.78	87.06	3.20	222
10	Q11173	chemical compound	1,223,387	1.3	91,228,463	0.69	19.13	12.69	35.72	50.86	591
11	Q8054	protein	986,599	1.05	88,483,828	0.67	18.55	14.26	13.51	72.21	267
12	Q3305213	painting	539,468	0.57	56,769,083	0.43	11.9	12.10	24.46	63.35	785
13	Q13100073	village-level division in China	588,477	0.63	51,615,572	0.39	10.82	6.84	62.73	30.41	77
14	Q11424	film	263,070	0.28	47,176,067	0.36	9.89	14.37	12.74	66.01	1029
15	Q486972	human settlement	563,958	0.6	39,590,792	0.3	8.3	10.84	22.94	49.32	1120
16	Q13406463	Wikimedia list article	334,939	0.36	33,742,245	0.26	7.07	2.45	78.06	10.48	880
17	Q13433827	encyclopedia article	512,141	0.55	33,373,227	0.25	7.0	9.03	45.52	39.76	164
18	Q8502	mountain	525,553	0.56	33,340,188	0.25	6.99	11.37	27.79	50.39	709
19	Q2668072	collection	500,968	0.53	32,670,637	0.25	6.85	15.24	12.30	72.71	665
20	Q79007	street	578,926	0.62	30,252,119	0.23	6.34	13.86	24.24	59.88	572
21	Q4022	river	399,552	0.42	28,833,476	0.22	6.04	11.34	24.99	52.15	580
22	Q30612	clinical trial	356,838	0.38	27,731,502	0.21	5.81	16.05	12.33	71.78	124
23	Q532	village	274,840	0.29	26,483,275	0.2	5.55	9.69	26.25	45.22	818
24	Q17633526	Wikinews article	286,950	0.3	21,830,150	0.17	4.58	3.68	72.76	16.46	256
25	Q482994	album	269,095	0.29	21,181,015	0.16	4.44	12.20	22.64	53.05	638
26	Q23397	lake	260,135	0.28	18,053,096	0.14	3.78	11.32	25.43	53.19	602
27	Q54050	hill	327,277	0.35	17,228,390	0.13	3.61	12.67	23.07	56.77	470
28	Q16970	church building	211,291	0.22	16,821,530	0.13	3.53	14.03	15.06	63.56	1036
29	Q41176	building	265,925	0.28	16,293,008	0.12	3.42	14.21	14.99	68.06	1243
30	Q56436498	village in India	145,824	0.16	15,383,416	0.12	3.23	8.34	22.23	64.50	210
31	Q4830453	business	193,858	0.21	14,101,220	0.11	2.96	13.06	16.23	60.48	1790
32	Q47150325	calendar day of a given year	189,366	0.2	14,078,486	0.11	2.95	6.85	56.25	34.18	56
33	Q3947	house	197,736	0.21	12,468,434	0.1	2.61	15.26	15.18	70.67	760
34	Q3331189	version, edition, or translation	157,486	0.17	10,997,589	0.08	2.31	15.41	14.70	69.39	1006
35	Q18593264	item of collection or exhibition	147,402	0.16	10,732,969	0.08	2.25	16.96	9.69	73.38	306
36	Q27020041	sports season	158,877	0.17	10,693,504	0.08	2.24	12.11	15.12	53.43	353
37	Q355304	watercourse	174,620	0.19	10,080,421	0.08	2.11	12.17	25.07	54.79	302
38	Q7725634	literary work	164,860	0.18	10,049,521	0.08	2.11	13.85	16.69	58.88	1093
39	Q23442	island	148,587	0.16	9,885,277	0.08	2.07	11.40	22.52	50.06	829
40	Q11060274	print	119,806	0.13	9,700,063	0.07	2.03	14.85	9.74	76.96	269
41	Q811979	architectural structure	145,957	0.16	9,666,936	0.07	2.03	10.06	9.91	52.63	994
42	Q5084	hamlet	118,188	0.13	9,013,534	0.07	1.89	10.94	18.63	55.55	423
43	Q9842	primary school	157,451	0.17	8,916,373	0.07	1.87	13.99	16.22	68.98	410
44	Q19389637	biographical article	151,026	0.16	8,238,397	0.06	1.73	12.76	21.07	57.51	131
45	Q21014462	cell line	128,805	0.14	7,955,975	0.06	1.67	10.62	36.18	54.60	60
46	Q47521	stream	124,853	0.13	6,654,366	0.05	1.4	12.95	19.67	58.11	280
47	Q59199015	group of stereoisomers	111,599	0.12	5,843,270	0.04	1.23	15.43	18.82	67.23	216
48	Q61443690	branch post office	129,183	0.14	5,313,033	0.04	1.11	14.59	14.86	70.54	22
49	Q49008	prime number	127,545	0.14	5,188,768	0.04	1.09	10.01	36.78	52.40	101
50	Q4164871	position	120,117	0.13	4,720,668	0.04	0.99	12.72	32.76	52.28	654

Triples per item

While it is interesting to see how big a subgraph is and how many items it has, it is helpful to know how many triples each item has typically in a given subgraph. A very basic idea can be gained from density of a subgraph, where density = #of triples / #of items, in subgraph stats. Below is a diagram of box plot showing triples per item distribution for the top 50 subgraphs. The box plot omits min and max values, shows only mean, median, Q1, and Q3.

Predicates

Predicates are used in all subgraph. Sometimes some predicates are almost exclusively used in a particular subgraphs, other times a predicate may be used 99% of times in that particular subgraph. Moreover, the unique predicates used in a subgraph can inform us of the range of diverse statements a subgraph contains. These and some more analysis were done on predicates below.

Number of unique predicates

The number of unique predicates a subgraph uses has been listed in the table above (#Table of top 50 subgraph information). Feel free to sort by property column to view the most/least diverse subgraph.

Predicate distribution

There are ~7500 unique predicates across the top 50 subgraphs. Among them ~3500(46%) are used in any 1 subgraph only. Below is a figure showing this distribution.

Here is a csv file to the subgraph-predicate count: subgraph_pred_df_info.csv.

Top predicates

While it is interesting to see the top predicates for each subgraph, it is too much to view for this page. Below is a table of only the top 3 predicates per subgraph. Here is a csv file with the top 5 predicates per subgraph: top_subgraph_pred.csv. You can view more from subgraph_pred_df_info.csv with some filtering and grouping.

Note that:

The most common top predicates are description, rdf:type, wikibase:rank, and references.
Only scholarly articles' descriptions are 10% of Wikidata, with cites work and wikibase:rank being ~6%.
Wikimedia category descriptions are 4.5%, and the rest is ~1% or less of Wikidata triples.

Top predicates per subgraph and their triple distribution
	1st top predicate				2nd top predicate				3rd top predicate
Subgraph	Predicate	#of triples	%triples in subgraph	%triples in Wikidata	Predicate	#of triples	%triples in subgraph	%triples in Wikidata	Predicate	#of triples	%triples in subgraph	%triples in Wikidata
Wikimedia category	description	596672076	79.226	4.517	22-rdf-syntax-ns#type	22761531	3.022	0.172	rdf-schema#label	15094771	2.004	0.114
Wikimedia disambiguation page	description	112264922	62.326	0.85	rdf-schema#label	39587180	21.978	0.3	22-rdf-syntax-ns#type	4146198	2.302	0.031
Wikimedia list article	description	23714609	70.282	0.18	22-rdf-syntax-ns#type	1449015	4.294	0.011	instance of	1022424	3.03	0.008
Wikimedia template	description	93286385	81.609	0.706	22-rdf-syntax-ns#type	2918907	2.554	0.022	instance of	2557094	2.237	0.019
Wikinews article	description	14128436	64.72	0.107	22-rdf-syntax-ns#type	1114921	5.107	0.008	instance of	862083	3.949	0.007
album	22-rdf-syntax-ns#type	2793067	13.187	0.021	ontology#rank	2250457	10.625	0.017	rdf-schema#label	1871641	8.836	0.014
architectural structure	ontology#rank	1290969	13.354	0.01	22-rdf-syntax-ns#type	1289149	13.336	0.01	prov#wasDerivedFrom	725555	7.506	0.005
astronomical object	ontology#rank	144578828	12.719	1.095	prov#wasDerivedFrom	128331955	11.29	0.972	22-rdf-syntax-ns#type	117137727	10.305	0.887
biographical article	22-rdf-syntax-ns#type	1212529	14.718	0.009	ontology#rank	1061395	12.884	0.008	description	532327	6.462	0.004
branch post office	22-rdf-syntax-ns#type	775436	14.595	0.006	ontology#rank	775436	14.595	0.006	prov#wasDerivedFrom	645628	12.152	0.005
building	22-rdf-syntax-ns#type	2372760	14.563	0.018	ontology#rank	2262925	13.889	0.017	prov#wasDerivedFrom	1130158	6.936	0.009
business	22-rdf-syntax-ns#type	1948087	13.815	0.015	ontology#rank	1669295	11.838	0.013	prov#wasDerivedFrom	919184	6.518	0.007
calendar day of a given year	rdf-schema#label	6152052	43.698	0.047	instance of	1146460	8.143	0.009	22-rdf-syntax-ns#type	1040850	7.393	0.008
cell line	rdf-schema#label	1060304	13.327	0.008	description	1042089	13.098	0.008	22-rdf-syntax-ns#type	833878	10.481	0.006
chemical compound	description	24753956	27.134	0.187	22-rdf-syntax-ns#type	10660602	11.686	0.081	ontology#rank	10484519	11.493	0.079
church building	22-rdf-syntax-ns#type	2541716	15.11	0.019	ontology#rank	2256786	13.416	0.017	prov#wasDerivedFrom	972599	5.782	0.007
clinical trial	22-rdf-syntax-ns#type	4453735	16.06	0.034	ontology#rank	4453642	16.06	0.034	minimum age	1595252	5.752	0.012
collection	22-rdf-syntax-ns#type	5206241	15.936	0.039	ontology#rank	5204557	15.93	0.039	prov#wasDerivedFrom	2150646	6.583	0.016
encyclopedia article	description	11968615	35.863	0.091	22-rdf-syntax-ns#type	3302636	9.896	0.025	ontology#rank	2913370	8.73	0.022
family name	description	59885567	31.973	0.453	rdf-schema#label	57692163	30.802	0.437	core#altLabel	51377678	27.431	0.389
film	22-rdf-syntax-ns#type	7284888	15.442	0.055	ontology#rank	6480184	13.736	0.049	prov#wasDerivedFrom	3597645	7.626	0.027
gene	prov#wasDerivedFrom	16328401	13.338	0.124	22-rdf-syntax-ns#type	16046507	13.108	0.121	ontology#rank	15992810	13.064	0.121
group of stereoisomers	22-rdf-syntax-ns#type	874827	14.972	0.007	ontology#rank	871313	14.911	0.007	found in taxon	612204	10.477	0.005
hamlet	22-rdf-syntax-ns#type	1222418	13.562	0.009	ontology#rank	924362	10.255	0.007	prov#wasDerivedFrom	813808	9.029	0.006
hill	22-rdf-syntax-ns#type	2215050	12.857	0.017	ontology#rank	1857312	10.781	0.014	GeoNames ID	1568224	9.103	0.012
house	22-rdf-syntax-ns#type	1893455	15.186	0.014	ontology#rank	1825282	14.639	0.014	coordinate location	746416	5.986	0.006
human	22-rdf-syntax-ns#type	127210431	13.327	0.963	ontology#rank	115226473	12.071	0.872	rdf-schema#label	82571873	8.65	0.625
human settlement	22-rdf-syntax-ns#type	5214907	13.172	0.039	ontology#rank	3840166	9.7	0.029	description	3222856	8.14	0.024
island	22-rdf-syntax-ns#type	1282293	12.972	0.01	ontology#rank	958245	9.694	0.007	description	897631	9.08	0.007
item of collection or exhibition	22-rdf-syntax-ns#type	1821311	16.969	0.014	ontology#rank	1820436	16.961	0.014	part of	712209	6.636	0.005
lake	description	2406074	13.328	0.018	22-rdf-syntax-ns#type	2199894	12.186	0.017	ontology#rank	1813415	10.045	0.014
literary work	22-rdf-syntax-ns#type	1478965	14.717	0.011	ontology#rank	1252618	12.464	0.009	instance of	617093	6.141	0.005
mountain	description	4367836	13.101	0.033	22-rdf-syntax-ns#type	4046473	12.137	0.031	ontology#rank	3238261	9.713	0.025
painting	description	10311414	18.164	0.078	22-rdf-syntax-ns#type	6848409	12.064	0.052	ontology#rank	6791199	11.963	0.051
position	22-rdf-syntax-ns#type	616685	13.064	0.005	ontology#rank	580022	12.287	0.004	rdf-schema#label	544323	11.531	0.004
primary school	22-rdf-syntax-ns#type	1252277	14.045	0.009	ontology#rank	1236386	13.866	0.009	prov#wasDerivedFrom	1008762	11.314	0.008
prime number	description	795712	15.335	0.006	ontology#rank	644583	12.423	0.005	22-rdf-syntax-ns#type	526967	10.156	0.004
print	22-rdf-syntax-ns#type	1425563	14.696	0.011	ontology#rank	1425199	14.693	0.011	prov#wasDerivedFrom	1078309	11.117	0.008
protein	prov#wasDerivedFrom	12427165	14.045	0.094	22-rdf-syntax-ns#type	11368057	12.848	0.086	ontology#rank	11357567	12.836	0.086
river	description	3651037	12.662	0.028	22-rdf-syntax-ns#type	3567310	12.372	0.027	ontology#rank	2829316	9.813	0.021
scholarly article	description	1324177494	20.25	10.025	cites work	853611996	13.054	6.462	ontology#rank	796548851	12.181	6.03
sports season	22-rdf-syntax-ns#type	1572731	14.707	0.012	ontology#rank	1156720	10.817	0.009	instance of	496113	4.639	0.004
stream	22-rdf-syntax-ns#type	873978	13.134	0.007	ontology#rank	734964	11.045	0.006	GeoNames ID	587699	8.832	0.004
street	22-rdf-syntax-ns#type	4236711	14.005	0.032	ontology#rank	4096946	13.543	0.031	description	2256816	7.46	0.017
taxon	rdf-schema#label	69840848	18.982	0.529	description	45308808	12.315	0.343	22-rdf-syntax-ns#type	40988244	11.14	0.31
version, edition, or translation	22-rdf-syntax-ns#type	1591714	14.473	0.012	ontology#rank	1538937	13.993	0.012	prov#wasDerivedFrom	712852	6.482	0.005
village	22-rdf-syntax-ns#type	3307961	12.491	0.025	description	3212844	12.132	0.024	ontology#rank	2304145	8.7	0.017
village in India	description	2336722	15.19	0.018	ontology#rank	1467450	9.539	0.011	22-rdf-syntax-ns#type	1392224	9.05	0.011
village-level division in China	description	24717636	47.888	0.187	rdf-schema#label	4720063	9.145	0.036	22-rdf-syntax-ns#type	3533117	6.845	0.027
watercourse	22-rdf-syntax-ns#type	1256378	12.464	0.01	ontology#rank	1039967	10.317	0.008	description	1009377	10.013	0.008

Predicates across subgraphs

From the predicates point of view: how widely are they used? We already know some predicates are used in 1 subgraph. What about the others? Following is a diagram showing the usage of the 60 most used predicates in Wikidata across various subgraphs. Note that the usage were calculated only for the top 50 subgraphs, which account for ~85% of Wikidata. So this should give us an idea of the high use cases of each of these predicates. The rest can be considered a long tail to each of these plots.

The x-axis shows the rank of the subgraph instead of the name to save space. The rank-name mapping can be found in #Table of top 50 subgraph information. The figures are large but can be clicked and zoomed in without loss of resolution for better viewing.

Predicate usage in log scale

Below is the same image in logarithmic scale to show the wide differences in usage counts across subgraphs where applicable.

Top usage

As mentioned above, we can isolate some predicates that are used >=99% in a particular subgraph. Some are even used 100% of the times in that particular subgraph. The following graph shows the distribution of the highest percentage usage in a subgraph a predicate has.

For the predicates that are used a lot in a particular subgraph, it is possible that it is used a very small number of times in other subgraphs (second max usage count is low) or it is also used a lot in other subgraphs (second max usage count is quite high). In short: we also want to look at second max percentages.

Here is an interactive plot showing the max percent, second max percent, color coded with the number of subgraphs the predicate is used in: predicate_usage

Rate of growth of subgraphs

Here is an interactive chart showing the growth of the top 50 subgraphs over a period of one month: subgraph_growth_rate

Subgraph Connectivity

Wikidata is very inter-connected. Although some analysis is possible for individual graphs, it is also important to look into how connected these subgraphs are. In fact, Wikidata is a whole graph in and of itself. For the purposes of analysis, we defined subgraphs, otherwise, Wikidata (as of now) does not distinguish among these subgraphs in ways.

To define connectivity among subgraphs, we consider not only if they are directly connected, but also if they share some properties and items. These components of connectivity are described below. A visualization was created to show the strength of this connectivity between subgraphs here: wikidata_graph. This also shows further information about the subgraphs themselves and what connects them.

Through items

Items in Wikidata can be instance of (P31) multiple entities. If an item is P31 mountain and also P31 hill, we can assume mountains and hills are closely related. Following this assumption, we find the number of items that each pair of subgraph has in common. This helps us find similar subgraphs in Wikidata. A 50 X 50 table containing the number of items each pair of subgraph has in common is available here: subgraph_pair_items.csv. This can also be seen as an information on the edges in the subgraph visualization linked above.

Out of the ~80M items that fall under the top 50 subgraphs:

40,232 items are instance of 2 entities (in the top 50)
57 items are instance of 3 entities (in the top 50)
And the rest are instance of only 1 entity
There are examples of items that are instance of more than 3 entities, but not all of those entities are part of the top 50 subgraphs.

Following is a list of the subgraph pairs that have the most common items. The full list is given in nonzero_subgraph_pair_detail.csv. While most of the pairs seem reasonable, such as mountain-hill and river-watercourse, some pairs seem odd. This can also be confirmed by the small number of items the two subgraphs have in common. For example, it is odd for the same item to be a village and a river, or a human and a scholarly article, especially since out of the thousands of items these subgraphs have, only 6-10 items are shared. This list can be used as a guide to solve some of the errors (or check if they in fact are errors) in Wikidata.

Subgraph pairs with the most number of common items
subgraph1	subgraph2	#of items in subgraph1	#of items in subgraph2	#of shared items	%of subgraph1 items shared	%of subgraph2 items shared
hill	mountain	327277	525553	37102	11.34	7.06
river	watercourse	399552	174620	14972	3.75	8.57
hamlet	village	118188	274840	5282	4.47	1.92
hamlet	human settlement	118188	563958	3120	2.64	0.55
human settlement	village	563958	274840	2986	0.53	1.09
church building	architectural structure	211291	145957	2622	1.24	1.8
river	stream	399552	124853	2204	0.55	1.77
version, edition, or translation	literary work	157486	164860	1304	0.83	0.79
building	architectural structure	265925	145957	946	0.36	0.65
building	house	265925	197736	942	0.35	0.48

Through properties

Some properties can be used across Wikidata, while others are exclusive to some types of items. We get an idea about predicate usage in #Predicates across subgraphs section. Finding pairs of subgraphs that use a lot of the same properties forms a connection between them. If a pair of subgraphs has a lot of properties in common, it is possible for entities to show up in queries that use these properties, or at least the query may touch on those entities. A 50 X 50 table containing the number of predicates each pair of subgraph has in common is available here: subgraph_pair_predicates.csv, and a heatmap for the same is shown below.

Through triples

Another component of subgraph connectivity is direct connection among the triples across subgraphs. For example:

SELECT ?article ?author
WHERE
{
  ?article wdt:P31 wd:Q13442814.
  ?author wdt:P31 wd:Q5.
  ?article wdt:P50 ?author.
}
LIMIT 10

This query contains triple that connects the scholarly article subgraph with the human subgraph through P50, this is because ?article is instance of scholarly article, whereas ?author is instance of human. The connection here is from scholarly article subgraph to human subgraph.

This connection is defined as such: If the object of a triple that belong to subgraph A, is instance of subgraph B, there is a connection between subgraph A and B

Some of the top subgraph pairs with highest number of triple connection are shown below. Note that subgraphs have a lot of connections to themselves but is not shown here. More information about these subgraphs connections can be found in the subgraph visualization: wikidata_graph.

Subgraph pairs with most direct triple connections (directed edges)
From subgraph	To Subgraph	Triple count
scholarly article	human	1813767
protein	gene	938854
gene	protein	937527
human	family name	321203
film	human	229953
scholarly article	chemical compound	173924
Wikimedia category	taxon	156468
taxon	Wikimedia category	156440
Wikimedia disambiguation page	family name	131914
family name	Wikimedia disambiguation page	127352

A 50X50 chart of all the subgraph pairs and number of directed connections (triple from subgraph A to B is different from triples from subgraph B to A) between them can be found here: subgraph_pair_triple_connections.csv. A logarithmic scale heatmap of this chart is shown below.