[GIE][Doc] Update GIE tutorial #2583
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1. add real applications for graph traversal 2. Introduce gremlin match steps 3. Introduce some advanced techniques of gremlin in GIE 4. Add 5 LDBC BI Query as examples written with gremlin in GIE
MeloYang05
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| plt.title('Comment Length Histogram for comments from China') | ||
| plt.show() | ||
| ``` | ||
| :::{figure-md} |
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You can check https://alibaba-graphscope-build-pr-2583.surge.sh/interactive_engine/tutorial_ldbc_gremlin.html for the review. These images are not shown. You can include an image as follows:
:::{figure-md}
<img src="../images/xx.png"
alt="xxx"
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Figure xx. The caption.
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| 1. The two `person` `know` each other | ||
| 2. The two `person` `studyAt` the same `university` | ||
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| With the previously taught knowledge, you may write the following code in GIE to achieve this purpose: |
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Recommend proof-reading using chatgpt-like tools. The words can be improved a bit.
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| ## Pattern Match | ||
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| Suppose you want to find two `persons` and the `universities` they `studyAt` in the Property Graph that: |
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Property Graph -> graph. Apply to the following.
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| - "Find two `persons` and the `universities` they `studyAt` in the Property Graph that the two `person` `know` each other and the two `person` `studyAt` the same `university`" | ||
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| or can be illustrated as a figure: |
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or can be illustrated as a graph pattern as shown in the following.
| Figure 21. Pattern: two person know each other and study at the same university. | ||
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| Then you aim to find all the matched subgraphs of the Property Graph that are isomorphic to the pattern. For example, this is a sub-local-graph of university vertex(id = 144115188075858884) in the LDBC Graph: |
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sub-local-graph -> local graph. Apply to the above and the follows.
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Probably a bit too many Then, and Here are. As I said, proof-read using chatgpt.
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Recommend introducing subgraph isomorphism a little bit, and give a citation, e.g. from wikipedia,
| Figure 22. sub-local-graph of a university vertex. | ||
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| and here are the matched subgraphs in this sub-local-graph: |
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here are the matched subgraphs in this sub-local-graph. In the following, it occurs twice. Rephrase these sentences a bit.
| [{'pA': v[216172782113783812], 'pB': v[216172782113783882], 'c': v[36], 'm': v[360287970189640010]}, {'pA': v[216172782113783812], 'pB': v[216172782113783882], 'c': v[37], 'm': v[360287970189640010]}, {'pA': v[216172782113783812], 'pB': v[216172782113784105], 'c': v[38], 'm': v[360287970189640010]}, {'pA': v[216172782113783812], 'pB': v[216172782113783882], 'c': v[41], 'm': v[360287970189640010]}, {'pA': v[216172782113784105], 'pB': v[216172782113783882], 'c': v[43], 'm': v[42]}, {'pA': v[216172782113783814], 'pB': v[216172782113783962], 'c': v[50], 'm': v[360287970189640135]}, {'pA': v[216172782113783814], 'pB': v[216172782113784171], 'c': v[52], 'm': v[360287970189640135]}, {'pA': v[216172782113784481], 'pB': v[216172782113784199], 'c': v[54], 'm': v[49]}, {'pA': v[216172782113783814], 'pB': v[216172782113784038], 'c': v[56], 'm': v[360287970189640135]}, {'pA': v[216172782113783816], 'pB': v[216172782113784144], 'c': v[175], 'm': v[360287970189640462], ......, }] | ||
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| ## Advanced Techniques |
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These are not advanced techniques. Relational Operations are better terms.
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| which are all the vertices whose local id is 0. | ||
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| #### `where()` |
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Can introduce the syntactic sugar of expr() (in supported gremlin steps) for simplifying the filtering expression.
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| The output should be `[432]`. | ||
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| ### Project Steps |
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Here, the syntactic sugar of expr() can also be used. Cover some examples as well.
| The key to this query is creating two sub execution branches to count the two windows separately. We can use gremlin's `by()` step to achieve the purpose. Assume the `TagClass's name='Actor'` `$date='2012-01-01'`, `$date+100='2012-04-09'$` and `$date+200='2012-07-18'`, we can write in GIE as: | ||
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| ```python | ||
| q1 = g.execute('g.V().has(\'tagclass\', \'name\', \'Actor\').in(\'hasType\').as(\'tag\')\ |
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I suggest breaking the BI 12 query step by step to actually instruct a user to how to program such a complex query.
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BTW. Just need to break down into one query, the others can be simply given for their reference.
1. Proof read sentences by chatGPT 2. Fixed the issue where images were not displayed correctly 3. `Property Graph` -> `graph`, `sub-logcal-graph` -> `local graph` 4. `Advanced Techniques` -> `Relational Operations` 5. Add introduction of `expr()` 6. Explian LDBC BI Query 2 step by step
…into GIE_tutorial
Add real applications for graph traversal
Introduce gremlin
match()stepsIntroduce some advanced techniques of gremlin in GIE
Add 5 LDBC BI Queries as examples written with gremlin in GIE