Update README.txt

Graph Laplacian Learning (GLL) Package v2.0

Author: hegilmez

README.txt

@@ -1,14 +1,26 @@
-Graph Laplacian Learning (GLL) Package v.1.0.
+Graph Laplacian Learning (GLL) Package v2.0.
 
-This MATLAB package includes implementations of graph learning algorithms presented in [1].
+This MATLAB package includes implementations of graph learning algorithms presented in [1]-[2].
+
+[1] H. E. Egilmez, E. Pavez, and A. Ortega, "Graph learning from data under Laplacian and structural constraints," IEEE Journal of Selected Topics in Signal Processing, 2017.
+   
+    Arxiv version:
+        H. E. Egilmez, E. Pavez, and A. Ortega, "Graph learning from data under structural and Laplacian constraints," CoRR, vol. abs/1611.05181v2,2016. 
+    [Online]. Available: https://arxiv.org/abs/1611.05181
+
+[2] H. E. Egilmez, E. Pavez, and A. Ortega, "Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification," IEEE Transactions on Signal and Information Processing over Networks, 2018.
+ 
+    Arxiv version:
+        H. E. Egilmez, E. Pavez, and A. Ortega, "Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification," CoRR, vol. abs/1803.02553,2018. 
+    [Online]. Available: https://arxiv.org/abs/1803.02553
 
-[1] H. E. Egilmez, E. Pavez, and A. Ortega, "Graph learning from data under structural and Laplacian constraints," CoRR, vol. abs/1611.05181v2,2016. 
-   [Online]. Available: https://arxiv.org/abs/1611.05181
 
 To install the package:
 (1) Download the source files.
 (2) Run script 'start_graph_learning.m'
 
-The demo script 'demo_animals.m' shows the usage of functions used to estimate three different types of graph Laplacian matrices discussed in [1].
+The demo script 'demo_animals.m' shows the usage of functions used to estimate three different graph Laplacian matrices discussed in [1].
+
+The demo script 'demo_us_temperature.m' shows the usage of functions used to estimate combinatorial Laplacian matrices from smooth signals discussed in [2]. The code regenerates Fig.7(e) in [2].
+
 
-Additional scripts and a more detailed description will be available soon.