Characteristics of social network
The main application tasks developed for the analysis of social networks. The analysis of approaches to software development. Selection of project management tools. Especially the use of the programming language and integrated extension environment.
| Рубрика | Иностранные языки и языкознание |
| Вид | дипломная работа |
| Язык | английский |
| Дата добавления | 22.09.2016 |
| Размер файла | 1,1 M |
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6. PRACTICAL IMPLEMENTATION DESCRIPTION
6.1 Requirements formulation
Functional requirements
The following functional requirements have been formulated:
1. System must give a user and ability to input target's (a Vkontakte page of a person, in which social surrounding user is interested in) id for data retrieval.
2. System must be able to retrieve list of target's friends and common friends for each target's friend from Vkontakte Social Network
3. System must be able to build a social graph of target user.
4. System must count basic social graph characteristics.
5. System should count ranking for each friend of target and provide top 10 most ranked users.
Picture 8. UI Mockup
Thus, a web form should contain the following control elements (Table 3):
Table 3.
|
Element name |
Type |
Input/output? |
|
|
Target User UI |
Text |
Input |
|
|
Retrieve Information |
Button |
Input |
|
|
PageRank |
Button |
Input |
|
|
SimRank |
Button |
Input |
|
|
Interest Similarity Rank |
Button |
Input |
|
|
Local clustering Coefficient |
Grid |
Output |
|
|
Centrality |
Grid |
Output |
|
|
Social Graph |
Image |
Output (generating) |
|
|
PageRank Results |
Grid |
Output |
|
|
SimRank Results |
Grid |
Output |
|
|
Interest Similarity Results |
Grid |
Output |
6.2 UML
State diagram
Diagram 1 represents state diagram, which may be considered as the major algorithm
Diagram 1. State diagram
6.3 Programming an application
Since application implementation was conducted in Scrum methodology, the functional requirements were divided into several sprints.
Sprint 1 was a preparation included theoretical and methodological investigation. It included the next user stories and tasks:
1. Research graphs and networks
a. Social graphs
b. Interest graphs
c. Network measures
d. Applicability of achieved knowledge in the project
2. Investigate python libraries
a. igraph
b. networkx
c. flask
d. vk
3. Create a standalone application
Results of stories 1 and 2 are outlined in the previous chapters of thesis.
User story “Create a standalone application”
Current User Story is rather simple and required creation of Standalone application at https://vk.com/dev. (Picture 9 a-b)
Picture 9a. The first page
Sprint 2 included basic algorithms implementation. As algorithms were planned to be used in the web application, they were implemented separately as a scripts.
Stories:
1. Data retrieval and graph formation algorithm
a. Implement connection to Vkontakte
b. Implement friends.get request
c. Implement friends.getMutual request
d. Graph formation
2. Network measures calculations
a. Eigenvector centrality
b. Local clustering coefficient
3. SimRank calculation
4. PageRank calculation
5. Interest Similarity Rank calculation
a. Users' interests mining
b. Jaccard coefficient calculation
User story “Data retrieval”
As a result of this story, a retrieve.py script was developed.
|
Element |
retrieve.py |
|
|
Libraries |
import vk import time from vk.exceptions import VkAPIError import re |
|
|
def getFriends(uid): |
ac=ACCESS_TOKEN global session session = vk.Session(access_token=ac) global api api = vk.API(session) friends=api.friends.get(user_id=uid) return friends |
|
|
def getMutual(uid,friends): |
mutual={} c=1 for i in friends: try: mutual[i]=api.friends.getMutual(target_uid=i, source_uid=uid) except: mutual[i]=[] c=c+1 if (c%9==0): time.sleep(5) return mutual |
|
|
def getInterests(uid,friends): |
c=0 sorted_jacc=[] uinterest={} try: uinterest=api.users.get(user_ids=uid, fields='interests, music, movies, tv, books, games') except VkAPIError: uinterest={} print((uinterest[0].values())) if len(uinterest[0].keys())>0: uinterest[0].pop('first_name') uinterest[0].pop('last_name') uinterest[0].pop('uid') s=[] for ukey in uinterest[0].keys(): a=re.findall(r"[\w']+", uinterest[0].get(ukey).lower()) for h in a: if h!='' and h!='и' and h!='с' and h!='у'and h!='в'and h!='от'and h!='над'and h!='под'and h!='по' and h!='все' and not bool(re.search("^[0-9]$", h)): s.append(h) uinter=s if len(uinter)>0: interests={} time.sleep(5) for i in friends: try: interests[i]=api.users.get(user_ids=i, fields='interests, music, movies, tv, books, games') except VkAPIError: interests[i]=[] if len(interests[i])==0 or interests[i][0].get('deactivated'): interests.pop(i) else: interests[i][0].pop('first_name') interests[i][0].pop('last_name') interests[i][0].pop('uid') c=c+1 if (c%9==0): time.sleep(5) inter={} jacc={} for i in interests: s=[] for ukey in interests[i][0].keys(): a=re.findall(r"[\w']+", interests[i][0].get(ukey).lower()) for h in a: if h!='' and h!='и' and h!='с' and h!='у'and h!='в'and h!='от'and h!='над'and h!='под'and h!='по' and h!='все' and not bool(re.search("^[0-9]$", h)): s.append(h) inter[i]=s print(str(i)+" Interests: "+str(inter[i])) jacc[i]=Jacc(uinter,inter[i]) print(jacc) sorted_jacc=sorted(jacc.items(), key=operator.itemgetter(1),reverse=True)[0:10] print (sorted_jacc) else: sorted_jacc=[("error","No interests pointed")] else: sorted_jacc=[("error","No interests pointed")] return sorted_jacc |
|
|
def Jacc(ui,ui2): |
set1=ui set2=ui2 intersection=len(set(set1).intersection(set2)) jacc=intersection/(len(set1)+len(set2)-intersection) return jacc |
User Story `Graph and measures'
The output of this story was graph.py script
|
Element |
graph.py |
|
|
Libraries |
import networkx as nx import operator import networkx_addon import json from networkx.readwrite import json_graph |
|
|
def SocialGraph(uid,friends,mutual): |
G=nx.Graph() G.add_node(uid) G.add_nodes_from(friends) for k in friends: G.add_edge(uid,k) for i in mutual[k]: G.add_edge(k,i) |
|
|
def EigenCentr(G): |
ec=nx.eigenvector_centrality(G) sorted_EC = sorted(ec.items(), key=operator.itemgetter(1),reverse=True)[1:11] return sorted_EC |
|
|
def PageRank(G): |
pr=nx.pagerank(G) sorted_PR = sorted(pr.items(), key=operator.itemgetter(1),reverse=True)[1:11] return sorted_PR |
|
|
def SimRank(uid,G): |
sr = networkx_addon.similarity.simrank(G) suid=(sr[uid]) sorted_suid=sorted(suid.items(), key=operator.itemgetter(1),reverse=True)[1:11] return sorted_suid |
|
|
def LocalCluster(G): |
lc=nx.clustering(G) sorted_LC = sorted(lc.items(), key=operator.itemgetter(1),reverse=True)[1:11] return sorted_LC |
|
|
def D3Data(uid,G): |
Graphdata = json_graph.node_link_data(G) JGraphData = json.dumps(Graphdata) Return JGraphData |
User Story `SimRank calculation'
Result - SimRankMeasure.py
|
Step |
SimRankMeasure.py |
|
|
SimRank calculation |
import networkx_addon SRank = networkx_addon.similarity.simrank(G) |
User Story `PageRank calculation'
Result - PageRankMeasure.py
|
Step |
PageRankMeasure.py |
|
|
PageRank calculation |
PRank=pagerank(G, alpha=0.85, personalization=None, max_iter=100, tol=1e-06, nstart=None, weight='weight', dangling=None) |
Sprint 3 is mostly devoted to UI development. It has one labor-intensive story:
• UI design and development
The design and development of UI was held in flask microframework, which allows to build web-applications in python language.
For Web application operation script server.py and template index.html were implemented.
The extra stories in Backlog are the stories which may be taken in Sprint if UI design and development will be done before sprint end.
They include adding different parameters, which may also influence potential customers preferences.
1. Adding a parameter personal.religion;
2. Adding a parameter personal.smoking;
3. Adding a parameter personal.alcohol.
7. PRACTICAL RESULTS DESCRIPTION
7.1 Application demonstration
Application interface is presented on Picture 10. As the flask framework has some issues with placing several buttons on the form, connected with submit form type, it was decided to implement user interface with one button “Retrieve information”, which makes a POST request to the server, passing target user ID.
Picture 10. VkApp interface
After server gets ID, it sequentially executes methods stated in 7.3 Programming an application. When all methods are executed, server re-renders template index.html
As a result, all measures are stated in tabular view and graph is built in div container.
7.2 Restrictions
Vkontakte API allows external applications a limited number of requests per second, which greatly increases a calculation time because of required timeouts. After conducting a number of tests, it was found, that the optimal number of requests for iteration r=9. The minimum timeout, which allows stable work of application is t=5 sec. Thereby, the total calculation time increasing, connected with timeouts, may be calculated with the next formula:
, (11)
Where k is a number of needed cycles of API requests and N is a number of friends+1 (including target used). Taking into consideration experimentally found r and t and exploring code allows converting formula (11) in the next form:
(12)
For instance, for user with 20 friends
=3*3*5=45 seconds.
Interest Rating
Current application proposes a new similarity measure approach. This approach is similar with interest graph theory. The idea is that the more people interest have in common, the more alike they are.
Jaccard index was used to implement interest rating. First, all available interests are retrieved from target user page and his friends' pages. After that all the patterns like one-digit numbers and prepositions were removed and text was transformed to lowercase. Interests, which are usually stated through commas, were divided into separate words - elements of a list, which presented the key words of each user. After that a list of interest of target user was compared to the lists of interests of his friends:
· The intersections were counted
· The Jaccard coefficient was calculated using formula (8).
· Jaccard coefficients were ordered by descending with the users
7.3 Measures comparison
A user with ID 44687877 was taken as experimental user. After analyzing this user author got the next conclusions:
1. It can be stated, that eigenvector centrality, pageRank and SimRank give similar ratings for target user friends. There were usually fluctuations of ids on 1-2 and 3-4 places.
2. Unlike the measures, stated in point 1, Interest Rank provides different rating of similar users
Such results may be explained by the difference in the nature of calculations: while first coefficients take into account only links between users, the Interest Rank uses user's interest.
8. TASK COMPLETION PROOF
During the completion of the present thesis the next goals were reached
1. The existing methods of problem solution are outlined in Theoretical background and Methodological variety
2. Work's Scientific Value tries to prove scientific value of work based on current state of business in this area.
3. Software tools selection resulted in a set of tools for development and development support.
4. The first part of Practical Implementation Description contains the development of general algorithms, prototyping etc.
5. The second part of Practical Implementation Description reveals algorithm and UI codes.
6. Solution testing and practical applicability demonstration in Practical Results Description proves the operability of application.
As a result, it can be concluded that the major task defined in Task Definition section was completed.
CONCLUSION
Inspired by researches involving Facebook Social Graph analysis, recommendation systems and objects similarity, the author has created a web application, which provides user with basic and visual, but transparent information. This system presents a recommendation system based on links analysis and interest analysis. The idea was to create a system, which would take a user profile as an input parameter and provide links analysis and interest analysis of the input user and his/her friends, presenting a rank of interest similarities among user's friends and other useful information about social graph.
During this research a number of connected scientific works were overviewed and some operating applications were stated. Using Python and Flask proved to be a good experience, as at the beginning of the research author had no skills in these programming tools. A novel proposed approach of using interests' similarity may be successful in targeted advertising and may be further developed for more complex interest analysis.
Among further development gender, belief, attitude to politics, alcohol, cigarettes and many other parameters may be added to increase the productivity of analysis. Moreover, analysis time may be decreased with involvement of more complex algorithms for searching and ranking as well as parallel computing involving message queues.
BIBLIOGRAPHY
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2 My T. Thai, Panos M. Pardalos. Handbook of Optimization in Complex Networks: Communication and Social Networks. -- Springer, 2011. -- С. 541. -- ISBN 978-1-4614-0856-7
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5 Hossain N. Why the Interest Graph Is a Marketer's Best Friend. Mashable (19.06.2012)
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ABSTRACT
Computer social networks gave businesses an opportunity to conduct marketing research effectively, as social networks contain various important information like users' age, gender, interests etc. Availability of this data allows avoiding different questionings and other labor-intensive marketing researches because business now is able to promote products, based on the interests pointed in user profile. Moreover, social and interest graphs help to find potential customers among friends and users over the social network. This master's thesis is devoted to the development of marketing analysis solution based on social and interest graphs. Author has set the goal to create a web-application, which will be able to find and rank potential customers by using the information of existing one in Russian social network Vkontakte. Some steps in this work correlate with Systems development lifecycle. With the help of UML software modelling language conceptual diagrams were built. Using Python 3.x, algorithms were developed and user interface was rendered. Application development was carried under Scrum framework. An output of research is an application similar to recommendation system, which suggests potential customers most likely to make a purchase. The output application could be found useful by marketing agencies, which are interested in social graph analysis of Vkontakte. The future development of this research may include involvement of more complex algorithms for searching and ranking as well as parallel computing involving message queues.
APPENDIX
index.html
<!DOCTYPE html>
<html>
<head>
<title>VK Analysis</title>
<meta name="viewport" content="width=device-width, initial-scale=1.0" charset="utf-8">
<link href="//netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet" media="screen">
<script src="https://d3js.org/d3.v3.min.js" charset="utf-8"></script>
<script src="//code.jquery.com/jquery-1.11.0.min.js"></script>
<script src="//netdna.bootstrapcdn.com/bootstrap/3.1.1/js/bootstrap.min.js"></script>
<style>
.node {
stroke: #fff;
stroke-width: 1.5px;
}
.link {
fill: none;
stroke: #bbb;
}
</style>
</head>
<body>
<h1>Welcome to VK Analysis</h1>
<div class="container_fluid">
<div class="row">
<div class="col-lg-4">
<br>
<form role="form" method="POST" action="/">
<tr>
<td>Tagret user ID</td>
<td><input type="text" name="uid" class="form-control" id="url-box" placeholder="Enter user ID..." style="max-width: 300px;"></td>
<td><button type="submit" class="btn btn-default" name="Requ" value="Info"><img src="Static/images/vklogo.png" alt="Vk" style="vertical-align:middle">Retrieve information</button></td>
</form>
<br>
{% for error in errors %}
<h4>{{ error }}</h4>
{% endfor %}
<br>
{% if status=="Data is loaded" %}
<h2><img src="Static/images/ok.png" alt="Ok" style="vertical-align:middle">{{ status }}</h2>
<progress id="progressbar" value="100" max="100"></progress>
<h4>Processing time = {{ timepassed }}</h4>
<h4>Number of friends = {{ nfriends }}</h4>
<h4>Number of people = {{ tpeople }}</h4>
<h4>Number of edges = {{ nlinks }}</h4>
<br>
</div>
<div class="col-lg-4">
<h2>Local Clustering Coefficient</h2>
<table border="1" cellspacing="5">
<tr>
<th><b>User ID</b></th>
<th><b>LCC</b></th>
</tr>
{% for ulc in lc %}
<tr>
<td>{{ ulc[0] }}</td>
<td width="30">{{ ulc[1] }}</td>
</tr>
{% endfor %}
</table>
</div>
<div class="col-lg-4">
<h2>Eigenvector Centrality</h2>
<table border="1" cellspacing="5">
<tr>
<th><b>User ID</b></th>
<th><b>E-C</b></th>
</tr>
{% for uec in eic %}
<tr>
<td>{{ uec[0] }}</td>
<td>{{ uec[1] }}</td>
</tr>
{% endfor %}
</table>
</div>
</div>
<div class="row">
<div class="col-lg-6" id="graph" style="border: solid 1px black;height: 1000px;">
<h2>Graph</h2>
</div>
<div class="col-lg-3">
<h2>PageRank</h2>
{% if pr %}
<table border="1" cellspacing="5">
<tr>
<th><b>User ID</b></th>
<th><b>PageRank</b></th>
</tr>
{% for upr in pr %}
<tr>
<td>{{ upr[0] }}</td>
<td>{{ upr[1] }}</td>
</tr>
{% endfor %}
</table>
{% endif %}
<br>
<h2>SimRank</h2>
{% if pr %}
<table border="1" cellspacing="5">
<tr>
<th><b>User ID</b></th>
<th><b>SimRank</b></th>
</tr>
{% for usr in sr %}
<tr>
<td>{{ usr[0] }}</td>
<td>{{ usr[1] }}</td>
</tr>
{% endfor %}
</table>
{% endif %}
</div>
<div class="col-lg-3">
<h2>InterestRank</h2>
{% if ijac %}
<table border="1" cellspacing="5">
<tr>
<th><b>User ID</b></th>
<th><b>InterestRank</b></th>
</tr>
{% for uj in ijac %}
<tr>
<td>{{ uj[0] }}</td>
<td>{{ uj[1] }}</td>
</tr>
{% endfor %}
</table>
{% endif %}
</div>
{% endif %}
</div>
{% if status=="Data is loaded" %}
<script>
var width = 960,
height = 500;
var color = d3.scale.category20();
var force = d3.layout.force()
.linkDistance(10)
.linkStrength(2)
.size([width, height]);
var svg = d3.select("#graph").append("svg")
.attr("width", width)
.attr("height", height);
//d3.json("Static/44687877.json", function(error, graph) {
var d3Data1 = {{ d3Data }}.toString().replace(/'/g,"'");
root = JSON.parse(d3Data1);
if (error) throw error;
force
.nodes(graph.nodes)
.links(graph.links)
.start();
var link = svg.selectAll(".link")
.data(bilinks)
.enter().append("path")
.attr("class", "link");
var node = svg.selectAll(".node")
.data(graph.nodes)
.enter().append("circle")
.attr("class", "node")
.attr("r", 5)
.style("fill", function(d) { return color(d.group); })
.call(force.drag);
node.append("title")
.text(function(d) { return d.name; });
force.on("tick", function() {
link.attr("d", function(d) {
return "M" + d[0].x + "," + d[0].y
+ "S" + d[1].x + "," + d[1].y
+ " " + d[2].x + "," + d[2].y;
});
node.attr("transform", function(d) {
return "translate(" + d.x + "," + d.y + ")";
});
});
//});
</script>
{% endif %}
</body>
</html>
server.py
# -*- coding: utf-8 -*-
import sys, traceback
import re
import os
from flask import Flask, render_template, request, redirect
import networkx as nx
import time
import retrieve
import graph
app = Flask(__name__, static_url_path = "/Static", static_folder = "Static")
@app.route('/', methods=['GET', 'POST'])
@app.route('/index', methods=['GET', 'POST'])
def index():
errors = []
results = []
mutual={}
nfriends=0
tpeople=0
nlinks=0
VkG=nx.Graph()
status=""
pr=[]
lc=[]
eic=[]
sr=[]
ijac=[]
d3Data={}
timepassed=0
if request.method == "POST":
errors=[]
if request.form['Requ']=="Info":
status="Data is loading, please wait"
time1=time.time()
try:
uid = int(request.form['uid'])
if uid < 1 or uid > 1000000000:
raise Exception()
friends=retrieve.getFriends(uid)
nfriends=len(friends)
time.sleep(5)
mutual=retrieve.getMutual(uid,friends)
VkG=graph.SocialGraph(uid,friends,mutual)
eic=graph.EigenCentr(VkG)
lc=graph.LocalCluster(VkG)
pr=graph.PageRank(VkG)
sr=graph.SimRank(uid,VkG)
tpeople=VkG.number_of_nodes()
nlinks=VkG.number_of_edges()
timepassed=time.time()-time1
d3Data=str(graph.D3Data(uid,VkG))
ijac=retrieve.getInterests(uid,friends)
status="Data is loaded"
except:
errors.append(sys.exc_info()[0])
return render_template('index.html', errors=errors)
if request.form['Requ']=="PR":
pr=graph.PageRank(VkG)
return render_template('index.html', errors=errors, results=results, nfriends=nfriends, lc=lc, pr=pr, status=status, timepassed=timepassed, tpeople=tpeople, nlinks=nlinks, mutual=mutual, eic=eic, sr=sr, d3Data=d3Data, ijac=ijac)
if __name__ == "__main__":
app.run(debug=False)
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