diff --git a/Schrick-Noah_CS-7863_Homework-3.R b/Schrick-Noah_CS-7863_Homework-3.R
index fd9638c..599a1b7 100644
--- a/Schrick-Noah_CS-7863_Homework-3.R
+++ b/Schrick-Noah_CS-7863_Homework-3.R
@@ -89,7 +89,7 @@ y2_val <- eigen(g2.Lap)$values[n2-2]
 lap_dist1 <- dist(cbind(x1,y1))
 lap_dist2 <- dist(cbind(x2,y2))
 
-# Use hierachical clustering of distance matrix
+# Use hierarchical clustering of distance matrix
 lap_tree1 <- hclust(lap_dist1)
 lap_tree2 <- hclust(lap_dist2)
 
diff --git a/self_newman_mod.R b/self_newman_mod.R
new file mode 100644
index 0000000..efa3ad9
--- /dev/null
+++ b/self_newman_mod.R
@@ -0,0 +1,43 @@
+newman_mod <- function(g, weights=NULL){
+  A <- get.adjacency(g) # adj
+  m <- ecount(g)
+  n <- vcount(g)
+  
+  if (is.null(weights)){
+    weights <- rep(1,n)
+  }
+  
+  # Obtain the modularity matrix
+  B.node.i <- function(i){degree(g)[i]*degree(g)}
+  B.node.all <- sapply(1:n, B.node.i)
+  B <- A - (B.node.all/(2*m))
+  # NOTE: This is identical to: modularity_matrix(g) ! Can verify with:
+  # modularity_matrix(g) == B
+  B.eigs <- eigen(B)
+  max.lam <- B.eigs$values[1]
+  s <- ifelse(B.eigs$vectors[,1]>0,1,-1)
+  
+  weights <- B.eigs$vectors[n]/B.eigs$vectors[,1]
+  # Plotting
+  #V(g)$color <- ifelse(B[1,]>0,"green","yellow")
+  V(g)$color <- ifelse(B.eigs$vectors[,1]>0,"green","yellow")
+  V(g)$size <- 10
+  plot(g, main=paste(g1.netname, " Newman Modularity"))
+  clust1 = list()
+  clust2 = list()
+  clusters = list()
+  
+  # Make list of clusters
+  for(i in 1:n){
+      ifelse(V(g)[i]$color=="green",
+              clust1 <- append(clust1, V(g)[i]$name),
+              clust2 <- append(clust2, V(g)[i]$name))}
+
+  clusters <- list(clust1, clust2)
+  
+  Q.node.i <- function(i){sum(
+    (((B.eigs$vectors[i])*weights[i]*s)^2)*B.eigs[i]$values)}
+  Q <- (1/(4*m))*sapply(1:n, Q.node.i)
+ 
+  return(list(Q=Q,max.lam=max.lam,weights=weights,clusters=clusters))
+}