Update render script and Makefile

terminal/networkembedding

@@ -1,4 +1,4 @@
-                                                                                             awesome-network-embedding
+                                                                                   awesome-network-embedding
 !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
 !PRs Welcome (https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square) (http://makeapullrequest.com)
 !Gitter chat for developers at https://gitter.im/dmlc/xgboost (https://badges.gitter.im/Join%20Chat.svg) (https://gitter.im/awesome-network-embedding/Lobby)
@@ -11,7 +11,7 @@
 
 
 
-                                                                                    Paper References with the implementation(s)
+                                                                          Paper References with the implementation(s)
 - GraphGym
   - A platform for designing and evaluating Graph Neural Networks (GNN), NeurIPS 2020
   - Paper  (https://proceedings.neurips.cc/paper/2020/file/c5c3d4fe6b2cc463c7d7ecba17cc9de7-Paper.pdf)
@@ -266,7 +266,8 @@
   - Python  (https://github.com/benedekrozemberczki/diff2vec) 
 - Poincare
   - Poincaré Embeddings for Learning Hierarchical Representations (https://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations), NIPS 2017
-  - PyTorch  (https://github.com/facebookresearch/poincare-embeddings) Python  (https://radimrehurek.com/gensim/models/poincare.html) C++  (https://github.com/TatsuyaShirakawa/poincare-embedding)
+  - PyTorch  (https://github.com/facebookresearch/poincare-embeddings) Python  (https://radimrehurek.com/gensim/models/poincare.html) C++  
+(https://github.com/TatsuyaShirakawa/poincare-embedding)
 - PEUNE
   - PRUNE: Preserving Proximity and Global Ranking for Network Embedding (https://papers.nips.cc/paper/7110-prune-preserving-proximity-and-global-ranking-for-network-embedding), NIPS 2017
   - code  (https://github.com/ntumslab/PRUNE)
@@ -331,8 +332,8 @@
   - source  (https://github.com/TimDettmers/ConvE)
 - node2vec
   - node2vec: Scalable Feature Learning for Networks (http://dl.acm.org/citation.cfm?id=2939672.2939754), KDD'16
-  - arxiv  (https://arxiv.org/abs/1607.00653) Python  (https://github.com/aditya-grover/node2vec) Python-2  (https://github.com/apple2373/node2vec) Python-3  (https://github.com/eliorc/node2vec) C++  
-(https://github.com/xgfs/node2vec-c)  
+  - arxiv  (https://arxiv.org/abs/1607.00653) Python  (https://github.com/aditya-grover/node2vec) Python-2  (https://github.com/apple2373/node2vec) Python-3  
+(https://github.com/eliorc/node2vec) C++  (https://github.com/xgfs/node2vec-c)  
 - DNGR
   - Deep Neural Networks for Learning Graph Representations (http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12423), AAAI'16
   - Matlab  (https://github.com/ShelsonCao/DNGR) Python Keras  (https://github.com/MdAsifKhan/DNGR-Keras)
@@ -383,7 +384,7 @@
   - Deep Network Embedding for Graph Representation Learning in Signed Networks
   - paper  (https://ieeexplore.ieee.org/document/8486671) Code  (https://github.com/shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks)
 
-                                                                                                 Paper References
+                                                                                       Paper References
 
 A Comprehensive Survey on Graph Neural Networks (https://arxiv.org/abs/1901.00596), arxiv'19
 
@@ -448,7 +449,7 @@
 
 Comprehend DeepWalk as Matrix Factorization (https://arxiv.org/abs/1501.00358), arxiv
 
-                                                                                               Conference & Workshop
+                                                                                     Conference & Workshop
 
 Graph Neural Networks for Natural Language Processing (https://github.com/svjan5/GNNs-for-NLP), EMNLP'19
 
@@ -458,7 +459,7 @@
 
 WWW-18 Tutorial Representation Learning on Networks (http://snap.stanford.edu/proj/embeddings-www/), WWW'18
 
-                                                                                                   Related List
+                                                                                         Related List
 
 awesome-graph-classification (https://github.com/benedekrozemberczki/awesome-graph-classification)
 
@@ -482,7 +483,7 @@
 
 awesome-graph-classification (https://github.com/benedekrozemberczki/awesome-graph-classification)
 
-                                                                                                  Related Project
+                                                                                        Related Project
 
 Stanford Network Analysis Project website (http://snap.stanford.edu/)