updated README.md and doc

Author: mmschlk

README.md

@@ -19,7 +19,11 @@
 Shapley Interaction Quantification (`shapiq`) is a Python package for (1) approximating any-order Shapley interactions, (2) benchmarking game-theoretical algorithms for machine learning, (3) explaining feature interactions of model predictions. `shapiq` extends the well-known [shap](https://github.com/shap/shap) package for both researchers working on game theory in machine learning, as well as the end-users explaining models. SHAP-IQ extends individual Shapley values by quantifying the **synergy** effect between entities (aka **players** in the jargon of game theory) like explanatory features, data points, or weak learners in ensemble models. Synergies between players give a more comprehensive view of machine learning models.
 
 ## 🛠️ Install
-`shapiq` is intended to work with **Python 3.10 and above**. Installation can be done via `pip`:
+`shapiq` is intended to work with **Python 3.10 and above**. Installation can be done via `uv` and `pip`:
+
+```sh
+uv add shapiq
+```
 
 ```sh
 pip install shapiq

examples/api_examples/si_graph.ipynb

@@ -5,7 +5,7 @@
    "id": "58b9498051a5294b",
    "metadata": {},
    "source": [
-    "# SI Graph Plot\n",
+    "# API Example: SI Graph Plot\n",
     "\n",
     "This notebook is designed to demonstrate (and to document) how to use the `shapiq.si_graph` plot.\n",
     "The SI-graph-plot visualizes all interactions of computed Shapley Interactions, including those of higher order, as a network.\n",
@@ -49,7 +49,6 @@
    "id": "b857f08efa0c61d4",
    "metadata": {},
    "source": [
-    "\n",
     "## General Use\n",
     "The easiest use is simply calling interaction_values.si_graph_plot() on the interaction values you want to visualize. \n",
     "But first, we import the needed modules and train a model we want to explain.\n",
@@ -215,7 +214,7 @@
    "id": "11e70372add8e9a",
    "metadata": {},
    "source": [
-    "# Adjusting the positions\n",
+    "## Adjusting the positions\n",
     "The positions of the nodes are determined automatically.\n",
     "The base case is a circular layout, we can change it to a spring layout by setting ``circular_layout = False``.\n",
     "It is also possible to set the positions manually, with a dict mapping the players to their respective positions.\n",
@@ -228,7 +227,7 @@
    "id": "83927d1f7f4f63e6",
    "metadata": {},
    "source": [
-    "# Focusing on specific interactions\n",
+    "## Focusing on specific interactions\n",
     "When plotting interaction values of higher orders, the number of edges can make it difficult to read.\n",
     "If we want to simplify our graph, we can filter and visualize only a subset of interactions that are most relevant.\n",
     "For example, we can focus on interactions above a certain threshold, select the 10 strongest interactions (by absolute value), or limit our view to only positive or negative interactions.\n",