diff --git a/README.md b/README.md
index 89ca48b..ed7dc7a 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
-<!-- # TGB -->
-![TGB logo](imgs/2023_TGX_logo.png)
+<!-- # TGX -->
+![TGX logo](imgs/2023_TGX_logo.png)
 
 # Temporal Graph Analysis with TGX (WSDM 2024 Demo Track)
 <h4>
@@ -7,15 +7,55 @@
 	<a href="https://complexdata-mila.github.io/TGX/"><img src="https://img.shields.io/badge/docs-orange"></a>
 </h4> 
 
-<!-- <a href="https://pypi.org/project/py-tgb/"><img src="https://img.shields.io/pypi/v/py-tgb.svg?color=brightgreen"></a>
-<a href="https://tgb.complexdatalab.com/"><img src="https://img.shields.io/badge/website-blue"></a> -->
+TGX supports all datasets from [TGB](https://tgb.complexdatalab.com/) and [Poursafaei et al. 2022](https://openreview.net/forum?id=1GVpwr2Tfdg) as well as any custom dataset in `.csv` format. 
+TGX provides numerous temporal graph visualization plots and statistics out of the box
 
 
-### Example Usage ###
-TGX provides many built in datasets as well as supporting TGB datasets. In addition, TGX provides dataset discretization and visualization.
-To get started, see our [starting example](https://github.com/ComplexData-MILA/TGX/blob/master/starting_example.py)
+### Data Loading ###
+For detailed tutorial on how to load the datasets into `tgx.Graph`, see [`docs/tutorials/data_loader.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_loader.ipynb)
+
+1. Load TGB datasets
+```
+import tgx
+dataset = tgx.tgb_data("tgbl-wiki")
+ctdg = tgx.Graph(dataset)
+```
+
+2. Load built-in datasets
+```
+dataset = tgx.builtin.uci()
+ctdg = tgx.Graph(dataset)
+```
+
+3. Load custom datasets from `.csv` 
+```
+from tgx.io.read import read_csv
+toy_fname = "docs/tutorials/toy_data.csv"
+edgelist = read_csv(toy_fname, header=True,index=False, t_col=0,)
+tgx.Graph(edgelist=edgelist)
+```
+
+### Visualization and Statistics ###
+For detailed tutorial on how to generate visualizations and compute statistics for temporal graphs, see [`docs/tutorials/data_viz_stats.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_viz_stats.ipynb)
+
+1. Discretize the network (required for viz)
+
+```
+dataset = tgx.builtin.uci()
+ctdg = tgx.Graph(dataset)
+time_scale = "weekly"
+dtdg, ts_list = ctdg.discretize(time_scale=time_scale, store_unix=True)
+```
+
+2. Plot the number of nodes over time
+
+```
+tgx.degree_over_time(dtdg, network_name="uci")
+```
+
+3. Compute novelty index
 ```
-python starting_example.py -d tgbl-wiki -t daily
+tgx.get_novelty(dtdg)
 ```
 
 
diff --git a/docs/index.md b/docs/index.md
index f0a452f..db941f5 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,175 +1,106 @@
-# Welcome to TGX
+<!-- # TGX -->
+![TGX logo](2023_TGX_logo.png)
 
+# Temporal Graph Analysis with TGX (WSDM 2024 Demo Track)
+<h4>
+	<a href="https://arxiv.org/abs/2402.03651"><img src="https://img.shields.io/badge/arXiv-pdf-yellowgreen"></a>
+	<a href="https://complexdata-mila.github.io/TGX/"><img src="https://img.shields.io/badge/docs-orange"></a>
+</h4> 
 
+TGX supports all datasets from [TGB](https://tgb.complexdatalab.com/) and [Poursafaei et al. 2022](https://openreview.net/forum?id=1GVpwr2Tfdg) as well as any custom dataset in `.csv` format. 
+TGX provides numerous temporal graph visualization plots and statistics out of the box
 
-### Pip Install
 
-You can install TGX via [pip](https://pypi.org/project/py-tgb/)
+### Data Loading ###
+For detailed tutorial on how to load the datasets into `tgx.Graph`, see [`docs/tutorials/data_loader.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_loader.ipynb)
+
+1. Load TGB datasets
 ```
-pip install py-tgx
+import tgx
+dataset = tgx.tgb_data("tgbl-wiki")
+ctdg = tgx.Graph(dataset)
 ```
 
-<!-- ### Links and Datasets
-
-<!-- The project website can be found [here](https://tgb.complexdatalab.com/). -->
-
-<!-- The API documentations can be found [here](https://shenyanghuang.github.io/TGB/). -->
-
-<!-- all dataset download links can be found at [info.py](https://github.com/shenyangHuang/TGB/blob/main/tgb/utils/info.py)
-
-TGB dataloader will also automatically download the dataset as well as the negative samples for the link property prediction datasets. --> 
-
-
-<!-- ### Install dependency -->
-### Install dependency
-Our implementation works with python >= 3.9 and can be installed as follows
-
-1. set up virtual environment (conda should work as well)
+2. Load built-in datasets
 ```
-python -m venv ~/tgx_env/
-source ~/tgx_env/bin/activate
+dataset = tgx.builtin.uci()
+ctdg = tgx.Graph(dataset)
 ```
 
-2. install external packages
+3. Load custom datasets from `.csv` 
 ```
-pip install -r requirements.txt
+from tgx.io.read import read_csv
+toy_fname = "docs/tutorials/toy_data.csv"
+edgelist = read_csv(toy_fname, header=True,index=False, t_col=0,)
+tgx.Graph(edgelist=edgelist)
 ```
 
+### Visualization and Statistics ###
+For detailed tutorial on how to generate visualizations and compute statistics for temporal graphs, see [`docs/tutorials/data_viz_stats.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_viz_stats.ipynb)
 
+1. Discretize the network (required for viz)
 
-3. install local dependencies under root directory `/TGX`
 ```
-pip install -e py-tgx
+dataset = tgx.builtin.uci()
+ctdg = tgx.Graph(dataset)
+time_scale = "weekly"
+dtdg, ts_list = ctdg.discretize(time_scale=time_scale, store_unix=True)
 ```
 
+2. Plot the number of nodes over time
 
-<!-- ### Instruction for tracking new documentation and running mkdocs locally
-
-1. first run the mkdocs server locally in your terminal 
 ```
-mkdocs serve
+tgx.degree_over_time(dtdg, network_name="uci")
 ```
 
-2. go to the local hosted web address similar to
+3. Compute novelty index
 ```
-[14:18:13] Browser connected: http://127.0.0.1:8000/
-``` -->
-
-<!-- Example: to track documentation of a new hi.py file in tgb/edgeregression/hi.py -->
-
-
-<!-- 3. create docs/api/tgb.hi.md and add the following
+tgx.get_novelty(dtdg)
 ```
-# `tgb.edgeregression`
 
-::: tgb.edgeregression.hi
-``` -->
 
-<!-- 4. edit mkdocs.yml 
-```
-nav:
-  - Overview: index.md
-  - About: about.md
-  - API:
-	other *.md files 
-	- tgb.edgeregression: api/tgb.hi.md
-``` -->
+### Install dependency
+Our implementation works with python >= 3.9 and can be installed as follows
 
-### Creating new branch ###
+1. set up virtual environment (conda should work as well)
 ```
-git fetch origin
-
-git checkout -b test origin/test
+python -m venv ~/tgx_env/
+source ~/tgx_env/bin/activate
 ```
 
-### dependencies for mkdocs (documentation)
+2. install external packages
 ```
-pip install mkdocs
-pip install mkdocs-material
-pip install mkdocstrings-python
-pip install mkdocs-jupyter
-pip install notebook
-pip install mkdocs-glightbox
+pip install -r requirements.txt
 ```
 
-
-### full dependency list
-Our implementation works with python >= 3.9 and has the following dependencies
+3. install local dependencies under root directory `/TGX`
+<!-- ```
+pip install -e py-tgx
+``` -->
 ```
-matplotlib==3.8.0
-pandas==2.1.1
-numpy==1.26.0
-seaborn==0.13.0
-tqdm==4.66.1
-scikit-learn==1.3.1
-tgb==0.9.0
+pip install -e .
 ```
 
 
 
-<!-- ## Code blocks
-
-`pip install tgb` -->
-
-
-
-<!-- 
-
-### Plain codeblock
-
-A plain codeblock:
+3. [alternatively] install from test-pypi
 
 ```
-Some code here
-def myfunction()
-// some comment
+pip install -i https://test.pypi.org/simple/ py-tgx
 ```
+You can specify the version with `==`, note that the pypi version might not always be the most updated version
 
-#### Code for a specific language
 
-Some more code with the `py` at the start:
-
-``` py
-import tensorflow as tf
-def whatever()
+4. [optional] install mkdocs dependencies to serve the documentation locally
 ```
-
-#### With a title
-
-``` py title="bubble_sort.py"
-def bubble_sort(items):
-    for i in range(len(items)):
-        for j in range(len(items) - 1 - i):
-            if items[j] > items[j + 1]:
-                items[j], items[j + 1] = items[j + 1], items[j]
+pip install mkdocs-glightbox
 ```
 
-#### With line numbers
+### Creating new branch ###
 
-``` py linenums="1"
-def bubble_sort(items):
-    for i in range(len(items)):
-        for j in range(len(items) - 1 - i):
-            if items[j] > items[j + 1]:
-                items[j], items[j + 1] = items[j + 1], items[j]
+first create the branch on github
 ```
+git fetch origin
 
-#### Highlighting lines
-
-``` py hl_lines="2 3"
-def bubble_sort(items):
-    for i in range(len(items)):
-        for j in range(len(items) - 1 - i):
-            if items[j] > items[j + 1]:
-                items[j], items[j + 1] = items[j + 1], items[j]
+git checkout -b test origin/test
 ```
-
-## Icons and Emojs
-
-:smile: 
-
-:fontawesome-regular-face-laugh-wink:
-
-:fontawesome-brands-twitter:{ .twitter }
-
-:octicons-heart-fill-24:{ .heart } -->
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 6fe62b9..c8ab89a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,4 +1,4 @@
-site_name: TGX
+site_name: Temporal Graph Analysis with TGX
 site_url: https://shenyanghuang.github.io/TGX
 nav:
     - Home: index.md
@@ -62,21 +62,15 @@ nav:
 theme:
   name: material
   features:
+    - navigation.tabs
+    - toc.integrate
     - navigation.footer
     - navigation.indexes
-    # - navigation.instant
-    # - navigation.instant.prefetch
-    # - navigation.instant.progress
-    # - navigation.prune
     - navigation.sections
-    - navigation.tabs
     - navigation.top
     - navigation.tracking
     - navigation.tabs.sticky
-    # - navigation.sections
     - navigation.expand
-    # - toc.integrate
-    # - navigation.top
     - search.suggest
     - search.highlight
     - content.tabs.link
@@ -94,11 +88,13 @@ theme:
       toggle:
         icon: material/toggle-switch
         name: Switch to light mode    
-      primary: orange
+      primary: blue
       accent: lime
 
 
 plugins: 
+  - search
+
   - glightbox:
        touchNavigation: true
        loop: false
@@ -131,16 +127,9 @@ plugins:
 
   - mkdocs-jupyter:
       execute: false
-#         handlers:
-#           python:
-#             paths: [.] 
+
 markdown_extensions:
   - pymdownx.arithmatex:
       generic: true
   - attr_list
   - md_in_html
-
-# extra_javascript:
-#   - javascripts/mathjax.js
-#   - https://polyfill.io/v3/polyfill.min.js?features=es6
-#   - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
\ No newline at end of file