From 46814a1a78bda53fe3fce38336d977c6b9dba7bd Mon Sep 17 00:00:00 2001
From: microLizzy <32970198+microLizzy@users.noreply.github.com>
Date: Wed, 16 Oct 2019 00:09:05 -0700
Subject: [PATCH] Update motifs-and-structral-roles_lecture.md

---
 preliminaries/motifs-and-structral-roles_lecture.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/preliminaries/motifs-and-structral-roles_lecture.md b/preliminaries/motifs-and-structral-roles_lecture.md
index c19f778..6908b22 100755
--- a/preliminaries/motifs-and-structral-roles_lecture.md
+++ b/preliminaries/motifs-and-structral-roles_lecture.md
@@ -59,11 +59,12 @@ We can consider roles as functions of nodes in a network which can be measured b
 ### Structural equivalence
 We say nodes u and v are structurally equivalent if they have the same relationships to each other. Structurally equivalent nodes are likely to be similar in many different ways. For example, node u and v are structurally equivalent in Figure 4 since they connect other nodes in the same way.
 
-![Figure 4](../assets/img/Exact_Subgraph_Enumeration.png?style=centerme)
+![Figure 4](../assets/img/structurally_equivalent.png?style=centerme)
 
 ### RoIX
 Roles allow us to identify different properties of nodes in network. Here we will introduce an automatic structural roles discovery method called RolX. It's an unsupervised learning approach without prior knowledge. Figure 5 is the RoIX approach overview.
 
+![Figure 5](../assets/img/RoIX.png?style=centerme)
 
 ### Recursive Feature Extraction
 The basic idea of recursive feature extraction is to aggregate features of a node and use them to generate new recursive features. By this way we can turn network connectivity into structural features.