Revise docs for GAE #2439

doudoubobo · 2023-02-20T06:31:11Z

as titled.

github-actions · 2023-02-20T06:31:35Z

🎊 PR Preview 387d2b0 has been successfully built and deployed to https://alibaba-graphscope-build-pr-2439.surge.sh

yecol · 2023-02-20T07:05:35Z

docs/getting_started_gae.md

+
+## Running NexworkX Graph Algorithms
+
+GraphScope is compatible with NetworkX APIs, and thus a graph algorithm developed for NetworkX (e.g., degree centrality) can directly on GraphScope:


yecol · 2023-02-20T07:06:50Z

docs/design_of_gae.md

@@ -1,2 +1,27 @@
 # Design of GAE

+In GraphScope, Graph Analytics Engine (GAE) is responsible for handling various graph analytics algorithms. GAE has three main components: graph storage, execution framework and algorithm library. Next, we give an overview to each of them below.


spell out the relation of GAE and GRAPE.

yecol · 2023-02-20T07:08:01Z

docs/design_of_gae.md

+
+## Algorithm Library
+
+GAE of GraphScope provides 20 graph analytics algorithms as built-in algorithms, and users can directly invoke them. The build-in algorithms contain most commonly used algorithms, including PageRank, BFS, DFS, shortest path and LCC. In addition, GraphScope is compatible with NetworkX APIs, and thus diverse kinds of [built-in algorithms in NetworkX](https://networkx.org/documentation/stable/reference/algorithms/index.html) can also be directly invoked by users. In total, over 100 build-in graph analytical algorithms can be directly executed over GraphScope, without any developing effort.


Where can we find the full list of the supported 20 built-in algorithms? give link.

yecol · 2023-02-20T07:10:27Z

docs/getting_started_gae.md


+## Installing Environments for Compilation 


Do we need this to run your code?
Since this is a "getting started", please give SIMPLE example covered by pip installed graphscope.

yecol · 2023-02-20T07:10:57Z

docs/getting_started_gae.md

+```bash
+git clone git@github.com:alibaba/GraphScope.git
+cd GraphScope
+./scripts/install_deps.sh --dev


Besides, we are using gs, ask Siyuan for details.

yecol · 2023-02-20T07:14:39Z

docs/graph_analytics_workloads.md


-Currently, there exist diverse types of graph analytics algorithms, which usually iteratively access a large fraction of (even all) vertices/edges of a graph to explore underlying insights hidden in graph data. Typical graph analytics algorithms include general analytics algorithms (e.g., PageRank, shortest path, and maximum flow), community detection algorithms (e.g., maximum clique/bi-clique, connected components, Louvain and label propagation), graph mining algorithms (e.g., frequent structure mining and graph pattern discovery). 
+Currently, there exist diverse types of graph analytics algorithms, which usually iteratively access a large fraction of (even all) vertices/edges of a graph to explore underlying insights hidden in graph data. Typical graph analytics algorithms include general analytics algorithms (e.g., PageRank, the shortest path, and maximum flow), community detection algorithms (e.g., maximum clique/bi-clique, connected components, Louvain and label propagation), graph mining algorithms (e.g., frequent structure mining and graph pattern discovery). These graph analytics algorithms have been widely applied in real scenarios. For example,  as shown in the following figure, Google uses PageRank to rank web pages in their search engine results, and the shortest path algorithm can help path planning in logistics and delivery services.


We can give more details about the analytical algorithms as context and background.
e.g., what is, what for, what's the application scenarios for

pagerank;

SSSP, (shortest path)

Louvain, (community detection)..

yecol · 2023-02-20T07:16:07Z

docs/graph_analytics_workloads.md


-GAE of GraphScope provides 20 graph analytics algorithms as built-in algorithms, and users can directly invoke them. The build-in algorithms contain most commonly used algorithms, including PageRank, BFS, DFS, shortest path and LCC. In addition, GraphScope is compatible with NetworkX APIs, and thus diverse kinds of [built-in algorithms in NetworkX](https://networkx.org/documentation/stable/reference/algorithms/index.html) can also be directly invoked by users. In total, over 100 build-in graph analytical algorithms can be directly executed over GraphScope, without any developing effort.
+## Characteristics of Graph Analytics Workloads


Challenges of Graph Analytics on Large Graphs.

for small graphs, well studied.

yecol · 2023-02-20T07:19:43Z

docs/graph_analytics_workloads.md

+
+## What can GraphScope Do 
+
+In GraphScope, Graph Analytics Engine (GAE) is responsible for handling graph analytics algorithms.It addresses the abovementioned challenges in the following ways:


If you make them as bullet or subsection,
describe "how achieved this" in Design of GAE and give a link here for each subsection.

yecol · 2023-02-20T07:20:05Z

docs/how_to_customize_algorithms_with_python.md

@@ -0,0 +1,150 @@
+# How to customize algorithms with Python
+


add a intro paragraph.

yecol · 2023-02-20T07:21:44Z

docs/java_tutorials.md

-In addition, GraphScope GAE supports the PIE programming model, which can provide better performance than widely-used vertex-centric model. GraphScope GAE is equipped with `Java PIE` interface exposed by `GRAPE-jdk`, and users can develop their own algorithms using the PIE programming model.
-
-The following tutorials will introduce how to leverage the above features in GraphScope GAE Jave SDK.
+Java plays an important role in big data ecosystem, and a lot of graph processing systems developed by Java (e.g., Apache Giraph and GraphX) have been widely applied. To integrate with Java ecosystem, GraphScope allows graph analytics algorithms developed for Apache Giraph and GraphX to directly run over GraphScope GAE.


merge this in the user_guide_and_examples_of_gae page.

doudoubobo added 5 commits February 10, 2023 16:40

revise graph analytics

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update gae

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update gae design

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Add docs for GAE

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doudoubobo requested a review from yecol February 20, 2023 06:31

Add GRAPE to GAE

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doudoubobo added 3 commits February 20, 2023 18:51

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doudoubobo merged commit 1b69aba into alibaba:main Feb 21, 2023

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Revise docs for GAE #2439

Revise docs for GAE #2439

doudoubobo commented Feb 20, 2023

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yecol Feb 20, 2023


		## Running NexworkX Graph Algorithms

		GraphScope is compatible with NetworkX APIs, and thus a graph algorithm developed for NetworkX (e.g., degree centrality) can directly on GraphScope:

		@@ -1,2 +1,27 @@
		# Design of GAE

		In GraphScope, Graph Analytics Engine (GAE) is responsible for handling various graph analytics algorithms. GAE has three main components: graph storage, execution framework and algorithm library. Next, we give an overview to each of them below.


		## Algorithm Library

		GAE of GraphScope provides 20 graph analytics algorithms as built-in algorithms, and users can directly invoke them. The build-in algorithms contain most commonly used algorithms, including PageRank, BFS, DFS, shortest path and LCC. In addition, GraphScope is compatible with NetworkX APIs, and thus diverse kinds of [built-in algorithms in NetworkX](https://networkx.org/documentation/stable/reference/algorithms/index.html) can also be directly invoked by users. In total, over 100 build-in graph analytical algorithms can be directly executed over GraphScope, without any developing effort.


		Currently, there exist diverse types of graph analytics algorithms, which usually iteratively access a large fraction of (even all) vertices/edges of a graph to explore underlying insights hidden in graph data. Typical graph analytics algorithms include general analytics algorithms (e.g., PageRank, shortest path, and maximum flow), community detection algorithms (e.g., maximum clique/bi-clique, connected components, Louvain and label propagation), graph mining algorithms (e.g., frequent structure mining and graph pattern discovery).
		Currently, there exist diverse types of graph analytics algorithms, which usually iteratively access a large fraction of (even all) vertices/edges of a graph to explore underlying insights hidden in graph data. Typical graph analytics algorithms include general analytics algorithms (e.g., PageRank, the shortest path, and maximum flow), community detection algorithms (e.g., maximum clique/bi-clique, connected components, Louvain and label propagation), graph mining algorithms (e.g., frequent structure mining and graph pattern discovery). These graph analytics algorithms have been widely applied in real scenarios. For example, as shown in the following figure, Google uses PageRank to rank web pages in their search engine results, and the shortest path algorithm can help path planning in logistics and delivery services.


		GAE of GraphScope provides 20 graph analytics algorithms as built-in algorithms, and users can directly invoke them. The build-in algorithms contain most commonly used algorithms, including PageRank, BFS, DFS, shortest path and LCC. In addition, GraphScope is compatible with NetworkX APIs, and thus diverse kinds of [built-in algorithms in NetworkX](https://networkx.org/documentation/stable/reference/algorithms/index.html) can also be directly invoked by users. In total, over 100 build-in graph analytical algorithms can be directly executed over GraphScope, without any developing effort.
		## Characteristics of Graph Analytics Workloads


		## What can GraphScope Do

		In GraphScope, Graph Analytics Engine (GAE) is responsible for handling graph analytics algorithms.It addresses the abovementioned challenges in the following ways:

Revise docs for GAE #2439

Revise docs for GAE #2439

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Challenges of Graph Analytics on Large Graphs.

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