add readme

Author: kinkunchan

.DS_Store

@@ -2,61 +2,69 @@
 
 **Authors: GEMS Lab Team @ University of Michigan**
 
-This SEMB library allows fast onboarding to explore structural embedding of graph data using hetereogenous methods, with a unified API interface and a modular codebase enabling easy intergration of 3rd party methods and datasets.
+This SEMB library allows fast onboarding to get and evaluate structural node embeddings. With the unified API interface and the modular codebase, SEMB library enables easy intergration of 3rd-party methods and datasets.
 
-The library itself has already included a set of popular methods and datasets ready for use immediately.
+The library itself has already included a set of popular methods and datasets ready for immediate use.
+
+- Built-in methods: [node2vec](https://github.com/aditya-grover/node2vec), [struc2vec](https://github.com/leoribeiro/struc2vec), [GraphWave](https://github.com/snap-stanford/graphwave), [xNetMF](https://github.com/GemsLab/REGAL), [role2vec](https://github.com/benedekrozemberczki/role2vec), [DRNE](https://github.com/tadpole/DRNE), [MultiLENS](https://github.com/GemsLab/MultiLENS), [RiWalk](github.com/maxuewei2/RiWalk), [SEGK](https://github.com/giannisnik/segk)
+
+- Built-in datasets: 
+
+  | Dataset                                                      | # Nodes | # Edges |
+  | ------------------------------------------------------------ | ------- | ------- |
+  | [BlogCatalog](http://snap.stanford.edu/node2vec/)            | 10,312  | 333,983 |
+  | [Facebook](http://snap.stanford.edu/data/egonets-Facebook.html) | 4,039   | 88,234  |
+  | [ICEWS](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QI2T9A) | 1,255   | 1,414   |
+  | [PPI](snap.stanford.edu/graphsage/)                          | 56,944  | 818,786 |
+  | [BR air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph) | 131     | 1,038   |
+  | [EU air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph) | 399     | 5,995   |
+  | [US air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph) | 1,190   | 13,599  |
+  | [DD6](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets) | 4,152   | 20,640  |
 
 The library requires *Python 3.7+*.
 
-## Getting started
+## Installation and Usage
 
 Make sure you are using *Python 3.7+* for all below!
 
-### Installation
-`python setup.py install` (TODO: Pip support will be added soon)
-
-### Import and load a dataset
-```py
-from semb.datasets import load, get_dataset_ids
-# explore all datasets (both built in and extended by 3rd party)
-ids = get_dataset_ids()
-# load a dataset
-graph = load(ids[0])
-```
-
-### Import and load a method
-```py
-from semb.methods import load, get_method_ids
-# explore all methods (both built in and extended by 3rd party)
-ids = get_method_ids()
-# load a method, returns a constructor for a method's base class
-Method = load(ids[0])
-# create and run a method.
-# NOTE: except for the first "graph" arg, everything other argument MUST be in keyword form!
-method = Method(graph, a=1, b=2, c=3, ...)
-method.train()
-embeddings = method.get_embeddings()
-```
+`python setup.py install`
+
+After installation, we  highly recommend you go through our [Tutorial](https://github.com/GemsLab/StrucEmbeddingLibrary/blob/master/Tutorial.ipynb) to see how SEMB library works.
+
+
 
 ## Extending SEMB
 
 First make sure the `semb` library is installed.
 
 ### Developing 3rd party Dataset extension
 
-- Create a Python 3.7+ [package](https://packaging.python.org/tutorials/packaging-projects/) with a name in form of `semb-dataset[$YOUR_CHOSEN_DATASET_ID]`
+Currently, SEMB only supports embedding and evaluation on *undirected* and *unweighted* graphs.
+
+- Create a Python 3.7+ [package](https://packaging.python.org/tutorials/packaging-projects/) with a name in form at `semb/datasets/[$YOUR_CHOSEN_DATASET_ID]`
 - Within the package root directory, make sure `__init__.py` is present
 - Create a `dataset.py` and make a `Method` class that inherits from `from semb.datasets import BaseDataset` and implement the required methods. See `semb/datasets/airports/dataset.py` for more details.
+  - To use the built-in `load_dataset()`method, we accept the graph edgelist with the following format
+    - `<Node1_id (int)> <Blank> <Node2_id (int)> <\n>`
+    - Otherwise, you can overload and implement your own `load_dataset()` function. Please make sure that the returned graph is of `networkx.classes.graph.Graph` datatype. 
+  - If the dataset is accompanied by the label file, to use the built-in `load_label()` function, we accept the label file with the following format
+    - `<Node_id (int)> <delimeter> <Node_label (int)>`
+    - Otherwise, you can overload and implement your own `load_label()` function. Please make sure that the returned type is python built-in `dict()` with the key as `<Node_id (int)>` and value as `<Node_label (int)>`
 - Install the package via `setup.py` or pip.
 - Now the dataset is loadable by the main client program that uses `semb`!
 
 ### Developing 3rd party Method extension
 
-- Create a Python 3.7+ [package](https://packaging.python.org/tutorials/packaging-projects/) with a name in form of `semb-method[$YOUR_CHOSEN_METHOD_ID]`
+- Create a Python 3.7+ [package](https://packaging.python.org/tutorials/packaging-projects/) with a name in form of `semb/methods/[$YOUR_CHOSEN_METHOD_ID]`
 - Within the package root directory, make sure `__init__.py` is present
-- Create a `dataset.py` and make a `Dataset` class that inherits from `from semb.methods import BaseMethod` and implement the required methods. See `semb/methods/node2vec/method.py` for more details.
+- Create a ` method.py` and make a `Method` class that inherits from `from semb.methods import BaseMethod` and implement the required methods. See `semb/methods/node2vec/method.py` for more details.
+  - Please make sure that your implemented method accepts `networkx.classes.graph.Graph` as input.
+  - Please make sure that after `train()` is called, the `self.embeddings` should be a Python built-in `dict()` with key as `<Node_id (int)>` and value(embedding) as `<List(float)>`.
 - Install the package via `setup.py` or pip.
 - Now the method is load-able by the main client program that uses `semb`!
 
 ### Note
 For both `dataset` and `method` extensions, make sure the `get_id()` to be overridden and returns the same id as your chosen id in your package name.
+
+
+