Fixed error for reverse indices and changed GraphNumpyContainer into Alias. Simplified name of default entries.

Author: aimat

README.md

@@ -82,11 +82,22 @@ A major issue for graphs is their flexible size and shape, when using mini-batch
 
 ### Input
 
-Here, for ragged tensors, the nodelist of shape `(batch, None, F)` and edgelist of shape `(batch, None, F')` have one ragged dimension `(None, )`.
-The graph structure is represented by an index-list of shape `(batch, None, 2)` with index of incoming or receiving node `i` and outgoing or sending node `j` as `(i, j)`. 
-The first index of incoming node `i` is usually sorted for faster pooling operations, but can also be unsorted. 
-Furthermore, the graph is directed, so an additional edge with `(j, i)` is required for undirected graphs. 
-A ragged constant can be directly obtained from a list of numpy arrays: `tf.ragged.constant(indices, ragged_rank=1, inner_shape=(2, ))` which yields shape `(batch, None, 2)`.
+Graph tensors for edge-indices or attributes for multiple graphs is passed to the model in form of ragged tensors 
+of shape `(batch, None, Dim)` where `Dim` denotes a fixed feature or index dimension.
+Such a ragged tensor has `ragged_rank=1` with one ragged dimension indicated by `None` and is build from a value and partition tensor.
+For example, the graph structure is represented by an index-list of shape `(batch, None, 2)` with index of incoming or receiving node `i` and outgoing or sending node `j` as `(i, j)`.
+Note, an additional edge with `(j, i)` is required for undirected graphs. 
+A ragged constant can be easily created and passed to a model:
+
+```python
+import tensorflow as tf
+import numpy as np
+idx = [[[0, 1], [1, 0]], [[0, 1], [1, 2], [2, 0]], [[0, 0]]]  # batch_size=3
+# Get ragged tensor of shape (3, None, 2)
+print(tf.ragged.constant(idx, ragged_rank=1, inner_shape=(2, )).shape)
+print(tf.RaggedTensor.from_row_lengths(np.concatenate(idx), [len(i) for i in idx]).shape) 
+```
+
 
 ### Model