=Paper=
{{Paper
|id=Vol-2362/paper8
|storemode=property
|title=The Web-Application Development for Analysis of Social Graph of the User in Network
|pdfUrl=https://ceur-ws.org/Vol-2362/paper8.pdf
|volume=Vol-2362
|authors=Yuliia Leshchenko,Alina Yelizeva,Yuliia Rudchenko
|dblpUrl=https://dblp.org/rec/conf/colins/LeshchenkoYR19
}}
==The Web-Application Development for Analysis of Social Graph of the User in Network==
https://ceur-ws.org/Vol-2362/paper8.pdf
The Web-Application Development for Analysis of Social
Graph of the User in Network
Yuliia Leshchenko1[0000-0001-9232-697X], Alina Yelizeva2[0000-0002-8228-9383],
Yuliia Rudchenko3[0000-0002-4085-2254]
1-3 National Aerospace University "Kharkiv Aviation Institute",
Chkalov str.,17, Kharkiv, Ukraine
j.leshhenko@khai.edu1,
a.elizeva@ khai.edu2,
rudchenko96@gmail.com3
Abstract. The paper is devoted to the research of the social graph analysis of
the network user. The review of existing methods of analysis and visualization
of the social graph is carried out represented. Comparative analysis of existing
systems of processing social graphs is carried out represented. Tools for devel-
opment a web-based application and interacting with the Facebook API are se-
lected. The facilities of using social networking data are analyzed. Features of
social networking information analysis is discussed in the paper. The basic al-
gorithm of the program's work is developed, which will ensure the receipt of
publications from the social network and their creation in the WEB-application.
A consolidated resource for the analysis of social networking information in the
content of publications is developed, taking into account its algorithm of work.
It will be performed the assignment of a certain interest to the user by analyzing
the text of its publication, and displaying interest statistics in the graph, as well
as mapping the location of interests on the map of Ukraine.
Keywords: web application, social networks, social graph, visualization, analy-
sis, data processing.
1 Introduction
Nowadays Internet takes important part in human life. Internet of "early" period was
represented simply by sites containing some information and links to other resources
or file directories. This is not just a collection of services such as online translators,
electronic dictionaries or WEB interfaces for e-mail accounts. In the modern Internet,
WEB services integrate with desktop applications, taking advantage of their typical
interface, behavior and capabilities [1, 2].
Nowadays social networks become more popular. The main idea of social net-
works is very simple. A social network is a collection of social elements (people,
groups, organizations, communities) that exchange different data among
themselves [3]. The data can be text messages, images, video and audio files. Formal-
ly such networks are convenient to represent in the form of graphs [4, 5] and apply
developed mathematical models for their analysis [6].
Social networks analysis [7] turn into the main research method in contemporary
sociology [8, 9], anthropology [10, 11], geography [12-14], social psychology [15],
computer science [16, 17], business and organizations [18-20], and also it’s a wide-
spread theme for investigation and discussion [21].
Today's social network is the perfect place to get information about people [22]. By
various data and criteria you can get a lot of information that can be useful in different
fields.
The novelty of the research is in the fact that the developed resource analyzes [23]
the user's posts in the social network Facebook that can be useful for law-enforcement
agencies [24, 25], politics [26, 27] and business [28]. The resource makes it possible
to obtain users interest [29] and visualization of research results.
2 Main Material Presenting
Social networks on the Internet continue to be at the height of popularity fame and
have become the object of researcher’s persistent attention. Moreover, there are new
opportunities for their use: they are tightly integrated into many spheres of business
and become its integral part.
All popular social networks provide their Application Programming
Interfaces (API) which is a set of definitions for the interaction of software various
types [30]. The API is usually (but not surely) an abstraction method between low-
level and high-level software.
The main features of the Facebook API [31] for the right information getting are
conceded.
Linux was chosen as the operating system of Facebook, the bulk of the code is
written in PHP (which compiled in C ++ by HipHop). The MySQL database was used
as a repository. Memcached was used for caching.
Graph API is the basis of the Facebook platform, which allows you to read and
write information to the social network and represents objects (users, photos, events,
pages) and links between them (relationships, content and photo tags). Each object in
the graph has a unique identifier, using which you can it access as follows:
https: //graph.facebook.com/v2.5/ .
The answer will be given in the form of json.
All objects in the graph can be linked by a relationship. You can check the pres-
ence of connection with objects in the following way:
https://graph.facebook.com/<ідентифікатор_об’єкта>/<тип_зв’язку>.
It should be noted if the information is not complete, then the json-response will
has the data object with our data and the paging object containing the fields previous
and next, which contain the link for the previous page and the next one, respectively.
This is very convenient if you need to remove all information from a social network
from a particular keyword and its volume is not known in advance.
When review the Graph API [32], you should pay attention to Real-Time updates
as the most interesting feature. The presence of this item shows the seriousness of
Facebook treatment to integration with other applications. Real-time updates allow
outside developers to retrieve information about some of their users' changes. Face-
book itself reports that the user wrote a message in the news feed, added a friend, or
clicked the Like button. This point is required for periodic interrogating by APIs re-
sources.
The review and analysis of existing systems of social networks processing and vis-
ualization is performed. Today, there are several systems for analyzing social data
such as Gephi [32], Igraph [33], NetworkX [34] and others. Their comparative analy-
sis is presented in Table 1.
Gephi is a system intended for visualization and basic analysis of medium size
graphs (up to 1,000,000 nodes). It has a multifunctional user interface and a rich set of
network visualization methods (including real-time visualization of dynamic graphs).
The system architecture and open source code provide the addition of new features
through the development of plug-ins.
Unlike Gephi, Igraph is not a finished system, but a library of computational func-
tions. Igraph [33] is implemented on C, but has software shells for Python, Ruby and
R. The library contains a lot of graph theory methods, both classical and recent, for
example, methods for ranking nodes, community search, etc. The Igraph library is
designed to analyze large networks in research and training projects.
NetworkX [34] is a Python library for studying graphs and networks. NetworkX is
suitable for working on large real-world graphs: for example, for graphs containing
more than 10 million nodes and 100 million lines. Depending on the Python Diction-
aries data structure, NetworkX is a very efficient, highly scalable and easily portable
framework for working with networks and analyzing social networks
(see Table 1).
Table 1. Comparative analysis of existing systems for processing and visualization of social
networks.
Characteristics Gephi Igraph NetworkX
Site gephi.org igraph.sourceforge.net networkx.lanl.gov
Users Scientific, educa- Scientific, educational Scientific, educa-
tional organizations organizations tional organiza-
tions
Data level Up to 1 million Up to several million Up to several mil-
nodes and lines nodes and lines lion nodes and
lines
Data acquisition Missing Missin Missing
Data sources Missing Missing Missing
Methods Visual analysis. Multiple methods Multiple methods
Basic statistical of graph theory of graph
methods. theory
Graph theory basic
methods.
Analysis mode Retrospective Retrospective analysis Retrospective
analysis analysis
Methods Visual analysis. Multiple methods Multiple methods
Basic statistical of graph theory of graph theory
methods.
Graph theory basic
methods
Considered ob- Network structure Network structure Network structure
jects (nodes, directed and (nodes, directed and (nodes, directed
non-directed links) non-directed links) and non-directed
links)
Distribution OpenSource (CDDL OpenSource (GPL OpenSource (BSD
terms 1.0, GPL 3.0) 2.0+) License)
Language support English English English
How you can see, the presented systems are primarily focused on the retrospective
analysis of graphs, which consist of a relatively little number of nodes (up to one
million nodes). Also, such systems work only with one level of representation – the
graph, which requires a lot of preliminary preparation and data structuring. There is
no trouble of the level of texts analysis and, as a rule, a very scantly arsenal of data
mining methods [35].
3 Features of social networking information analysis
Whole various social networks can be described by a tuple of two sets: multiple sets
of users and plurality of content. The social graph of users is a multi-graph, which
means users form a multi-faceted relationship and may contains additional structural
data
G={V,E1 …Ek ,p,d1 …dk }, (1)
where V – actors; E1…Ek – types of relationships in the network; p – profile of the
actor V → p; d1…dk – corresponding relation Ei → di .
We can describe the content graph of the network, similarly
C ={T,R1 …Rm ,O,y1 …ym }, (2)
where T – set of heterogeneous content of the network; R1…Rm – relationship between
the content; O – content parameters T → O; y1…ym – parameters of the corresponding
relation in the middle of the content Rj → yj.
Finally, A ϵ V x T – the ratio of actors and content
A={Li …Lm ,e1 …en }. (3)
Relation between the actors of network and the content can be considered through the
author's message (post) and comments (comments). Messages can be formed like text,
image, audio or video. They notify network participants about some event or occur-
rence. Comments – is a fixed attitude of actors to messages in a certain form. The
simplest analysis of social networks is based on an egocentric graph.
The peaks of this graph correspond to the central actor and his closest neighbors.
This graph does not reflect the characteristics of the network, but it can be applied by
studying the social roles in the group. Some templates can be detected by analyzing
the topology of the network. But in most cases you need to know the characteristics of
the network. Such as centrality, density, average number of passed paths through the
actor and the level of structural equivalence with a certain pattern.
Sometimes statistical characteristics are required: dispersion, centralization, topog-
raphy distribution histogram, and others. Relationship patterns in the egocentric net-
work are reflected in the content of the position, social or professional activity of the
actor.
Each social role forms network structure and hence can be defined from it. Thus,
the nature and structure of the links, actors can be classified. For example, we can
identify potentially useful users and include them to the context-search system, detect
"provocateurs", "spammers", "bots", "flooders" and protect them from other actors.
More complex studies are conducted on a full-scale graph of the network. The
study of popular social networks: Flickr, YouTube, LiveJournal Orkut and Facebook
shows their scalable nature. This means that the network has a power-sharing distribu-
tion between actors. So, most actors have the same number of connections, and the
part of actors with an excessively large number of connections is a rarity. Therefore,
the online network P(k) ~ k-y can be represented by a fairly branched structure with
hierarchically related vertices, such as computer networks.
4 Development of algorithms for the operation of the program
interface
Thus, on the basis of research of existing software tools and existing mechanisms for
obtaining information for a software complex that will implement the process of ob-
taining data from a particular user from Facebook social network and will perform the
assignment of a certain interest to the user by analyzing the text of its publication, and
displaying interest statistics in the graph, as well as mapping the location of interests
on the map of Ukraine. Separately, a development algorithm that will independently
attribute the text of a publication to a certain interest, and also need to develop an
algorithm for processing data and displaying them to the user.
A concept has been developed that reflects user interaction and consolidated re-
source. To do this, we break the process of obtaining and processing data in separate
steps:
1. Authentication of the social network user.
2. Obtaining access to user data, and recording them to a local database.
3. Conducting data analysis.
4. Creation of interest standards with the help of the administrator.
5. Automatic launch of the system of self-determination of interest on the basis of
preserved standards.
6. Output of statistics in the form of a graph, and mapping of interests on the map of
Ukraine.
The general concept of interaction between the user and the consolidated resource to
solve the problem of determining the user's interest in publishing it is possible to de-
pict schematically as shown in Fig. 1 [36]. The features of the analysis of the infor-
mation of the social network Facebook to develop a universal approach for imple-
menting an effective consolidated resource is considered below.
We need to consider the scheme of the algorithm (Fig. 2) of the web-based applica-
tion that will be able to analyze the information of social networks based on the de-
veloped concepts [36].
Fig. 1. Scheme of the interaction interface Fig. 2. Scheme of the web application
algorithm. algorithm.
5 Software Implementation
The web application is designed for users of the Facebook social network. It allows
registered user to provide analysis and visualization of the selected data. The applica-
tion uses integration methods with the Facebook API.
Nowadays there are quite a number of tools permitting to develop applications. It
was decided to use the Tomcat7 [37] server for the application development. The
server is entirely implemented in Java programming language. Tomcat is a very popu-
lar and widespread server for web development. Maven [38] is used to build the web
application.
In order to integrate the web application with Facebook, you must perform "Log on
through Facebook", after which the application receives an access token that provides
temporary secure access to various Facebook APIs [39]. Fig. 3-4 show the integration
of the web application with Facebook.
Fig. 3. Development of Application identifier.
Fig. 4. User Settings.
In the web application development the page access tokens were used. These tokens
are used in API Graph [40-42] and called for managing Facebook pages. To create
such a token, the Page Administrator must provide additional access permission to
manage pages. With this permission, you can get the Page access token using the user
access token with the required permissions.
The D3.js [43, 44] library was selected to visualize the Facebook user's social
graph. D3.js is a JavaScript [45] library for document management based on data. D3
helps you translate data into life using HTML [46], SVG [47] and CSS [48]. D3 al-
lows you to bind arbitrary data to the DOM [49, 50], and then apply the results of
manipulations with them to the document.
The start page opens after the program starting, which is shown in Fig. 5.
Fig. 5. Web-application Start Page.
The user has a choice between two buttons, called "Facebook Connect" and "Signin
As Admin". The "Facebook Connect" button requires the Internet connection, because
processes startes after clicking this button are accessed by the Facebook social net-
work for user authentication. For the "Signin As Admin" button, the Internet connec-
tion is not required.
Clicking the "Facebook Connect" button is considered. When you click the button,
a Facebook authentication page appears (Fig. 6).
Fig. 6. Authentication on Facebook Page.
If you have already passed the authentication, you will immediately find yourself on
the main page (Fig. 7).
Fig. 7. The Home Page.
The web-application home page appears after successful authenticating and agreeing
to personal data access (Fig. 7).
A welcome message with the logged-in username and all his posts from the Face-
book page will appear on the home page. There are buttons with the interest title by
the post. The user can click the same title button that best describes the content of the
text.
The user can resign a post on his Facebook page in a form called "create post on
Facebook". There are two buttons "Send" and "Reset" under the form (Fig. 7). When
you click the "Send" button, the post appears immediately on the Facebook page. If
you press the "Reset" button after entering the text, the text will be deleted. When
user clicks the "Click for display your Statistics" button (Fig. 7), the page with graphs
and the map appears as shown in Fig. 8-9.
Fig. 8. The Statistics Page View.
Fig. 9. The Statistics Page View.
6 Conclusion
The web application for processing and visualization of the Facebook user social
graph is developed. The analysis of existing methods of analysis and visualization of
the social graph is carried out. A comparative analysis of existing systems of pro-
cessing social graphs is carried out. Tools for the web application development and
interacting with the Facebook API are chosen. The web application is implemented
using the Java language; the social graph visualization is implemented using the D3.js
library.
The basic algorithm of the program's work, which ensures the publications receipt
from the social network and their creation in the web-application, is developed.
After testing the program, it can be concluded that the program works correctly.
The algorithm for analyzing information, based on existing interactions, is effective,
which suggests that the system has a learning function.
The recognition percent is 98-99. Since analogues that analyze information from
social networks don’t conduct a publications analysis, it can be concluded that the
developed consolidated resource is an ideal solution for analyzing the information of
social networks in the content of publications, taking into consideration its algorithm
of work.
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