How to find out the neighbour vertices of a particular vertex in graphframe(pyspark)?

Question

I am trying to find out the neighbouring vertices of a particular vertex using the graphframe API available in pyspark. How can I do it? For example consider the following graph edges ( it should be considered as bidirectional although the input is directional).

edges = [[4,3],[4,5],[5,6],[3,6],[1,3],[1,0],[0,3])
vertices = [0,1,3,4,5,6]
g = GraphFrame(vertices,edges) //this makes the graph directional, is there a way to make it bidirectional?

Now I want to do something like-
degree(3) = 5    
neighbour(3) = [4,5,6,1,0]

Here is my code which takes an input file( edge.txt) like

v1 v2
4 3
4 5
5 6
3 6
1 3
1 0
0 3

import sys

from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession

conf = SparkConf().setAppName('myapp')
sc = SparkContext(conf=conf)

sc.setLogLevel("WARN")

spark = SparkSession(sc)

file_name = sys.argv[1]
log_txt = sc.textFile("/user/rikhan/"+str(file_name))
header = log_txt.first()
log_txt = log_txt.filter(lambda line : line!=header)  

temp_var = log_txt.map(lambda k: k.split(" "))

hasattr(temp_var,"toDF")
log_df = temp_var.toDF(header.split(" "))

log_df.dropDuplicates(['v1','v2'])

from functools import reduce
from pyspark.sql.functions import col,lit,when
from graphframes import *
import networkx as nx
import networkx.generators.small as gs
import matplotlib.pyplot as plt
from pyspark.sql import Row
from pyspark.sql import SQLContext
from pyspark.sql import DataFrame
from pyspark.sql import Column
from pyspark.sql import GroupedData
from pyspark.sql import DataFrameNaFunctions
from pyspark.sql import DataFrameStatFunctions
from pyspark.sql import functions
from pyspark.sql import types
from pyspark.sql import Window

edges = log_df.selectExpr("v1 as src","v2 as dst")

vertices = log_df.toPandas()['v1'].unique()
vertices2 = log_df.toPandas()['v2'].unique()
ver = vertices.tolist() + vertices2.tolist()
vertex = []
for x in ver:
    if x not in vertex:
        vertex.append(x)

rdd1 = sc.parallelize(vertex)
row_rdd = rdd1.map(lambda x: Row(x))
ver = spark.createDataFrame(row_rdd,['id'])

g = GraphFrame(ver,edges)