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| import networkx as nx
from elasticsearch import helpers
from elasticsearch.client import Elasticsearch
import sys
import urllib
import json
es = Elasticsearch(
[
'http://192.168.0.101:9200/'
]
)
alertsIndexName = "alerts"
def createAlertsIndex():
alertsIndexSettings = {
"settings": {
"number_of_replicas": "0",
"number_of_shards": "1"
},
"mappings": {
"task": {
"properties": {
"riskType": {
"type": "keyword"
},
"url": {
"type": "text",
"index": "false"
}
}
}
}
}
if not es.indices.exists(index=alertsIndexName):
es.indices.create(index=alertsIndexName, body=alertsIndexSettings)
def getSellersList():
sellers=[]
sellersQuery = {
"size": 0,
"aggs": {
"topTerms": {
"terms": {
"field": "seller",
"size": 50000
}
}
}
}
results = es.search(index="reviews", body=sellersQuery)["aggregations"]["topTerms"]["buckets"]
for bucket in results:
sellers.append({
"seller" : bucket["key"],
"numReviews" : bucket["doc_count"]
})
return sellers
def nodeId(node):
return node["field"] + ":" + node["term"]
def erasePrint(msg):
sys.stdout.write('\r')
sys.stdout.write(msg)
sys.stdout.flush()
createAlertsIndex()
sellers=getSellersList()
rowNum = 0;
totalNumReviews=0
for sellerDetails in sellers:
rowNum +=1
totalNumReviews+=sellerDetails["numReviews"]
sellerId=sellerDetails["seller"]
q = {
"query": {
"bool": {
"must": [
{
"term": {"seller": sellerId}
},
{
"match": {"rating": 5}
}
]
}
},
"controls": {
"sample_size": 2000,
"use_significance": True
},
"vertices": [
{
# Find most loyal reviewers - significantly connected to the current seller
"field": "reviewer",
"size": 50,
"min_doc_count": 1
}
],
"connections": {
# Find date/times of loyal reviewers' activity - guide the exploration so only current-seller-related reviews
"query": {
"term": {
"seller": sellerId
}
},
# This will naturally favour date/times that are common to multiple reviewers
"vertices": [
{
"field": "hour",
"size": 500,
"min_doc_count": 1
}
]
}
}
erasePrint("Examining seller " + str(rowNum) + " of " + str(len(sellers)))
results = es.transport.perform_request('POST', "/reviews/_xpack/graph/_explore", body=q)
# Use NetworkX to create a client-side graph of reviewers and date/times we can analyze
G = nx.Graph()
for node in results["vertices"]:
G.add_node(nodeId(node), type=node["field"])
for edge in results["connections"]:
n1 = results["vertices"][int(edge["source"])]
n2 = results["vertices"][int(edge["target"])]
G.add_edge(nodeId(n1), nodeId(n2))
# Examine all "islands" of reviewers connected by same date/time
subgraphs = nx.connected_component_subgraphs(G)
numCoincidences = 0
for subgraph in subgraphs:
numHours = 0
reviewers = []
for n, d in subgraph.nodes(data=True):
if d["type"] == "hour":
numHours += 1
if d["type"] == "reviewer":
reviewers.append(n)
if numHours > 1:
if len(reviewers) > 1:
for srcI in range(0, len(reviewers)):
reviewer1 = reviewers[srcI]
for targetI in range(srcI + 1, len(reviewers)):
reviewer2 = reviewers[targetI]
paths = nx.all_simple_paths(subgraph, source=reviewer1, target=reviewer2, cutoff=2)
# Each path is a reviewer <-> date/time <-> reviewer triple
sameTimeReviews = 0
for path in paths:
sameTimeReviews += 1
# Two reviewers reviewing at same date/time is not a coincidence - however
# repeated synchronized reviews *are* a coincidence
if sameTimeReviews > 1:
numCoincidences += sameTimeReviews - 1
if numCoincidences > 0:
erasePrint("")
print "seller:" + str(sellerId) + " has ", numCoincidences, " reviewer coincidences in", sellerDetails["numReviews"], "reviews"
gq = json.dumps(q)
workspaceUrl = "graph#/workspace/32935810-5e4d-11e8-811e-7b68199c5a31?query=" + urllib.quote_plus(gq)
doc = {
"url": workspaceUrl,
"seller": sellerId,
"riskType": "SockPuppetry",
"riskRating": numCoincidences,
"numDocs": sellerDetails["numReviews"]
}
res = es.index(index=alertsIndexName, doc_type='task', id=sellerId, body=doc)
erasePrint("")
print "Completed analysis of", len(sellers), "sellers and",totalNumReviews,"reviews"
|